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A DNA Data Structure
Ben Shirt-Ediss & Natalio Krasnogor
The Dr. Jekyll Side of DNA
Source:
http://compbio.pbworks.com
Mr. Hide: DNA as a Dynamic Polymerdization Reactions
e of DNA hybridization reactions is the Watson-Crick DNA hybridization between two
A strands as discussed in Section 1.2.2. Two ssDNA strands can attach to each other.
o detach from one another, if the temperature is greater than the melting temperature
6). The melting temperature of a dsDNA is defined as the temperature at which 50%
nverted to single stranded form.
Figure 6: DNA Denaturation Renaturation [60]
Hybridisation Toehold-Mediated
Strand Displacement
Toehold Exchange
trol of DNA Strand Displacement Kinetics A R T I C L E
Several subsequent works used Yurke’s basic reaction sequence
(a hybridization step followed by strand displacement to reverse the
effect of the initial hybridization) for controlling complex nanos-
cale structures. Simmel and Yurke34
demonstrated a nanoactuator
related to Yurke’s original tweezer design. Addition of a first input
strand pushed the two arms of the nanoactuator apart; addition of
a second input strand set them free. In further work they built a
device that could be switched between three distinct states using
two pairs of fuel strands35
. Tian and Mao36
built a device consisting
of two DNA complexes reminiscent of interlocking gears that could
be repeatedly cycled through three different states.
Reconfiguring self-assembled structures. Strand displacement
can be combined with structural self-assembly to enable dynamic
reconfiguration of larger DNA nanostructures post-assembly, and
can be used to induce ch
ple of this was describe
toehold-mediated cyclin
rotary DNA device. Th
states corresponding to
JX2 (Fig. 1b). They also
structure large enough t
scope and demonstrated
tural motif relative to th
Their device was based
nanomachine30
that resp
than DNA inputs.
Chakraborty et al.38
l
that could be switched be
Seeman39
demonstrated
2* 3*
3
1 2
22
2
3
1
5'
3'
3'
CCCTCATTCAATACCCTACG
AGAGGTA
c
a
b
32
Input A
Complex X
2. Domain 2 undergoes
branch migration
3. Strand displacement
completes
1. Toehold domains
initiate binding
Complex Y
Output B
(toehold)
(toehold)
GGGAGTAAGTTATGGGATGC
3*2*
2
1 2* 3*
3
1
2
2
3*2*
2* 3*
2 3
21
2* 3*
2
CCACATACATCATATT
1
–––
Source:
Zhang & Winfree. 2011. Nat Chem.
Source:
Zhang & Winfree. 2009
DNA: Dynamic and also Well Understood
Predictable tertiary structure
Can use methods in Mol. Bio
to quantify experimental success
Enzymatic reactions cutting/joining
DNA well characterised
Hybridisation, branch migration
and strand displacement reactions
well understood
PAGE, Fret, PCR, AFM…
xDNA model (left), with equal
has di↵erentiated major and
x with one base pair displayed.
III. INTRODUCING DIFFERENT WID
AND MINOR DNA GROOVES
B-DNA in the original oxDNA m
widths, while in reality DNA has a lar
a smaller minor groove. Having realisti
and minor grooves is equivalent to havi
tioned backbone sites in the model, an
the physical properties of many DNA
in DNA origami, antiparallel double
crossovers, for which the position of t
shown to be crucial for origami structur
is anisotropic duplex bending: the dup
bend more easily into the major groove
the groove widths are unequal.
The oxDNA nucleotide is compos
sites: the hydrogen-bonding, stacking, a
introduce di↵erent groove widths by ch
oxDNA
DNA Computing
ime study of the dynamics of a single mol-
ule and the chemical and biochemical re-
tions that such a molecule may undergo in
lution.
REFERENCES AND NOTES
. W. E. Moerner, Science 265, 46 (1994), and refer-
ences therein.
. M. Orrit, J. Bernard, R. I. Personov, J. Phys. Chem.
97,10256(1993).
. F. GOttler, T. Irngartinger, T. Plakhotnik, A. Renn, U.
P. Wild, Chem. Phys. Lett. 217, 393 (1994).
. M. Ishikawa, K. Hirano, T. Hayakawa, S. Hosoi, S.
Brenner, Jpn. J. Appl. Phys. 33,1571 (1994).
. E. Betzig and R. J. Chichester, Science 262, 1422
(1993).
. J. K. Trautman, J. J. Macklin, L. E. Brus, E. Betzig,
Nature 369, 40 (1994).
. D. C. Nguyen, R. A. Keller, J. H. Jett, J. C. Martin,
Anal. Chem. 59, 2158 (1987).
. K. Peck, L. Stryer, A. N. Glazer, R. A. Mathies, Proc.
Natl. Acad. Sci. U.S.A. 86, 4087 (1989).
. W. B. Whitten, L. M. Ramsey, S. A. Arnold, B. V.
Bronk, Anal. Chem. 63,1027 (1991); K. C. Ng, W. B.
Whitten, S. A. Arnold, L. M. Ramsey, ibid. 64, 2914
(1992).
. M. Eigen and R. Rigler, Proc. Natl. Acad. Sci. U.S.A.
91, 5740 (1994).
. In fluorescence correlation spectroscopy, the inten-
sity recorded at time t is multiplied by that recorded
at t + At and the product is integrated over a finite
period of time; see D. E. Koppel, Phys. Rev. A 10,
1938 (1974).
. T. T. Perkins, D. E. Smith, S. Chu, Science 264, 819
(1994); T. T. Perkins, S. R. Quake, D. E. Smith, S.
Chu, ibid., p. 822.
. S. B. Smith, L. Finzi, C. Bustamante, ibid. 258,1122
(1992); C. Bustamante, Annu. Rev. Biophys. Bio-
phys. Chem. 20, 415 (1991).
. M. Washizu and 0. Kurosawa, IEEE Trans. Ind. Appl.
26,1165 (1990).
. S. B. Smith, P. K. Aldridge, J. B. Callis, Science 243,
203 (1989).
. N. J. Rampino and A. Chrambach, Anal. Biochem.
194, 278 (1991).
. K. Morikawa and M. Yanagida, J. Biochem. 89, 693
(1981).
. I. Auzanneau, C. Barreau, L. Salome, C. R. Acad.
Sci. Paris 316, 459 (1993).
. H. Kabata et al., Science 262, 1561 (1993).
. D. A. Schafer, J. Gelles, M. P. Sheetz, R. Landick,
Nature 352, 444 (1991).
. Laser excitation at 488.0 and 514.5 nm was provided
by an argon ion laser (Lexel Lasers, Fremont, CA). The
laser beam entered the microscope through a back
port and was directed to an oil-immersion objective
(x100, NA = 1.3, Nikon Instrument Group, Melville,
NY) by a dichroic beamsplitter (505DRLP02 or
22. M. B. Schneider and W. W. Webb, Appl. Opt. 20,
1382 (1981).
23. R. Rigler, U. Mets, J. Widengren, P. Kask, Eur. Bio-
phys. J. 22,169 (1993).
24. W. Feller, An Introduction to Probability Theory and
its Applications (Wiley, New York, ed. 3, 1968).
25. S. A. Soper, H. L. Nutter, R. A. Keller, L. M. Davis, E.
B. Shera, Photochem. Photobiol. 57, 972 (1993).
34. W. P. Ambrose, P. M. Goodwin, J. C. Martin, R. A.
Keller, ibid., p. 364.
35. S.N. acknowledges the Whitaker Foundation for a
young investigator award. D.T.C. is a Beckman Cell
Science Scholar of Stanford University. This work
was supported by Beckman Instruments, Inc.
18 July 1994; accepted 19 September 1994
Molecular Computation of Solutions to
Combinatorial Problems
Leonard M. Adleman
The tools of molecular biology were used to solve an instance of the directed Hamiltonian
path problem. A small graph was encoded in molecules of DNA, and the "operations" of
the computation were performed with standard protocols and enzymes. This experiment
demonstrates the feasibility of carrying out computations at the molecular level.
In 1959, Richard Feynman gave a visionary
talk describing the possibility of building
computers that were "sub-microscopic" (1).
Despite remarkable progress in computer
miniaturization, this goal has yet to be
achieved. Here, the possibility of comput-
ing directly with molecules is explored.
A directed graph G with designated ver-
tices vn and vout is said to have a Hamilto-
nian path (2) if and only if there exists a
sequence of compatible "one-way" edges el,
e2, ... ., e, (that is, a path) that begins at in,
ends at v., and enters every other vertex
exactly once. Figure 1 shows a graph that
for vn = 0 and v01u = 6 has a Hamiltonian
path, given by the edges 0-*1, 1->2, 2->3,
3---4, 4->5, 5->6. If the edge 2->3 were
removed from the graph, then the result-
ing graph with the same designated verti-
ces would not have a Hamiltonian path.
Similarly, if the designated vertices were
changed to vin = 3 and vout = 5 there
Department of Computer Science and Institute for Molec-
ular Medicine and Technology, University of Southern Cal-
ifornia, 941 West 37th Place, Los Angeles, CA 90089,
USA.
SCIENCE * VOL. 266 * 11 NOVEMBER 1994
would be no Hamiltonian path (because,
for example, there are no edges entering
vertex 0).
There are well-known algorithms for de-
ciding whether an arbitrary directed graph
with designated vertices has a Hamiltonian
path or not. However, all known algorithms
for this problem have exponential worst-case
complexity, and hence there are instances of
modest size for which these algorithms re-
quire an impractical amount of computer
time to render a decision. Because the direct-
ed Hamiltonian path problem has been
proven to be NP-complete, it seems likely
that no efficient (that is, polynomial time)
algorithm exists for solving it (2, 3).
The following (nondeterministic) algo-
rithm solves the directed Hamiltonian path
problem:
Step 1: Generate random paths through the
graph.
Step 2: Keep only those paths that begin with vin
and end with v,,f.
Step 3: If the graph has n vertices, then keep
only those paths that enter exactly n vertices.
Step 4: Keep only those paths that enter all of
1021
onFebruary25,2016DownloadedfromonFebruary25,2016DownloadedfromonFebruary25,2016DownloadedfromonFebruary25,2016Downloadedfrom
Science
1994
02 TATCGG&TCGGTATATCCGA
0 GCTATTCGAQCTTAAAGCTA
04 GGCTAGGTACCAGCATGT
02-3 GTATATCCQGACTATTCQG
03-4 CTTA&AGCTAQGCTAQGTAC
03 CG&TA&GCTCGA.&TTTCG&T
0,),
Fig. 1. Directed graph. When vi, = 0 and v,,t = 6,
a unique Hamiltonian path exists: 0-s1, 1 -s2,
2-s3, 3->4, 4-s5, 5-s6.
03,4
start stop
A unique hamiltonian path exists:
0>1,1>2,2>3,3>4,4>5,5>6
C
-150
-100
-50
1 2 3 4 5 6
-150
-100
-50
1 2 3 4 5 6 7
-150
-100
-50
1 2 3 4 5 6 7
illonis ----
Formation of DNA molecules encode possible paths
PCR & cut gel to select paths starting at 0, ending at 6
DNA paths entering all vertices found by
6 washings with magnetic beads
7 days of lab work
DNA Computing
ime study of the dynamics of a single mol-
ule and the chemical and biochemical re-
tions that such a molecule may undergo in
lution.
REFERENCES AND NOTES
. W. E. Moerner, Science 265, 46 (1994), and refer-
ences therein.
. M. Orrit, J. Bernard, R. I. Personov, J. Phys. Chem.
97,10256(1993).
. F. GOttler, T. Irngartinger, T. Plakhotnik, A. Renn, U.
P. Wild, Chem. Phys. Lett. 217, 393 (1994).
. M. Ishikawa, K. Hirano, T. Hayakawa, S. Hosoi, S.
Brenner, Jpn. J. Appl. Phys. 33,1571 (1994).
. E. Betzig and R. J. Chichester, Science 262, 1422
(1993).
. J. K. Trautman, J. J. Macklin, L. E. Brus, E. Betzig,
Nature 369, 40 (1994).
. D. C. Nguyen, R. A. Keller, J. H. Jett, J. C. Martin,
Anal. Chem. 59, 2158 (1987).
. K. Peck, L. Stryer, A. N. Glazer, R. A. Mathies, Proc.
Natl. Acad. Sci. U.S.A. 86, 4087 (1989).
. W. B. Whitten, L. M. Ramsey, S. A. Arnold, B. V.
Bronk, Anal. Chem. 63,1027 (1991); K. C. Ng, W. B.
Whitten, S. A. Arnold, L. M. Ramsey, ibid. 64, 2914
(1992).
. M. Eigen and R. Rigler, Proc. Natl. Acad. Sci. U.S.A.
91, 5740 (1994).
. In fluorescence correlation spectroscopy, the inten-
sity recorded at time t is multiplied by that recorded
at t + At and the product is integrated over a finite
period of time; see D. E. Koppel, Phys. Rev. A 10,
1938 (1974).
. T. T. Perkins, D. E. Smith, S. Chu, Science 264, 819
(1994); T. T. Perkins, S. R. Quake, D. E. Smith, S.
Chu, ibid., p. 822.
. S. B. Smith, L. Finzi, C. Bustamante, ibid. 258,1122
(1992); C. Bustamante, Annu. Rev. Biophys. Bio-
phys. Chem. 20, 415 (1991).
. M. Washizu and 0. Kurosawa, IEEE Trans. Ind. Appl.
26,1165 (1990).
. S. B. Smith, P. K. Aldridge, J. B. Callis, Science 243,
203 (1989).
. N. J. Rampino and A. Chrambach, Anal. Biochem.
194, 278 (1991).
. K. Morikawa and M. Yanagida, J. Biochem. 89, 693
(1981).
. I. Auzanneau, C. Barreau, L. Salome, C. R. Acad.
Sci. Paris 316, 459 (1993).
. H. Kabata et al., Science 262, 1561 (1993).
. D. A. Schafer, J. Gelles, M. P. Sheetz, R. Landick,
Nature 352, 444 (1991).
. Laser excitation at 488.0 and 514.5 nm was provided
by an argon ion laser (Lexel Lasers, Fremont, CA). The
laser beam entered the microscope through a back
port and was directed to an oil-immersion objective
(x100, NA = 1.3, Nikon Instrument Group, Melville,
NY) by a dichroic beamsplitter (505DRLP02 or
22. M. B. Schneider and W. W. Webb, Appl. Opt. 20,
1382 (1981).
23. R. Rigler, U. Mets, J. Widengren, P. Kask, Eur. Bio-
phys. J. 22,169 (1993).
24. W. Feller, An Introduction to Probability Theory and
its Applications (Wiley, New York, ed. 3, 1968).
25. S. A. Soper, H. L. Nutter, R. A. Keller, L. M. Davis, E.
B. Shera, Photochem. Photobiol. 57, 972 (1993).
34. W. P. Ambrose, P. M. Goodwin, J. C. Martin, R. A.
Keller, ibid., p. 364.
35. S.N. acknowledges the Whitaker Foundation for a
young investigator award. D.T.C. is a Beckman Cell
Science Scholar of Stanford University. This work
was supported by Beckman Instruments, Inc.
18 July 1994; accepted 19 September 1994
Molecular Computation of Solutions to
Combinatorial Problems
Leonard M. Adleman
The tools of molecular biology were used to solve an instance of the directed Hamiltonian
path problem. A small graph was encoded in molecules of DNA, and the "operations" of
the computation were performed with standard protocols and enzymes. This experiment
demonstrates the feasibility of carrying out computations at the molecular level.
In 1959, Richard Feynman gave a visionary
talk describing the possibility of building
computers that were "sub-microscopic" (1).
Despite remarkable progress in computer
miniaturization, this goal has yet to be
achieved. Here, the possibility of comput-
ing directly with molecules is explored.
A directed graph G with designated ver-
tices vn and vout is said to have a Hamilto-
nian path (2) if and only if there exists a
sequence of compatible "one-way" edges el,
e2, ... ., e, (that is, a path) that begins at in,
ends at v., and enters every other vertex
exactly once. Figure 1 shows a graph that
for vn = 0 and v01u = 6 has a Hamiltonian
path, given by the edges 0-*1, 1->2, 2->3,
3---4, 4->5, 5->6. If the edge 2->3 were
removed from the graph, then the result-
ing graph with the same designated verti-
ces would not have a Hamiltonian path.
Similarly, if the designated vertices were
changed to vin = 3 and vout = 5 there
Department of Computer Science and Institute for Molec-
ular Medicine and Technology, University of Southern Cal-
ifornia, 941 West 37th Place, Los Angeles, CA 90089,
USA.
SCIENCE * VOL. 266 * 11 NOVEMBER 1994
would be no Hamiltonian path (because,
for example, there are no edges entering
vertex 0).
There are well-known algorithms for de-
ciding whether an arbitrary directed graph
with designated vertices has a Hamiltonian
path or not. However, all known algorithms
for this problem have exponential worst-case
complexity, and hence there are instances of
modest size for which these algorithms re-
quire an impractical amount of computer
time to render a decision. Because the direct-
ed Hamiltonian path problem has been
proven to be NP-complete, it seems likely
that no efficient (that is, polynomial time)
algorithm exists for solving it (2, 3).
The following (nondeterministic) algo-
rithm solves the directed Hamiltonian path
problem:
Step 1: Generate random paths through the
graph.
Step 2: Keep only those paths that begin with vin
and end with v,,f.
Step 3: If the graph has n vertices, then keep
only those paths that enter exactly n vertices.
Step 4: Keep only those paths that enter all of
1021
onFebruary25,2016DownloadedfromonFebruary25,2016DownloadedfromonFebruary25,2016DownloadedfromonFebruary25,2016Downloadedfrom
Science
1994competing
with silicon
embedding
control in
molecular systems
and cells
DNA Computing
ime study of the dynamics of a single mol-
ule and the chemical and biochemical re-
tions that such a molecule may undergo in
lution.
REFERENCES AND NOTES
. W. E. Moerner, Science 265, 46 (1994), and refer-
ences therein.
. M. Orrit, J. Bernard, R. I. Personov, J. Phys. Chem.
97,10256(1993).
. F. GOttler, T. Irngartinger, T. Plakhotnik, A. Renn, U.
P. Wild, Chem. Phys. Lett. 217, 393 (1994).
. M. Ishikawa, K. Hirano, T. Hayakawa, S. Hosoi, S.
Brenner, Jpn. J. Appl. Phys. 33,1571 (1994).
. E. Betzig and R. J. Chichester, Science 262, 1422
(1993).
. J. K. Trautman, J. J. Macklin, L. E. Brus, E. Betzig,
Nature 369, 40 (1994).
. D. C. Nguyen, R. A. Keller, J. H. Jett, J. C. Martin,
Anal. Chem. 59, 2158 (1987).
. K. Peck, L. Stryer, A. N. Glazer, R. A. Mathies, Proc.
Natl. Acad. Sci. U.S.A. 86, 4087 (1989).
. W. B. Whitten, L. M. Ramsey, S. A. Arnold, B. V.
Bronk, Anal. Chem. 63,1027 (1991); K. C. Ng, W. B.
Whitten, S. A. Arnold, L. M. Ramsey, ibid. 64, 2914
(1992).
. M. Eigen and R. Rigler, Proc. Natl. Acad. Sci. U.S.A.
91, 5740 (1994).
. In fluorescence correlation spectroscopy, the inten-
sity recorded at time t is multiplied by that recorded
at t + At and the product is integrated over a finite
period of time; see D. E. Koppel, Phys. Rev. A 10,
1938 (1974).
. T. T. Perkins, D. E. Smith, S. Chu, Science 264, 819
(1994); T. T. Perkins, S. R. Quake, D. E. Smith, S.
Chu, ibid., p. 822.
. S. B. Smith, L. Finzi, C. Bustamante, ibid. 258,1122
(1992); C. Bustamante, Annu. Rev. Biophys. Bio-
phys. Chem. 20, 415 (1991).
. M. Washizu and 0. Kurosawa, IEEE Trans. Ind. Appl.
26,1165 (1990).
. S. B. Smith, P. K. Aldridge, J. B. Callis, Science 243,
203 (1989).
. N. J. Rampino and A. Chrambach, Anal. Biochem.
194, 278 (1991).
. K. Morikawa and M. Yanagida, J. Biochem. 89, 693
(1981).
. I. Auzanneau, C. Barreau, L. Salome, C. R. Acad.
Sci. Paris 316, 459 (1993).
. H. Kabata et al., Science 262, 1561 (1993).
. D. A. Schafer, J. Gelles, M. P. Sheetz, R. Landick,
Nature 352, 444 (1991).
. Laser excitation at 488.0 and 514.5 nm was provided
by an argon ion laser (Lexel Lasers, Fremont, CA). The
laser beam entered the microscope through a back
port and was directed to an oil-immersion objective
(x100, NA = 1.3, Nikon Instrument Group, Melville,
NY) by a dichroic beamsplitter (505DRLP02 or
22. M. B. Schneider and W. W. Webb, Appl. Opt. 20,
1382 (1981).
23. R. Rigler, U. Mets, J. Widengren, P. Kask, Eur. Bio-
phys. J. 22,169 (1993).
24. W. Feller, An Introduction to Probability Theory and
its Applications (Wiley, New York, ed. 3, 1968).
25. S. A. Soper, H. L. Nutter, R. A. Keller, L. M. Davis, E.
B. Shera, Photochem. Photobiol. 57, 972 (1993).
34. W. P. Ambrose, P. M. Goodwin, J. C. Martin, R. A.
Keller, ibid., p. 364.
35. S.N. acknowledges the Whitaker Foundation for a
young investigator award. D.T.C. is a Beckman Cell
Science Scholar of Stanford University. This work
was supported by Beckman Instruments, Inc.
18 July 1994; accepted 19 September 1994
Molecular Computation of Solutions to
Combinatorial Problems
Leonard M. Adleman
The tools of molecular biology were used to solve an instance of the directed Hamiltonian
path problem. A small graph was encoded in molecules of DNA, and the "operations" of
the computation were performed with standard protocols and enzymes. This experiment
demonstrates the feasibility of carrying out computations at the molecular level.
In 1959, Richard Feynman gave a visionary
talk describing the possibility of building
computers that were "sub-microscopic" (1).
Despite remarkable progress in computer
miniaturization, this goal has yet to be
achieved. Here, the possibility of comput-
ing directly with molecules is explored.
A directed graph G with designated ver-
tices vn and vout is said to have a Hamilto-
nian path (2) if and only if there exists a
sequence of compatible "one-way" edges el,
e2, ... ., e, (that is, a path) that begins at in,
ends at v., and enters every other vertex
exactly once. Figure 1 shows a graph that
for vn = 0 and v01u = 6 has a Hamiltonian
path, given by the edges 0-*1, 1->2, 2->3,
3---4, 4->5, 5->6. If the edge 2->3 were
removed from the graph, then the result-
ing graph with the same designated verti-
ces would not have a Hamiltonian path.
Similarly, if the designated vertices were
changed to vin = 3 and vout = 5 there
Department of Computer Science and Institute for Molec-
ular Medicine and Technology, University of Southern Cal-
ifornia, 941 West 37th Place, Los Angeles, CA 90089,
USA.
SCIENCE * VOL. 266 * 11 NOVEMBER 1994
would be no Hamiltonian path (because,
for example, there are no edges entering
vertex 0).
There are well-known algorithms for de-
ciding whether an arbitrary directed graph
with designated vertices has a Hamiltonian
path or not. However, all known algorithms
for this problem have exponential worst-case
complexity, and hence there are instances of
modest size for which these algorithms re-
quire an impractical amount of computer
time to render a decision. Because the direct-
ed Hamiltonian path problem has been
proven to be NP-complete, it seems likely
that no efficient (that is, polynomial time)
algorithm exists for solving it (2, 3).
The following (nondeterministic) algo-
rithm solves the directed Hamiltonian path
problem:
Step 1: Generate random paths through the
graph.
Step 2: Keep only those paths that begin with vin
and end with v,,f.
Step 3: If the graph has n vertices, then keep
only those paths that enter exactly n vertices.
Step 4: Keep only those paths that enter all of
1021
onFebruary25,2016DownloadedfromonFebruary25,2016DownloadedfromonFebruary25,2016DownloadedfromonFebruary25,2016Downloadedfrom
Science
1994
AND/OR
logic gates Source:
Qian & Winfree 2011
saw gate with a few wires can create a catalytic
cycle in which input transforms free fuel into free
output without being consumed in the process
(Fig. 1D and fig. S1, B and C). Initially, the output
signal is bound to the right side of the gate; the
input and fuel signals are free (in our analogy, the
output is riding on the right side of the seesaw
board; the input and fuel are wandering around).
The input signal first releases the output signal and
binds to the gate instead (the input jumps onto the
left side of the board and makes the output jump
off). The fuel signal then displaces the input signal
by binding to the gate in the same way (the fuel
pushes off the input). A catalytic cycle has been
completed. In general, a free signal on one side of
a seesaw gate can catalyze the exchange of signals
on the other side, and this exchange will not hap-
pen without the catalyst. These reactions are driv-
en forward by the entropy of equilibration for the
seesawing reactions. A small amount of free input
can catalyze the release of a large amount of free
output (fig. S2).
Thresholding can be directly combined with a
seesaw catalyst to support a digital abstraction—
which is the basic principle underlying digital
logic in electronics—by pushing the intrinsically
analog signal toward either the ideal ON or OFF
value. Fluorescence kinetics experiments (Fig. 1E)
demonstrated the circuit in Fig. 1A connected to
the reporter in Fig. 1B. The input-versus-output
relationship (plotted in Fig. 1F) reveals a sharp
threshold, ideal for signal restoration.
A cascade of two seesaw gates can compute
the logic function OR or AND. To explain this,
we introduce two composable seesaw compo-
nents for digital circuits. We first define the
gross production of signal X as the total amount
eventually released from the gate:
〈X〉 ¼ ∫
þ∞
0
Xprod
ðtÞdt ð1Þ
Motivated by sequence design constraints (figs. S3
and S4), we then define two types of feedforward
seesaw gates, each assuming an irreversible down-
stream drain. The first type is called an amplifying
gate. It has a threshold and fuel. If the gross
production of its input is greater than the initial
amount of threshold, the output will keep being
released catalytically until it reaches the max-
imum, which is the initial amount of bound
arrives at the gate (negative number). A reporter
that transforms a DNA signal into a fluorescence
signal is represented by half a node with a zigzag
arrow (Fig. 1B), with its initial relative concen-
tration written similar to a threshold.
Each signal is a single-stranded DNA mole-
cule that has two recognition domains identify-
ing the two gates it connects, one on either side
of a central toehold domain. Each gate is asso-
ciated with a gate base strand that has (the com-
plement of ) one recognition domain flanked by
two toehold domains. When a signal strand is
bound to a gate, it forms a gate:signal complex
with the gate’s base strand. At any given mo-
ment (not counting the transient states during
reactions shown in fig. S1C), a gate base strand
always has a signal strand bound to one side,
leaving the toehold on the other side uncovered.
There are three basic reactions involved in
a seesaw network (Fig. 1C and fig. S1C). The
first one is seesawing: A free signal on one side
of a gate can release a signal bound on the other
side of the gate by toehold-mediated strand dis-
placement. The process starts with the free signal
strand (e.g., w2,5) hybridizing to the gate:signal
complex (e.g., G5:5,6) at the uncovered toehold
domain (e.g., T*) and then undergoing branch mi-
gration through the recognition domain (e.g., S5).
The previously bound signal will fall off when it
is attached to the gate base strand only by the
short toehold. The resulting gate:signal complex
(e.g., G2,5:5) will have an uncovered toehold on
the other side, and therefore the now-free signal
(e.g., w5,6) can reverse the process symmetrically.
The second reaction is thresholding: A thresh-
old species associated with a gate and an imping-
ing signal can react with the signal by means
of a longer toehold (e.g., s2*T*), producing
only inert waste species that have no exposed
toehold. Thresholding is much faster than see-
sawing because the toehold-mediated strand dis-
placement rate grows exponentially with toehold
length for short toeholds (7, 8). As a result, see-
sawing effectively only happens when the input
signal exceeds the threshold. The third reaction
is reporting: A reporter species similar to a thresh-
old, but modified with a fluorophore and quench-
er pair, can absorb an impinging signal while
generating a fluorescence signal. Unlike thresh-
binds to the gate instead (the input jumps onto the
left side of the board and makes the output jump
off). The fuel signal then displaces the input signal
by binding to the gate in the same way (the fuel
pushes off the input). A catalytic cycle has been
completed. In general, a free signal on one side of
a seesaw gate can catalyze the exchange of signals
on the other side, and this exchange will not hap-
pen without the catalyst. These reactions are driv-
en forward by the entropy of equilibration for the
seesawing reactions. A small amount of free input
can catalyze the release of a large amount of free
output (fig. S2).
Thresholding can be directly combined with a
seesaw catalyst to support a digital abstraction—
which is the basic principle underlying digital
we introduce two composable seesaw comp
nents for digital circuits. We first define th
gross production of signal X as the total amou
eventually released from the gate:
〈X〉 ¼ ∫
þ∞
0
Xprod
ðtÞdt ð
Motivated by sequence design constraints (figs. S
and S4), we then define two types of feedforwa
seesaw gates, each assuming an irreversible dow
stream drain. The first type is called an amplifyin
gate. It has a threshold and fuel. If the gro
production of its input is greater than the initi
amount of threshold, the output will keep bein
released catalytically until it reaches the ma
imum, which is the initial amount of boun
Fig. 2. Digital logic gates implemented with the seesaw DNA motif. (A) Abstract diagram of
DNA Computing
ime study of the dynamics of a single mol-
ule and the chemical and biochemical re-
tions that such a molecule may undergo in
lution.
REFERENCES AND NOTES
. W. E. Moerner, Science 265, 46 (1994), and refer-
ences therein.
. M. Orrit, J. Bernard, R. I. Personov, J. Phys. Chem.
97,10256(1993).
. F. GOttler, T. Irngartinger, T. Plakhotnik, A. Renn, U.
P. Wild, Chem. Phys. Lett. 217, 393 (1994).
. M. Ishikawa, K. Hirano, T. Hayakawa, S. Hosoi, S.
Brenner, Jpn. J. Appl. Phys. 33,1571 (1994).
. E. Betzig and R. J. Chichester, Science 262, 1422
(1993).
. J. K. Trautman, J. J. Macklin, L. E. Brus, E. Betzig,
Nature 369, 40 (1994).
. D. C. Nguyen, R. A. Keller, J. H. Jett, J. C. Martin,
Anal. Chem. 59, 2158 (1987).
. K. Peck, L. Stryer, A. N. Glazer, R. A. Mathies, Proc.
Natl. Acad. Sci. U.S.A. 86, 4087 (1989).
. W. B. Whitten, L. M. Ramsey, S. A. Arnold, B. V.
Bronk, Anal. Chem. 63,1027 (1991); K. C. Ng, W. B.
Whitten, S. A. Arnold, L. M. Ramsey, ibid. 64, 2914
(1992).
. M. Eigen and R. Rigler, Proc. Natl. Acad. Sci. U.S.A.
91, 5740 (1994).
. In fluorescence correlation spectroscopy, the inten-
sity recorded at time t is multiplied by that recorded
at t + At and the product is integrated over a finite
period of time; see D. E. Koppel, Phys. Rev. A 10,
1938 (1974).
. T. T. Perkins, D. E. Smith, S. Chu, Science 264, 819
(1994); T. T. Perkins, S. R. Quake, D. E. Smith, S.
Chu, ibid., p. 822.
. S. B. Smith, L. Finzi, C. Bustamante, ibid. 258,1122
(1992); C. Bustamante, Annu. Rev. Biophys. Bio-
phys. Chem. 20, 415 (1991).
. M. Washizu and 0. Kurosawa, IEEE Trans. Ind. Appl.
26,1165 (1990).
. S. B. Smith, P. K. Aldridge, J. B. Callis, Science 243,
203 (1989).
. N. J. Rampino and A. Chrambach, Anal. Biochem.
194, 278 (1991).
. K. Morikawa and M. Yanagida, J. Biochem. 89, 693
(1981).
. I. Auzanneau, C. Barreau, L. Salome, C. R. Acad.
Sci. Paris 316, 459 (1993).
. H. Kabata et al., Science 262, 1561 (1993).
. D. A. Schafer, J. Gelles, M. P. Sheetz, R. Landick,
Nature 352, 444 (1991).
. Laser excitation at 488.0 and 514.5 nm was provided
by an argon ion laser (Lexel Lasers, Fremont, CA). The
laser beam entered the microscope through a back
port and was directed to an oil-immersion objective
(x100, NA = 1.3, Nikon Instrument Group, Melville,
NY) by a dichroic beamsplitter (505DRLP02 or
22. M. B. Schneider and W. W. Webb, Appl. Opt. 20,
1382 (1981).
23. R. Rigler, U. Mets, J. Widengren, P. Kask, Eur. Bio-
phys. J. 22,169 (1993).
24. W. Feller, An Introduction to Probability Theory and
its Applications (Wiley, New York, ed. 3, 1968).
25. S. A. Soper, H. L. Nutter, R. A. Keller, L. M. Davis, E.
B. Shera, Photochem. Photobiol. 57, 972 (1993).
34. W. P. Ambrose, P. M. Goodwin, J. C. Martin, R. A.
Keller, ibid., p. 364.
35. S.N. acknowledges the Whitaker Foundation for a
young investigator award. D.T.C. is a Beckman Cell
Science Scholar of Stanford University. This work
was supported by Beckman Instruments, Inc.
18 July 1994; accepted 19 September 1994
Molecular Computation of Solutions to
Combinatorial Problems
Leonard M. Adleman
The tools of molecular biology were used to solve an instance of the directed Hamiltonian
path problem. A small graph was encoded in molecules of DNA, and the "operations" of
the computation were performed with standard protocols and enzymes. This experiment
demonstrates the feasibility of carrying out computations at the molecular level.
In 1959, Richard Feynman gave a visionary
talk describing the possibility of building
computers that were "sub-microscopic" (1).
Despite remarkable progress in computer
miniaturization, this goal has yet to be
achieved. Here, the possibility of comput-
ing directly with molecules is explored.
A directed graph G with designated ver-
tices vn and vout is said to have a Hamilto-
nian path (2) if and only if there exists a
sequence of compatible "one-way" edges el,
e2, ... ., e, (that is, a path) that begins at in,
ends at v., and enters every other vertex
exactly once. Figure 1 shows a graph that
for vn = 0 and v01u = 6 has a Hamiltonian
path, given by the edges 0-*1, 1->2, 2->3,
3---4, 4->5, 5->6. If the edge 2->3 were
removed from the graph, then the result-
ing graph with the same designated verti-
ces would not have a Hamiltonian path.
Similarly, if the designated vertices were
changed to vin = 3 and vout = 5 there
Department of Computer Science and Institute for Molec-
ular Medicine and Technology, University of Southern Cal-
ifornia, 941 West 37th Place, Los Angeles, CA 90089,
USA.
SCIENCE * VOL. 266 * 11 NOVEMBER 1994
would be no Hamiltonian path (because,
for example, there are no edges entering
vertex 0).
There are well-known algorithms for de-
ciding whether an arbitrary directed graph
with designated vertices has a Hamiltonian
path or not. However, all known algorithms
for this problem have exponential worst-case
complexity, and hence there are instances of
modest size for which these algorithms re-
quire an impractical amount of computer
time to render a decision. Because the direct-
ed Hamiltonian path problem has been
proven to be NP-complete, it seems likely
that no efficient (that is, polynomial time)
algorithm exists for solving it (2, 3).
The following (nondeterministic) algo-
rithm solves the directed Hamiltonian path
problem:
Step 1: Generate random paths through the
graph.
Step 2: Keep only those paths that begin with vin
and end with v,,f.
Step 3: If the graph has n vertices, then keep
only those paths that enter exactly n vertices.
Step 4: Keep only those paths that enter all of
1021
onFebruary25,2016DownloadedfromonFebruary25,2016DownloadedfromonFebruary25,2016DownloadedfromonFebruary25,2016Downloadedfrom
Science
1994
a
b
11 *
5*
6*
4
3*
2*
1*
76*3*
4
1 1
52
1
1
1*
5*2*
1*
Track base
Track base Track base Track base
1*1
7 2*
3*
1*1
6
7
11*
5*
1
5
6*3
4
11*
2*
1
2
3*6
7
11*
5*
1
5
6*
7*
1*
4*
1* 1*
7*
1
7*5
6
Walker
Hairpin H1 Hairpin H2
1*
2* 5*
1*1
25
11
4
3* 6* 7 2*
3* 3*
2*76*3*
1
52
1 1*
5*2*
1* 1
4
1
2
33
4
11*
2* 5*
1*1
2 5
1
3* 6* 7 2*
3*
1
2
1 1*1 1*1
1*
4*
Expended track Fresh track
Direction
of motion
3
2 4*
1*1
1*
3
2 4*
REVIEW ARTICLENATURE CHEMISTRY DOI: 10.1038/NCHEM.957
Source:
Omabegho, T., Sha, R. & Seeman, N. C. 2009
Molecular
walkers
DNA Computing
ime study of the dynamics of a single mol-
ule and the chemical and biochemical re-
tions that such a molecule may undergo in
lution.
REFERENCES AND NOTES
. W. E. Moerner, Science 265, 46 (1994), and refer-
ences therein.
. M. Orrit, J. Bernard, R. I. Personov, J. Phys. Chem.
97,10256(1993).
. F. GOttler, T. Irngartinger, T. Plakhotnik, A. Renn, U.
P. Wild, Chem. Phys. Lett. 217, 393 (1994).
. M. Ishikawa, K. Hirano, T. Hayakawa, S. Hosoi, S.
Brenner, Jpn. J. Appl. Phys. 33,1571 (1994).
. E. Betzig and R. J. Chichester, Science 262, 1422
(1993).
. J. K. Trautman, J. J. Macklin, L. E. Brus, E. Betzig,
Nature 369, 40 (1994).
. D. C. Nguyen, R. A. Keller, J. H. Jett, J. C. Martin,
Anal. Chem. 59, 2158 (1987).
. K. Peck, L. Stryer, A. N. Glazer, R. A. Mathies, Proc.
Natl. Acad. Sci. U.S.A. 86, 4087 (1989).
. W. B. Whitten, L. M. Ramsey, S. A. Arnold, B. V.
Bronk, Anal. Chem. 63,1027 (1991); K. C. Ng, W. B.
Whitten, S. A. Arnold, L. M. Ramsey, ibid. 64, 2914
(1992).
. M. Eigen and R. Rigler, Proc. Natl. Acad. Sci. U.S.A.
91, 5740 (1994).
. In fluorescence correlation spectroscopy, the inten-
sity recorded at time t is multiplied by that recorded
at t + At and the product is integrated over a finite
period of time; see D. E. Koppel, Phys. Rev. A 10,
1938 (1974).
. T. T. Perkins, D. E. Smith, S. Chu, Science 264, 819
(1994); T. T. Perkins, S. R. Quake, D. E. Smith, S.
Chu, ibid., p. 822.
. S. B. Smith, L. Finzi, C. Bustamante, ibid. 258,1122
(1992); C. Bustamante, Annu. Rev. Biophys. Bio-
phys. Chem. 20, 415 (1991).
. M. Washizu and 0. Kurosawa, IEEE Trans. Ind. Appl.
26,1165 (1990).
. S. B. Smith, P. K. Aldridge, J. B. Callis, Science 243,
203 (1989).
. N. J. Rampino and A. Chrambach, Anal. Biochem.
194, 278 (1991).
. K. Morikawa and M. Yanagida, J. Biochem. 89, 693
(1981).
. I. Auzanneau, C. Barreau, L. Salome, C. R. Acad.
Sci. Paris 316, 459 (1993).
. H. Kabata et al., Science 262, 1561 (1993).
. D. A. Schafer, J. Gelles, M. P. Sheetz, R. Landick,
Nature 352, 444 (1991).
. Laser excitation at 488.0 and 514.5 nm was provided
by an argon ion laser (Lexel Lasers, Fremont, CA). The
laser beam entered the microscope through a back
port and was directed to an oil-immersion objective
(x100, NA = 1.3, Nikon Instrument Group, Melville,
NY) by a dichroic beamsplitter (505DRLP02 or
22. M. B. Schneider and W. W. Webb, Appl. Opt. 20,
1382 (1981).
23. R. Rigler, U. Mets, J. Widengren, P. Kask, Eur. Bio-
phys. J. 22,169 (1993).
24. W. Feller, An Introduction to Probability Theory and
its Applications (Wiley, New York, ed. 3, 1968).
25. S. A. Soper, H. L. Nutter, R. A. Keller, L. M. Davis, E.
B. Shera, Photochem. Photobiol. 57, 972 (1993).
34. W. P. Ambrose, P. M. Goodwin, J. C. Martin, R. A.
Keller, ibid., p. 364.
35. S.N. acknowledges the Whitaker Foundation for a
young investigator award. D.T.C. is a Beckman Cell
Science Scholar of Stanford University. This work
was supported by Beckman Instruments, Inc.
18 July 1994; accepted 19 September 1994
Molecular Computation of Solutions to
Combinatorial Problems
Leonard M. Adleman
The tools of molecular biology were used to solve an instance of the directed Hamiltonian
path problem. A small graph was encoded in molecules of DNA, and the "operations" of
the computation were performed with standard protocols and enzymes. This experiment
demonstrates the feasibility of carrying out computations at the molecular level.
In 1959, Richard Feynman gave a visionary
talk describing the possibility of building
computers that were "sub-microscopic" (1).
Despite remarkable progress in computer
miniaturization, this goal has yet to be
achieved. Here, the possibility of comput-
ing directly with molecules is explored.
A directed graph G with designated ver-
tices vn and vout is said to have a Hamilto-
nian path (2) if and only if there exists a
sequence of compatible "one-way" edges el,
e2, ... ., e, (that is, a path) that begins at in,
ends at v., and enters every other vertex
exactly once. Figure 1 shows a graph that
for vn = 0 and v01u = 6 has a Hamiltonian
path, given by the edges 0-*1, 1->2, 2->3,
3---4, 4->5, 5->6. If the edge 2->3 were
removed from the graph, then the result-
ing graph with the same designated verti-
ces would not have a Hamiltonian path.
Similarly, if the designated vertices were
changed to vin = 3 and vout = 5 there
Department of Computer Science and Institute for Molec-
ular Medicine and Technology, University of Southern Cal-
ifornia, 941 West 37th Place, Los Angeles, CA 90089,
USA.
SCIENCE * VOL. 266 * 11 NOVEMBER 1994
would be no Hamiltonian path (because,
for example, there are no edges entering
vertex 0).
There are well-known algorithms for de-
ciding whether an arbitrary directed graph
with designated vertices has a Hamiltonian
path or not. However, all known algorithms
for this problem have exponential worst-case
complexity, and hence there are instances of
modest size for which these algorithms re-
quire an impractical amount of computer
time to render a decision. Because the direct-
ed Hamiltonian path problem has been
proven to be NP-complete, it seems likely
that no efficient (that is, polynomial time)
algorithm exists for solving it (2, 3).
The following (nondeterministic) algo-
rithm solves the directed Hamiltonian path
problem:
Step 1: Generate random paths through the
graph.
Step 2: Keep only those paths that begin with vin
and end with v,,f.
Step 3: If the graph has n vertices, then keep
only those paths that enter exactly n vertices.
Step 4: Keep only those paths that enter all of
1021
onFebruary25,2016DownloadedfromonFebruary25,2016DownloadedfromonFebruary25,2016DownloadedfromonFebruary25,2016Downloadedfrom
Science
1994
Universal substrate for
reaction kinetics
DNA as a universal substrate for chemical kinetics
David Soloveichika,1
, Georg Seeliga,b,1
, and Erik Winfreec,1
a
Department of Computer Science and Engineering, University of Washington, Seattle, WA 98195; b
Department of Electrical Engineering, University of
Washington, Seattle, WA 98195; and c
Departments of Computer Science, Computation and Neural Systems, and Bioengineering, California Institute of
Technology, Pasadena, CA 91125
Edited by José N. Onuchic, University of California San Diego, La Jolla, CA, and approved January 29, 2010 (received for review August 18, 2009)
Molecular programming aims to systematically engineer molecular
and chemical systems of autonomous function and ever-increasing
complexity. A key goal is to develop embedded control circuitry
within a chemical system to direct molecular events. Here we show
that systems of DNA molecules can be constructed that closely ap-
proximate the dynamic behavior of arbitrary systems of coupled
chemical reactions. By using strand displacement reactions as a
primitive, we construct reaction cascades with effectively unimole-
cular and bimolecular kinetics. Our construction allows individual
reactions to be coupled in arbitrary ways such that reactants can
participate in multiple reactions simultaneously, reproducing the
desired dynamical properties. Thus arbitrary systems of chemical
equations can be compiled into real chemical systems. We illustrate
our method on the Lotka–Volterra oscillator, a limit-cycle oscillator,
a chaotic system, and systems implementing feedback digital logic
and algorithmic behavior.
molecular programming ∣ mass-action kinetics ∣ strand displacement
cascades ∣ chemical reaction networks ∣ nonlinear chemical dynamics
Chemical reaction equations and mass-action kinetics provide
a powerful mathematical language to describe and analyze
chemical systems. For well over a century, mass-action kinetics
has been used to model chemical experiments and to predict
and explain their dynamical properties. Both biological and non-
biological chemical systems can exhibit complex behaviors such as
oscillations, memory, logic and feedback control, chaos, and pat-
tern formation—all of which can be explained by the correspond-
ing systems of coupled chemical reactions (1–4). Whereas the use
wire the components to achieve particular functions. Attempts to
systematically understand what functional behaviors can be ob-
tained by using such components have targeted connections to
analog and digital electronic circuits (10, 18, 19), neural networks
(20–22), and computing machines (15, 20, 23, 24); in each case,
complex systems are theoretically constructed by composing
modular chemical subsystems that carry out key functions, such
as boolean logic gates, binary memories, or neural computing
units. Despite its apparent difficulty, we directly targeted CRNs
for three reasons. First, shoehorning the design of synthetic
chemical circuits into familiar but possibly inappropriate comput-
ing models may not capture the natural potential and limitations
of the chemical substrate. Second, there is a vast literature on
the theory of CRNs (25, 26) and even on general methods to im-
plement arbitrary polynomial ordinary differential equations as
CRNs (27, 28). Third, as a fundamental model that captures
the essential formal structure of chemistry, implementation of
CRNs could provide a useful programming paradigm for mole-
cular systems.
Here we propose a method for compiling an arbitrary CRN
into nucleic-acid-based chemistry. Given a formal specification
of coupled chemical kinetics, we systematically design DNA mo-
lecules implementing an approximation of the system scaled to an
appropriate temporal and concentration regime. Formal species
are identified with certain DNA strands, whose interactions are
mediated by a set of auxiliary DNA complexes. Nonconserving
CRNs can be implemented because the auxiliary species implic-
itly supply energy and mass.
OGY
Soloveichik et al. 2010
1:
12:
3:
2
3
buffering module
time (hrs)
concentration(nM)
unscaled
1.5
1
1
5.105 /M/s
1/300 /s
1/300 /s
scaled
0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5
5
10
15
20
A B CIdeal chemical reactions DNA reaction modules Simulation of ideal and DNA reactions
ideal DNA
DNA Computing
NAM data
storage
ime study of the dynamics of a single mol-
ule and the chemical and biochemical re-
tions that such a molecule may undergo in
lution.
REFERENCES AND NOTES
. W. E. Moerner, Science 265, 46 (1994), and refer-
ences therein.
. M. Orrit, J. Bernard, R. I. Personov, J. Phys. Chem.
97,10256(1993).
. F. GOttler, T. Irngartinger, T. Plakhotnik, A. Renn, U.
P. Wild, Chem. Phys. Lett. 217, 393 (1994).
. M. Ishikawa, K. Hirano, T. Hayakawa, S. Hosoi, S.
Brenner, Jpn. J. Appl. Phys. 33,1571 (1994).
. E. Betzig and R. J. Chichester, Science 262, 1422
(1993).
. J. K. Trautman, J. J. Macklin, L. E. Brus, E. Betzig,
Nature 369, 40 (1994).
. D. C. Nguyen, R. A. Keller, J. H. Jett, J. C. Martin,
Anal. Chem. 59, 2158 (1987).
. K. Peck, L. Stryer, A. N. Glazer, R. A. Mathies, Proc.
Natl. Acad. Sci. U.S.A. 86, 4087 (1989).
. W. B. Whitten, L. M. Ramsey, S. A. Arnold, B. V.
Bronk, Anal. Chem. 63,1027 (1991); K. C. Ng, W. B.
Whitten, S. A. Arnold, L. M. Ramsey, ibid. 64, 2914
(1992).
. M. Eigen and R. Rigler, Proc. Natl. Acad. Sci. U.S.A.
91, 5740 (1994).
. In fluorescence correlation spectroscopy, the inten-
sity recorded at time t is multiplied by that recorded
at t + At and the product is integrated over a finite
period of time; see D. E. Koppel, Phys. Rev. A 10,
1938 (1974).
. T. T. Perkins, D. E. Smith, S. Chu, Science 264, 819
(1994); T. T. Perkins, S. R. Quake, D. E. Smith, S.
Chu, ibid., p. 822.
. S. B. Smith, L. Finzi, C. Bustamante, ibid. 258,1122
(1992); C. Bustamante, Annu. Rev. Biophys. Bio-
phys. Chem. 20, 415 (1991).
. M. Washizu and 0. Kurosawa, IEEE Trans. Ind. Appl.
26,1165 (1990).
. S. B. Smith, P. K. Aldridge, J. B. Callis, Science 243,
203 (1989).
. N. J. Rampino and A. Chrambach, Anal. Biochem.
194, 278 (1991).
. K. Morikawa and M. Yanagida, J. Biochem. 89, 693
(1981).
. I. Auzanneau, C. Barreau, L. Salome, C. R. Acad.
Sci. Paris 316, 459 (1993).
. H. Kabata et al., Science 262, 1561 (1993).
. D. A. Schafer, J. Gelles, M. P. Sheetz, R. Landick,
Nature 352, 444 (1991).
. Laser excitation at 488.0 and 514.5 nm was provided
by an argon ion laser (Lexel Lasers, Fremont, CA). The
laser beam entered the microscope through a back
port and was directed to an oil-immersion objective
(x100, NA = 1.3, Nikon Instrument Group, Melville,
NY) by a dichroic beamsplitter (505DRLP02 or
22. M. B. Schneider and W. W. Webb, Appl. Opt. 20,
1382 (1981).
23. R. Rigler, U. Mets, J. Widengren, P. Kask, Eur. Bio-
phys. J. 22,169 (1993).
24. W. Feller, An Introduction to Probability Theory and
its Applications (Wiley, New York, ed. 3, 1968).
25. S. A. Soper, H. L. Nutter, R. A. Keller, L. M. Davis, E.
B. Shera, Photochem. Photobiol. 57, 972 (1993).
34. W. P. Ambrose, P. M. Goodwin, J. C. Martin, R. A.
Keller, ibid., p. 364.
35. S.N. acknowledges the Whitaker Foundation for a
young investigator award. D.T.C. is a Beckman Cell
Science Scholar of Stanford University. This work
was supported by Beckman Instruments, Inc.
18 July 1994; accepted 19 September 1994
Molecular Computation of Solutions to
Combinatorial Problems
Leonard M. Adleman
The tools of molecular biology were used to solve an instance of the directed Hamiltonian
path problem. A small graph was encoded in molecules of DNA, and the "operations" of
the computation were performed with standard protocols and enzymes. This experiment
demonstrates the feasibility of carrying out computations at the molecular level.
In 1959, Richard Feynman gave a visionary
talk describing the possibility of building
computers that were "sub-microscopic" (1).
Despite remarkable progress in computer
miniaturization, this goal has yet to be
achieved. Here, the possibility of comput-
ing directly with molecules is explored.
A directed graph G with designated ver-
tices vn and vout is said to have a Hamilto-
nian path (2) if and only if there exists a
sequence of compatible "one-way" edges el,
e2, ... ., e, (that is, a path) that begins at in,
ends at v., and enters every other vertex
exactly once. Figure 1 shows a graph that
for vn = 0 and v01u = 6 has a Hamiltonian
path, given by the edges 0-*1, 1->2, 2->3,
3---4, 4->5, 5->6. If the edge 2->3 were
removed from the graph, then the result-
ing graph with the same designated verti-
ces would not have a Hamiltonian path.
Similarly, if the designated vertices were
changed to vin = 3 and vout = 5 there
Department of Computer Science and Institute for Molec-
ular Medicine and Technology, University of Southern Cal-
ifornia, 941 West 37th Place, Los Angeles, CA 90089,
USA.
SCIENCE * VOL. 266 * 11 NOVEMBER 1994
would be no Hamiltonian path (because,
for example, there are no edges entering
vertex 0).
There are well-known algorithms for de-
ciding whether an arbitrary directed graph
with designated vertices has a Hamiltonian
path or not. However, all known algorithms
for this problem have exponential worst-case
complexity, and hence there are instances of
modest size for which these algorithms re-
quire an impractical amount of computer
time to render a decision. Because the direct-
ed Hamiltonian path problem has been
proven to be NP-complete, it seems likely
that no efficient (that is, polynomial time)
algorithm exists for solving it (2, 3).
The following (nondeterministic) algo-
rithm solves the directed Hamiltonian path
problem:
Step 1: Generate random paths through the
graph.
Step 2: Keep only those paths that begin with vin
and end with v,,f.
Step 3: If the graph has n vertices, then keep
only those paths that enter exactly n vertices.
Step 4: Keep only those paths that enter all of
1021
onFebruary25,2016DownloadedfromonFebruary25,2016DownloadedfromonFebruary25,2016DownloadedfromonFebruary25,2016Downloadedfrom
Science
1994
commentary
Nucleic acid memory
Victor Zhirnov, Reza M. Zadegan, Gurtej S. Sandhu, George M. Church and William L. Hughes
Nucleic acid memory has a retention time far exceeding electronic memory. As an alternative storage
media, DNA surpasses the information density and energy of operation offered by flash memory.
I
nformation and communication
technologies generate vast amounts of
data that will far eclipse today’s data flows
In this Commentary, we discuss
the information retention, density and
energetics of NAM — specifically related to
unfathomable technological advances —
such as those from the Human Genome
Project — with the scaling expertise of the
Zhirnov et al. 2016
10cm
10cm
10cm
Abstract Data Types
Container
List
Associative array
Multimap
Set
Stack
Queue
Double-ended queue
Priority queue
Tree
Graph
DNA Molecular Stack Recorder
Record occurence of events in a cell
Interfere with cellular mRNA in an
ordered way
Release a “trigger” signal for
downstream processes after certain
cellular events have transpired
……
b'
aa'
d'c'
g'
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b
c a
a'
b
d e
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x
c'
push write-X
pop
b
a'a
dc
g
f'f
e
report
x'm
0 1
GAAG
U
G
U
G
U
G
CGGGAGAU
G
G C
U C U C C C G
A
A
G U G G U
G
U
C C G C C G
G G
C
A
GCGGCGG
U
U
G
G
U
C
UCCC
b'
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d'c'
g'
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c a
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pop
b
a'a
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g
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report
x'm
0 1
G
G
G
AG
A
C
C
A
A
CCGCCGC
U
G
C C
C G G C G G
A
C
A C C A C
U
U
C G G G A G A
G C
C
AUCUCCCG
C
A
C
A
C
ACUUC
d' c'e'
start push
read pop
b
a'a
dc
g
f'f
e
0 1
GA
A
G
U
G
G
U
G U
U
U
G
G
U
C U
C
C
C
G
A
A G
U
G
U
G
U
G
C
DNA Bricks
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
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lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'm
0 1
GGGAGAG
C
C A
U C U C C C
G C A
C
A
C
A
CUU
C
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
start push write-X
b g
0 1
G
G
G
AG
A
G
C
C
A
UCUCCCG
C
AC
A
C
A
C U U
C G G G A G A
C
C
A A
A
U
U
A
G
U
A
G
G
U
A G
A
C
A
A
A
A
A
A A G A C
C
G
C
U
A
A
A
C
UCUAA
U
C
A
CA
C
C
U
A
C
U
A
A
U
A
C
A
C
C
ACUUC
(27nt)
(31nt)
(64nt)
(64nt)
(98nt)
b'
aa'
d'c'
g'
ff'
e'
b
c a
a'
b
d e
hh'
lk
x
c'
push write-X
pop
b
a'a
dc
g
f'f
e
report
x'm
0 1
GAAG
U
G
U
G
U
G
CGGGAGAU
G
G C
U C U C C C G
A
A
G U G G U
G
U
C C G C C G
G G
C
A
GCGGCGG
U
U
G
G
U
C
UCCC
b'
aa'
d'c'
g'
ff'
e'
c a
a'
b
d e
hh'
lk
x
push write-X
pop
b
a'a
dc
g
f'f
e
report
x'm
0 1
G
G
G
AG
A
C
C
A
A
CCGCCGC
U
G
C C
C G G C G G
A
C
A C C A C
U
U
C G G G A G A
G C
C
AUCUCCCG
C
A
C
A
C
ACUUC
DNA Bricks
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'm
0 1
GGGAGAG
C
C A
U C U C C C
G C A
C
A
C
A
CUU
C
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
start push write-X
b g
0 1
G
G
G
AG
A
G
C
C
A
UCUCCCG
C
AC
A
C
A
C U U
C G G G A G A
C
C
A A
A
U
U
A
G
U
A
G
G
U
A G
A
C
A
A
A
A
A
A A G A C
C
G
C
U
A
A
A
C
UCUAA
U
C
A
CA
C
C
U
A
C
U
A
A
U
A
C
A
C
C
ACUUC
(27nt)
(64nt)
(64nt)
(98nt)
Genetic Algorithm - MOO
Find a set of brick sequences such that:
Individual bricks have the required
secondary structure (e.g. hairpins and ss/ds segments)
Desired brick-pair reactions have a
maximally negative Gibbs free energy of binding
Undesired brick-pair reactions have
close to 0 or positive Gibbs free energy of binding
DNA Molecular Stack Recorder
c a
a*
b
ca a*
b
c*a* a
b*
d*f* f
g*
e*
c a b a*
c* a* b* a d*f* f
g*
e*
*
b
RECORDING
Start
UCNC2016, Annunziata Lopiccolo – Newcastle University
stack
empty
Recording signals
DNA Molecular Stack Recorder
c a
a*
b
ca a*
b
c*a* a
b*
d*f* f
g*
e*
c a b a*
c* a* b* a d*f* f
g*
e*
*
b
c a
a*
b
c*a* a
b*
d*f* f
g*
e*
ca a*
b
c*a* a
b*
d*f* f
g*
e*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
Start
Push
UCNC2016, Annunziata Lopiccolo – Newcastle University
stack
empty
Recording signals
DNA Molecular Stack Recorder
c a
a*
b
ca a*
b
c*a* a
b*
d*f* f
g*
e*
c a b a*
c* a* b* a d*f* f
g*
e*
a
a*
b
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
b
Push binds by toehold
UCNC2016, Annunziata Lopiccolo – Newcastle University
stack
empty
Recording signals
DNA Molecular Stack Recorder
c a
a*
b
ca a*
b
c*a* a
b*
d*f* f
g*
e*
c a b a*
c* a* b* a d*f* f
g*
e*
*
b
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c a b a*
c* a* b* a d*f* f
g*
e*
d e ca a*
b
c*a* a
b*
d*f* f
g*
e*
Branch migration
UCNC2016, Annunziata Lopiccolo – Newcastle University
stack
empty
Recording signals
DNA Molecular Stack Recorder
c a
a*
b
ca a*
b
c*a* a
b*
d*f* f
g*
e*
c a b a*
c* a* b* a d*f* f
g*
e*
*
b
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c a b a*
c* a* b* a d*f* f
g*
e*
d e ca a*
b
c*a* a
b*
d*f* f
g*
e*
a*
b
c*a* a
b*
d*f* f
g*
e*
ca a*
b
c*a* a
b*
d*f* f
g*
e*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c*a* a
b*
d*f* f
g*
e*
c a b a*
c* a* b* a d*f* f
g*
e*
d e ca a*
b
c*a* a
b*
d*f* f
g*
e*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f*
d e c a
a*
b
c a b a* d e c a b a* d
X
UCNC2016, Annunziata Lopiccolo – Newcastle University
stack
empty
X
Recording signals
DNA Molecular Stack Recorder
c a
a*
b
ca a*
b
c*a* a
b*
d*f* f
g*
e*
c a b a*
c* a* b* a d*f* f
g*
e*
b
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c a b a*
c* a* b* a d*f* f
g*
e*
d e ca a*
b
c*a* a
b*
d*f* f
g*
e*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
b
X
Hybridisation
UCNC2016, Annunziata Lopiccolo – Newcastle University
1 signal
on stack
X
Recording signals
DNA Molecular Stack Recorder
c a
a*
b
ca a*
b
c*a* a
b*
d*f* f
g*
e*
c a b a*
c* a* b* a d*f* f
g*
e*
b
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c a b a*
c* a* b* a d*f* f
g*
e*
d e ca a*
b
c*a* a
b*
d*f* f
g*
e*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
b
c a
a*
b
c*a* a
b*
d*f* f
g*
e*
ca a*
b
c*a* a
b*
d*f* f
g*
e*
c a b a*
c* a* b* a d*f* f e*
Push
X
UCNC2016, Annunziata Lopiccolo – Newcastle University
1 signal
on stack
X
Recording signals
DNA Molecular Stack Recorder
ca a*
c*a* a
b*
d*f* f
g*
e*
c a b a*
c* a* b* a d*f* f
g*
e*
b
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c a b a*
c* a* b* a d*f* f
g*
e*
d e ca a*
b
c*a* a
b*
d*f* f
g*
e*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
b
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
X
Push binds by toehold
UCNC2016, Annunziata Lopiccolo – Newcastle University
1 signal
on stack
X
Recording signals
DNA Molecular Stack Recorder
c a b a*
c* a* b* a d*f* f
g*
e*
b
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c a b a*
c* a* b* a d*f* f
g*
e*
d e ca a*
b
c*a* a
b*
d*f* f
g*
e*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
b
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e ca a*
b
c*a* a
b*
d*f* f
g*
e*
X
Branch migration
UCNC2016, Annunziata Lopiccolo – Newcastle University
1 signal
on stack
X
Recording signals
DNA Molecular Stack Recorder
c a b a*
c* a* b* a d*f* f
g*
e*
b
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c a b a*
c* a* b* a d*f* f
g*
e*
d e ca a*
b
c*a* a
b*
d*f* f
g*
e*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
b
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e ca a*
b
c*a* a
b*
d*f* f
g*
e*
a*
b
c*a* a
b*
d*f* f
g*
e*
ca a*
b
c*a* a
b*
d*f* f
g*
e*
c a b a*
c* a* b* a d*f*
g
d e c a
a*
b
c a b a*
c* a* b* a d*f*
g
d
c*a* a
b*
d*f* f
g*
e*
c a b a*
c* a* b* a d*f*
g
d
c a b a*
c* a* b* a d*f*
g
d
d e c a
a*
b
c a b a* d
X
Y
Signal Y
UCNC2016, Annunziata Lopiccolo – Newcastle University
1 signal
on stack
X
Y
Recording signals
DNA Molecular Stack Recorder
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c a b a*
c* a* b* a d*f* f
g*
e*
d e ca a*
b
c*a* a
b*
d*f* f
g*
e*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
b
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e ca a*
b
c*a* a
b*
d*f* f
g*
e*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
b
X Y
Hybridisation
UCNC2016, Annunziata Lopiccolo – Newcastle University
2 signals
on stack
X
Y
Recording signals
DNA Molecular Stack Recorder
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c a b a*
c* a* b* a d*f* f
g*
e*
d e ca a*
b
c*a* a
b*
d*f* f
g*
e*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
b
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e ca a*
b
c*a* a
b*
d*f* f
g*
e*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
b
c a
a*
b
c*a* a
b*
d*f* f
g*
e*
ca a*
b
c*a* a
b*
d*f* f
g*
e*
c a b a*
c* a* b* a d
Push
X Y
UCNC2016, Annunziata Lopiccolo – Newcastle University
2 signals
on stack
X
Y
Recording signals
DNA Molecular Stack Recorder
c a b a*
c* a* b* a d*f* f
g*
e*
d e ca a*
c*a* a
b*
d*f* f
g*
e*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
b
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e ca a*
b
c*a* a
b*
d*f* f
g*
e*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
b
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
X Y
Push binds by toehold
UCNC2016, Annunziata Lopiccolo – Newcastle University
2 signals
on stack
X
Y
Recording signals
DNA Molecular Stack Recorder
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
b
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e ca a*
b
c*a* a
b*
d*f* f
g*
e*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
b
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
Branch migration
X Y
UCNC2016, Annunziata Lopiccolo – Newcastle University
2 signals
on stack
X
Y
Recording signals
DNA Molecular Stack Recorder
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
b
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e ca a*
b
c*a* a
b*
d*f* f
g*
e*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
b
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c*a* a
b*
d*f* f
g*
e*
ca
c*a
c
c*
d e c a
a*
b
c
c*
c*a* a
b*
d*f* f
g*
e*
c
c*
c
c*
d e c a
a*
b
c
c*
X Y
X
Signal X
UCNC2016, Annunziata Lopiccolo – Newcastle University
2 signals
on stack
X
Y
X
Recording signals
DNA Molecular Stack Recorder
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e ca a*
b
c*a* a
b*
d*f* f
g*
e*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
*
b
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
X Y X
Hybridisation
UCNC2016, Annunziata Lopiccolo – Newcastle University
3 signals
on stack
X
Y
X
Recording signals
DNA Molecular Stack Recorder
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e ca a*
b
c*a* a
b*
d*f* f
g*
e*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
*
b
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
X Y X
Hybridisation
UCNC2016, Annunziata Lopiccolo – Newcastle University
3 signals
on stack
X
Y
X
Popping signals
DNA Molecular Stack Recorder
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c*e*
d*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*d* e* c*
ac
Y XX
c a
c* a*
d* e* c*
c a
c* a*
c a
Read
UCNC2016, Annunziata Lopiccolo – Newcastle University
3 signals
on stack
X
Y
X
Popping signals
DNA Molecular Stack Recorder
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c*e*
d*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*d* e* c*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
e f*
g
fd a*
b
ac
Y XX
Read binds by toehold,
Branch migration follows
UCNC2016, Annunziata Lopiccolo – Newcastle University
3 signals
on stack
X
Y
X
Popping signals
DNA Molecular Stack Recorder
b a*
b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
b a*
b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c*e*
d*
b a*
b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
d* e* c*
b a*
b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
e f*
g
fd a*
b
ac
b a*
b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c a b a* d f g f* e
c* a* b* a d* f* g* f e*
b a*
b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c*e*
d*
Y
X
X Strand displacement
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c*e*
d*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*d* e* c*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
e f*
g
fd a*
b
ac
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c a b a*
c* a* b* a
Signal X released
UCNC2016, Annunziata Lopiccolo – Newcastle University
2 signals
on stack
X
Y
X
Popping signals
DNA Molecular Stack Recorderd* e* c*
c
c*
c
c*
ca a*
b
df f*
g
e
c
c*
c
c*
d* e* c*
c
c*
c
d*f* f
g*
e* c* a* b* a d*f* f
g*
e* a*
d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c*e*
d*
d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
d* e* c*
d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
e f*
g
fd a*
b
ac
d*f* f
g*
e*
d e c a
a*
b
c a b a* d f g f* e
c* a* b* a d* f* g* f e*
d*f* f
g*
e*
d e c a
a*
b
c*e*
d*
d e c a
Y
X
X
Pop
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c*e*
d*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*d* e* c*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
e f*
g
fd a*
b
ac
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c a b a*
c* a* b* a
UCNC2016, Annunziata Lopiccolo – Newcastle University
2 signals
on stack
X
Y
Popping signals
DNA Molecular Stack Recorder
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c*e*
d*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*d* e* c*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
e f*
g
fd a*
b
ac
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c a b a*
c* a* b* a
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c*e*
d*
d*f* f
g*
e* c* a* b* a d*f* f
g*
e* a*
d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c*e*
d*
d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
d* e* c*
d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
e f*
g
fd a*
b
ac
d*f* f
g*
e*
d e c a
a*
b
c a b a* d f g f* e
c* a* b* a d* f* g* f e*
d*f* f
g*
e*
d e c a
a*
b
c*e*
d*
d e c a
Y
X
X
Pop binds by toehold,
Branch migration follows
UCNC2016, Annunziata Lopiccolo – Newcastle University
2 signals
on stack
X
Y
Popping signals
DNA Molecular Stack Recorder
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c*e*
d*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*d* e* c*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
e f*
g
fd a*
b
ac
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c a b a*
c* a* b* a
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c*e*
d*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*d* e* c*
ac
d*f* f
g*
e* c* a* b* a d*f* f
g*
e* a*
d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c*e*
d*
d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
d* e* c*
d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
e f*
g
fd a*
b
ac
d*f* f
g*
e*
d e c a
a*
b
c a b a* d f g f* e
c* a* b* a d* f* g* f e*
d*f* f
g*
e*
d e c a
a*
b
c*e*
d*
d e c a
Y
X
X Hairpin
reforms
*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c*e*
d*
*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
d* e* c*
*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
e f*
g
fd a*
b
ac
*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c a b a* d f g f* e
c* a* b* a d* f* g* f e*
*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c*e*
d*
Pop-Push
double strand
UCNC2016, Annunziata Lopiccolo – Newcastle University
2 signals
on stack
X
Y
Popping signals
DNA Molecular Stack Recorder
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c*e*
d*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*d* e* c*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
e f*
g
fd a*
b
ac
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c a b a*
c* a* b* a
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c*e*
d*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*d* e* c*
ac
d*f* f
g*
e* c* a* b* a d*f* f
g*
e* a*
d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c*e*
d*
d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
d* e* c*
d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
e f*
g
fd a*
b
ac
d*f* f
g*
e*
d e c a
a*
b
c a b a* d f g f* e
c* a* b* a d* f* g* f e*
d*f* f
g*
e*
d e c a
a*
b
c*e*
d*
d e c a
Y
X
X
c a b a*
c* a* b* a d*f* f
g*
e*
d e c
c*
d* e* c*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c
c*
c a b a* d e c
Read
UCNC2016, Annunziata Lopiccolo – Newcastle University
2 signals
on stack
X
Y
d e c a
a*
b
d* e* c*
c a b a* d f g f* e
c* a* b* a d* f* g* f e*
Popping signals
DNA Molecular Stack Recorder
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*d* e* c*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
e f*
g
fd a*
b
ac
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c a b a*
c* a* b* a
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c*e*
d*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*d* e* c*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
e f*
g
fd a*
b
ac
c a b a* d e c a
b
c a b a*
d*f* f
g*
e* c* a* b* a d*f* f
g*
e* a*
d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c*e*
d*
d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
d* e* c*
d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
e f*
g
fd a*
b
ac
d*f* f
g*
e*
d e c a
a*
b
c a b a* d f g f* e
c* a* b* a d* f* g* f e*
d*f* f
g*
e*
d e c a
a*
b
c*e*
d*
d e c a
Y
X
X
Read binds by toehold,
Branch migration follows
UCNC2016, Annunziata Lopiccolo – Newcastle University
2 signals
on stack
X
Y
d e c a
a*
b
d* e* c*
c a b a* d f g f* e
c* a* b* a d* f* g* f e*
Popping signals
DNA Molecular Stack Recorder
a b a*
* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
a b a*
* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c*e*
d*
a b a*
* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
d* e* c*
a b a*
* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
e f*
g
fd a*
b
ac
a b a*
* a* b* a d*f* f
g*
e*
d e c a
a*
b
c a b a* d f g f* e
c* a* b* a d* f* g* f e*
a b a*
* a* b* a d*f* f
g*
e*
d e c a
a*
b
c*e*
d*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
e f*
g
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c a b a*
c* a* b* a
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c*e*
d*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*d* e* c*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
e f*
g
fd a*
b
ac
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c a b a*
c* a* b* a
c a b a* d e c a
d*f* f
g*
e* a*
d*f* f
g*
e*
d e c a
a*
b
c*e*
d*
d*f* f
g*
e*
d e c a
a*
b
d* e* c*
d*f* f
g*
e*
e f*
g
fd a*
b
ac
c a b a* d f g f* e
c* a* b* a d* f* g* f e*
d e c a
Y
X
X
Strand displacement
Signal Y released
UCNC2016, Annunziata Lopiccolo – Newcastle University
1 signal
on stack
X
Y
d e c a
a*
b
d* e* c*
c a b a* d f g f* e
c* a* b* a d* f* g* f e*
Popping signals
DNA Molecular Stack Recorder
* f
g*
e*
e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
* f
g*
e*
e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c*e*
d*
* f
g*
e*
e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
d* e* c*
* f
g*
e*
e c a b a*
c* a* b* a d*f* f
g*
e*
e f*
g
fd a*
b
ac
* f
g*
e*
e c a
a*
b
c a b a* d f g f* e
c* a* b* a d* f* g* f e*
* f
g*
e*
e c a
a*
b
c*e*
d*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
e f*
g
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c a b a*
c* a* b* a
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c*e*
d*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*d* e* c*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
e f*
g
fd a*
b
ac
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c a b a*
c* a* b* a
c a b a* d e c a
*
e* a*
*
e*
e c a
a*
b
c*e*
d*
*
e*
d e c a
a*
b
d* e* c*
*
e*
e f*
g
fd a*
b
ac
c a b a* d f g f* e
c* a* b* a d* f* g* f e*
d e c a
Y
X
X
f*
d* e* c*
c a b a*
c* a* b* a d*f*
d
c a b a*
c* a* b* a d*f*
d
ca a*
b
df f*
g
e
c a b a*
c* a* b* a d*f*
d
c a b a*
c* a* b* a d*f*
d
d* e* c*
c a b a*
c* a* b* a d*f*
d
c a b a* d
Pop
UCNC2016, Annunziata Lopiccolo – Newcastle University
1 signal
on stack
X
d e c a
a*
b
d* e* c*
c a b a* d f g f* e
c* a* b* a d* f* g* f e*
Popping signals
DNA Molecular Stack Recorder
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c a b a*
c* a* b* a
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c*e*
d*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*d* e* c*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
e f*
g
fd a*
b
ac
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c a b a*
c* a* b* a
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c*e*
d*
c a b a* d e c a
* f
g*
e*
e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
* f
g*
e*
e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c*e*
d*
* f
g*
e*
e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
d* e* c*
* f
g*
e*
e c a b a*
c* a* b* a d*f* f
g*
e*
e f*
g
fd a*
b
ac
* f
g*
e*
e c a
a*
b
c a b a* d f g f* e
c* a* b* a d* f* g* f e*
* f
g*
e*
e c a
a*
b
c*e*
d*
*
e* a*
*
e*
e c a
a*
b
c*e*
d*
*
e*
d e c a
a*
b
d* e* c*
*
e*
e f*
g
fd a*
b
ac
c a b a* d f g f* e
c* a* b* a d* f* g* f e*
d e c a
Y
X
X
Pop binds by toehold,
Branch migration follows
UCNC2016, Annunziata Lopiccolo – Newcastle University
1 signal
on stack
X
d e c a
a*
b
d* e* c*
c a b a* d f g f* e
c* a* b* a d* f* g* f e*
Popping signals
DNA Molecular Stack Recorder
* f
g*
e*
e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
* f
g*
e*
e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c*e*
d*
* f
g*
e*
e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
d* e* c*
* f
g*
e*
e c a b a*
c* a* b* a d*f* f
g*
e*
e f*
g
fd a*
b
ac
* f
g*
e*
e c a
a*
b
c a b a* d f g f* e
c* a* b* a d* f* g* f e*
* f
g*
e*
e c a
a*
b
c*e*
d*
*
e* a*
*
e*
e c a
a*
b
c*e*
d*
*
e*
d e c a
a*
b
d* e* c*
*
e*
e f*
g
fd a*
b
ac
c a b a* d f g f* e
c* a* b* a d* f* g* f e*
d e c a
Y
X
X
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c*e*
d*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*d* e* c*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
e f*
g
fd a*
b
ac
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c a b a*
c* a* b* a
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c*e*
d*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*d* e* c*
ac
Hairpin
reforms
** f
g*
e*
e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
** f
g*
e*
e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c*e*
d*
** f
g*
e*
e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
d* e* c*
** f
g*
e*
e c a b a*
c* a* b* a d*f* f
g*
e*
e f*
g
fd a*
b
ac
** f
g*
e*
e c a
a*
b
c a b a* d f g f* e
c* a* b* a d* f* g* f e*
** f
g*
e*
e c a
a*
b
c*e*
d*
Pop-Push
double strand
UCNC2016, Annunziata Lopiccolo – Newcastle University
1 signal
on stack
X
d e c a
a*
b
d* e* c*
c a b a* d f g f* e
c* a* b* a d* f* g* f e*
Popping signals
DNA Molecular Stack Recorder
* f
g*
e*
e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
* f
g*
e*
e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c*e*
d*
* f
g*
e*
e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
d* e* c*
* f
g*
e*
e c a b a*
c* a* b* a d*f* f
g*
e*
e f*
g
fd a*
b
ac
* f
g*
e*
e c a
a*
b
c a b a* d f g f* e
c* a* b* a d* f* g* f e*
* f
g*
e*
e c a
a*
b
c*e*
d*
*
e* a*
*
e*
e c a
a*
b
c*e*
d*
*
e*
d e c a
a*
b
d* e* c*
*
e*
e f*
g
fd a*
b
ac
c a b a* d f g f* e
c* a* b* a d* f* g* f e*
d e c a
Y
X
X
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c*e*
d*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*d* e* c*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
e f*
g
fd a*
b
ac
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c a b a*
c* a* b* a
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c*e*
d*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*d* e* c*
ac
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c
c*
d* e* c*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c
c*
c a b a* d e c a b a* d e c
Read
UCNC2016, Annunziata Lopiccolo – Newcastle University
1 signal
on stack
X
d e c a
a*
b
d* e* c*
c a b a* d f g f* e
c* a* b* a d* f* g* f e*
d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c*e*
d*
d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
d* e* c*
d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
e f*
g
fd a*
b
ac
d*f* f
g*
e*
d e c a
a*
b
c a b a* d f g f* e
c* a* b* a d* f* g* f e*
Popping signals
DNA Molecular Stack Recorderc a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*d* e* c*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
e f*
g
fd a*
b
ac
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c a b a*
c* a* b* a
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c*e*
d*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*d* e* c*
c a b a*
c* a* b* a d*f* f
g*
e*
e f*
g
fd a*
b
ac
* f
g*
e*
e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
* f
g*
e*
e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c*e*
d*
* f
g*
e*
e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
d* e* c*
* f
g*
e*
e c a b a*
c* a* b* a d*f* f
g*
e*
e f*
g
fd a*
b
ac
* f
g*
e*
e c a
a*
b
c a b a* d f g f* e
c* a* b* a d* f* g* f e*
* f
g*
e*
e c a
a*
b
c*e*
d*
*
e* a*
*
e*
e c a
a*
b
c*e*
d*
*
e*
d e c a
a*
b
d* e* c*
*
e*
e f*
g
fd a*
b
ac
c a b a* d f g f* e
c* a* b* a d* f* g* f e*
d e c a
Y
X
X
Read binds by toehold,
Branch migration follows
UCNC2016, Annunziata Lopiccolo – Newcastle University
1 signal
on stack
X
d e c a
a*
b
d* e* c*
c a b a* d f g f* e
c* a* b* a d* f* g* f e*
d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c*e*
d*
d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
d* e* c*
d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
e f*
g
fd a*
b
ac
d*f* f
g*
e*
d e c a
a*
b
c a b a* d f g f* e
c* a* b* a d* f* g* f e*
Popping signals
DNA Molecular Stack Recorder
b a*
b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
b a*
b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c*e*
d*
b a*
b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
d* e* c*
b a*
b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
e f*
g
fd a*
b
ac
b a*
b* a d*f* f
g*
e*
d e c a
a*
b
c a b a* d f g f* e
c* a* b* a d* f* g* f e*
* f
g*
e*
e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
* f
g*
e*
e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c*e*
d*
* f
g*
e*
e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
d* e* c*
* f
g*
e*
e c a b a*
c* a* b* a d*f* f
g*
e*
e f*
g
fd a*
b
ac
* f
g*
e*
e c a
a*
b
c a b a* d f g f* e
c* a* b* a d* f* g* f e*
* f
g*
e*
e c a
a*
b
c*e*
d*
*
e* a*
*
e*
e c a
a*
b
c*e*
d*
*
e*
d e c a
a*
b
d* e* c*
*
e*
e f*
g
fd a*
b
ac
c a b a* d f g f* e
c* a* b* a d* f* g* f e*
d e c a
Y
X
X
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
e f*
g
fd a*
b
ac
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c a b a*
c* a* b* a
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c*e*
d*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
ad* e* c*
c a b a*
c* a* b* a d*f* f
g*
e*
e f*
g
fd a*
b
ac
c a
a*
b
c a b a*
c* a* b* a
Strand displacement Signal X released
UCNC2016, Annunziata Lopiccolo – Newcastle University
stack
empty
X
d e c a
a*
b
d* e* c*
c a b a* d f g f* e
c* a* b* a d* f* g* f e*
d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c*e*
d*
d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
d* e* c*
d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
e f*
g
fd a*
b
ac
d*f* f
g*
e*
d e c a
a*
b
c a b a* d f g f* e
c* a* b* a d* f* g* f e*
Popping signals
Naive Chemical View
S + P + Xb'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'm
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'm
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'm
Real Well-Mixed Chemistry
S + P + Xb'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'm
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'm
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'm
— partly formed complexes
— partially bound complexes
— unintended side reactions
— DNA complexes have finite diffusion
and reaction rates
Real Well-Mixed Chemistry
S + P + Xb'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'm
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'm
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'm
— partly formed complexes
— partially bound complexes
— unintended side reactions
— DNA complexes have finite diffusion
and reaction rates
+ P b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'm
+ Xb'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'm
S
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'm
Real Well-Mixed Chemistry
Required: Rule-based stochastic
model to rigorously capture the
reactions happening in-vitro
To know if stack chemistry
is operating correctly:
core
reactions
Which Rules? — Reaction Space
all reactions
with full domains
bound
all possible reactions,
including partially bound
complexes
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'm
core
reactions
all reactions
with full domains
bound
all possible reactions,
including partially bound
complexes
Microsoft DSD 2.0
Which Rules? — Reaction Space
Multi-strand
thermodynamic
prediction software
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'm
core
reactions
Which Rules? — Reaction Space
pop
push
read
X…stack
stack…push
stack…X…stack
stack…Xpush
stack…push
stack…push
Core Reactions: 1-Step Rules
stack…
…stack
start push
X
readRate parameters
pop
start
…stack
push
X…stack
+
+
+
(1)
(2)
(3)
(4) +
…stack + X
…stack +
…stack + poppush
Recording
Popping
kf(h)
⌦
kr
kf(h+b)
⌦
kr
kf(h+b)
⌦
kr
!
!
kf(h) = 106
M 1
s 1
kf(h+b) = 105
M 1
s 1
core
reactions
(5)
kf(s1) = 105
M 1
s 1
kf(s2) = 104
M 1
s 1
kf(s2)
kf(s1)
kr = 0 (No ring complexes allowed)
Simulation Results
Recording signals
t = 5 min t = 1 hour
S
200nM
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'm
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'mP
200nM
c a
a*
b
c*a* a
b*
d*f* f
g*
e*
ca a*
b
c*a* a
b*
d*f* f
g*
e*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c*a* a
b*
d*f* f
g*
e*
c a b a*
c* a* b* a d*f* f
g*
e*
d e ca a*
b
c*a* a
b*
d*f* f
g*
e*
c a b a*
c* a* b* a d*f* f e*
d e c a b a*
c* a* b* a d*f* f e*
sp
sp
p
Simulation Results
Recording signals
S
200nM
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'm
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'mP
200nM
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'm
X
200nM
c*a* a
b*
d*f* f
g*
e*
ca a*
b
c*a* a
b*
d*f* f
g*
e*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c*a* a
b*
d*f* f
g*
e*
c a b a*
c* a* b* a d*f* f
g*
e*
d e ca a*
b
c*a* a
b*
d*f* f
g*
e*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c a b a*
c* a* b* a d*f* f e*
d e c a b a*
c* a* b* a d*f* f e*
d e c a
a*
b
spx
spxpx
spx
t = 5 min t = 1 hour
Simulation Results
Recording signals
S
200nM
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'm
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'mP
200nM
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'm
X
200nM
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'mP
200nM
spx spxpxp
t = 5 min t = 1 hour
spxp
p
sp
spxp
d e c a
a*
b
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c*a* a
b*
d*f* f
g*
e*
c a b a*
c* a* b* a d*f* f
g*
e*
d e ca a*
b
c*a* a
b*
d*f* f
g*
e*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c*a* a
b*
d*f* f
g*
e*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e ca a*
b
c*a* a
b*
d*f* f
g*
e*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
Simulation Results
Recording signals
S
200nM
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'm
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'mP
200nM
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'm
X
200nM
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'mP
200nM
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'm
X
200nM
t = 5 min t = 1 hour
spx
spxpx
spxpxpx
spxpxpxpx
spxpx
spxpxpx
g*
c*a* a
b*
d*f* f
g*
e*
c a b a*
c* a* b* a d*f* f
g*
e*
d e ca a*
b
c*a* a
b*
d*f* f
g*
e*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c*a* a
b*
d*f* f
g*
e*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e ca a*
b
c*a* a
b*
d*f* f
g*
e*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
Simulation Results
Recording signals
S
200nM
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'm
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'mP
200nM
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'm
X
200nM
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'mP
200nM
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'm
X
200nM
P
200nM
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'm
t = 5 min t = 1 hour
g* g*
e c a
a*
b
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
* a
b*
d*f* f
g*
e*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e ca a*
b
c*a* a
b*
d*f* f
g*
e*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
e c a
a*
b
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
p
spxp
spxpx
spxpxp
spxpxpx
spxpxpxp
spxpxpxpxp
spxpxp
spxpxpxp
Simulation Results
Recording signals
S
200nM
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'm
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'mP
200nM
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'm
X
200nM
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'mP
200nM
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'm
X
200nM
P
200nM
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'mX
200nM
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'm
t = 5 min t = 1 hour
g* g*
d*f* f
g*
e*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e ca a*
b
c*a* a
b*
d*f* f
g*
e*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
e c a
a*
b
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
spxpxpx
spxpx spxpxpxpx
spxpxpxpxpx
spxpxpxpxpxpx
spxpxpx
spxpxpxpxspxpx
Simulation Results
R
200nM
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'm
Popping signals
t = 5 min t = 1 hour
[xr]
spxp
spxpx
spxpxp spxpxp
spxpxpx
spxpxpxp
spxpxpxpx
spxpxpxpxp
[xr]
spxp spxpxpxp
g* g*
e c a
a*
b
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
* a
b*
d*f* f
g*
e*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e ca a*
b
c*a* a
b*
d*f* f
g*
e*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
e c a
a*
b
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
Simulation Results
R
200nM
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'm
Q
200nM
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'm
Popping signals
t = 5 min t = 1 hour
g*
c*a* a
b*
d*f* f
g*
e*
c a b a*
c* a* b* a d*f* f
g*
e*
d e ca a*
b
c*a* a
b*
d*f* f
g*
e*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c*a* a
b*
d*f* f
g*
e*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e ca a*
b
c*a* a
b*
d*f* f
g*
e*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
q
[xr]
spx
spxp
spxpx
spxpxp
spxpxpx
spxpxpxp
spxpxpxpx
spxpxpxpxp
[xr]
[pq]
[pq]
spxpx
spxpxp
spxpxpx
Simulation Results
R
200nM
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'm
Q
200nM
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'm
R
200nM
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'm
Popping signals
t = 5 min t = 1 hour
d e c a
a*
b
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c*a* a
b*
d*f* f
g*
e*
c a b a*
c* a* b* a d*f* f
g*
e*
d e ca a*
b
c*a* a
b*
d*f* f
g*
e*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c*a* a
b*
d*f* f
g*
e*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e ca a*
b
c*a* a
b*
d*f* f
g*
e*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
r
q
sp
[xr]
[pq]
spxp
spxpx
spxpxp
spxpxpxp
spxpxpxpxp
sp
[xr]
[pq]
spxp
spxpxp
Simulation Results
R
200nM
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'm
Q
200nM
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'm
R
200nM
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'm
Q
200nM
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'm
Popping signals
t = 5 min t = 1 hour
[xr]
[pq]
[xr]
[pq]
c*a* a
b*
d*f* f
g*
e*
ca a*
b
c*a* a
b*
d*f* f
g*
e*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c*a* a
b*
d*f* f
g*
e*
c a b a*
c* a* b* a d*f* f
g*
e*
d e ca a*
b
c*a* a
b*
d*f* f
g*
e*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c a b a*
c* a* b* a d*f* f e*
d e c a b a*
c* a* b* a d*f* f e*
d e c a
a*
b
spx
spx
spxp
spxpx
spxpxp
spxpxpx
spxpxpxp
spxpxpxpxp spxp
spxpx
Simulation Results
R
200nM
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'm
Q
200nM
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'm
R
200nM
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'm
Q
200nM
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'm
R
200nM
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'm
Popping signals
t = 5 min t = 1 hour
c a
a*
b
c*a* a
b*
d*f* f
g*
e*
ca a*
b
c*a* a
b*
d*f* f
g*
e*
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c a b a*
c* a* b* a d*f* f
g*
e*
d e c a
a*
b
c*a* a
b*
d*f* f
g*
e*
c a b a*
c* a* b* a d*f* f
g*
e*
d e ca a*
b
c*a* a
b*
d*f* f
g*
e*
c a b a*
c* a* b* a d*f* f e*
d e c a b a*
c* a* b* a d*f* f e*
[xr]
[pq]
sp
[xr]
[pq]
spxp spxpxp
spxpxpxp
sp spxp
Simulation Results
R
200nM
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'm
Q
200nM
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'm
R
200nM
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'm
Q
200nM
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'm
R
200nM
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'm
Q
200nM
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'm
Popping signals
t = 5 min t = 1 hour
g*
ca a*df f* e
c a b a*
c* a* b* a d*f* f
g*
e*
e f*
g
fd a*
b
ac
c a
a*
b
s
q
sp
[xr]
[pq]
[xr]
[pq]
s
q
sp spxp spxpxp
spxpxpxp
Experimental Results
●But results are hard to interpret because of the superposition of stacks.
●Concentration of read and pop needs to be controlled.
1 signal 2 signals 3 signals 1 signal2 signals
SPX
SPXP
SPXPX
SPXPXP
SPXPXPX
P RX PQ
Reading and Popping
UCNC2016, Annunziata Lopiccolo – Newcastle University
Recording Popping
Selected bioanalyzer results qualitatively show same pattern as simulations
PAGE gel results still pose some questions…!
P
P P P P
X X
X X
XP
S S
P P P
S S S
S dimer
S dimer
S
SP SP SP SP SP
SPP?
SSP?
S
S dimer
P
SP
SSP?
X
SPP?
XP
P+X S+P+X+P S+PS+X+P+P
Simulation Results
Brick order matters!
t = 1 hour t = 1 hour
S
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'm
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'm
P
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'm
X
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'm
P
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'm
X
spxpx
spxpxpx
S
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'm
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'm
X
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'm
X
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'm
P
b'
aa'
d'c'
g'
ff'
e'c a
a'
b
c a
a'
b
d e
hh'
lk
x
d' c'e'
start push write-X
read pop
b
a'a
dc
g
f'f
e
report
x'm
P
In order Out of order
spxpx
spxpxpx
spx
s
spxpxpxpx
spxpxpxpxpx
spxpxpxpxpxpx
spxpxpxpxpxpxpx
Challenges Establishing In-Silico/In-Vitro Equivalence
CHEMISTRY
MODEL
spxpxpx
spxpxpx
EXPERIMENT
“a qualitative
agreement exists”
“pure” output:
all species and
concentrations known
“proxy” output:
chemistry state encoded
by indirect variables
y scale: nt
y scale: migration time
Challenges Establishing In-Silico/In-Vitro Equivalence
CHEMISTRY
MODEL
DETECTOR

MODEL
spxpxpx
spxpxpx
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y scale: nt
y scale: migration time
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DNA data-structure

  • 1. A DNA Data Structure Ben Shirt-Ediss & Natalio Krasnogor
  • 2. The Dr. Jekyll Side of DNA Source: http://compbio.pbworks.com
  • 3. Mr. Hide: DNA as a Dynamic Polymerdization Reactions e of DNA hybridization reactions is the Watson-Crick DNA hybridization between two A strands as discussed in Section 1.2.2. Two ssDNA strands can attach to each other. o detach from one another, if the temperature is greater than the melting temperature 6). The melting temperature of a dsDNA is defined as the temperature at which 50% nverted to single stranded form. Figure 6: DNA Denaturation Renaturation [60] Hybridisation Toehold-Mediated Strand Displacement Toehold Exchange trol of DNA Strand Displacement Kinetics A R T I C L E Several subsequent works used Yurke’s basic reaction sequence (a hybridization step followed by strand displacement to reverse the effect of the initial hybridization) for controlling complex nanos- cale structures. Simmel and Yurke34 demonstrated a nanoactuator related to Yurke’s original tweezer design. Addition of a first input strand pushed the two arms of the nanoactuator apart; addition of a second input strand set them free. In further work they built a device that could be switched between three distinct states using two pairs of fuel strands35 . Tian and Mao36 built a device consisting of two DNA complexes reminiscent of interlocking gears that could be repeatedly cycled through three different states. Reconfiguring self-assembled structures. Strand displacement can be combined with structural self-assembly to enable dynamic reconfiguration of larger DNA nanostructures post-assembly, and can be used to induce ch ple of this was describe toehold-mediated cyclin rotary DNA device. Th states corresponding to JX2 (Fig. 1b). They also structure large enough t scope and demonstrated tural motif relative to th Their device was based nanomachine30 that resp than DNA inputs. Chakraborty et al.38 l that could be switched be Seeman39 demonstrated 2* 3* 3 1 2 22 2 3 1 5' 3' 3' CCCTCATTCAATACCCTACG AGAGGTA c a b 32 Input A Complex X 2. Domain 2 undergoes branch migration 3. Strand displacement completes 1. Toehold domains initiate binding Complex Y Output B (toehold) (toehold) GGGAGTAAGTTATGGGATGC 3*2* 2 1 2* 3* 3 1 2 2 3*2* 2* 3* 2 3 21 2* 3* 2 CCACATACATCATATT 1 ––– Source: Zhang & Winfree. 2011. Nat Chem. Source: Zhang & Winfree. 2009
  • 4. DNA: Dynamic and also Well Understood Predictable tertiary structure Can use methods in Mol. Bio to quantify experimental success Enzymatic reactions cutting/joining DNA well characterised Hybridisation, branch migration and strand displacement reactions well understood PAGE, Fret, PCR, AFM… xDNA model (left), with equal has di↵erentiated major and x with one base pair displayed. III. INTRODUCING DIFFERENT WID AND MINOR DNA GROOVES B-DNA in the original oxDNA m widths, while in reality DNA has a lar a smaller minor groove. Having realisti and minor grooves is equivalent to havi tioned backbone sites in the model, an the physical properties of many DNA in DNA origami, antiparallel double crossovers, for which the position of t shown to be crucial for origami structur is anisotropic duplex bending: the dup bend more easily into the major groove the groove widths are unequal. The oxDNA nucleotide is compos sites: the hydrogen-bonding, stacking, a introduce di↵erent groove widths by ch oxDNA
  • 5. DNA Computing ime study of the dynamics of a single mol- ule and the chemical and biochemical re- tions that such a molecule may undergo in lution. REFERENCES AND NOTES . W. E. Moerner, Science 265, 46 (1994), and refer- ences therein. . M. Orrit, J. Bernard, R. I. Personov, J. Phys. Chem. 97,10256(1993). . F. GOttler, T. Irngartinger, T. Plakhotnik, A. Renn, U. P. Wild, Chem. Phys. Lett. 217, 393 (1994). . M. Ishikawa, K. Hirano, T. Hayakawa, S. Hosoi, S. Brenner, Jpn. J. Appl. Phys. 33,1571 (1994). . E. Betzig and R. J. Chichester, Science 262, 1422 (1993). . J. K. Trautman, J. J. Macklin, L. E. Brus, E. Betzig, Nature 369, 40 (1994). . D. C. Nguyen, R. A. Keller, J. H. Jett, J. C. Martin, Anal. Chem. 59, 2158 (1987). . K. Peck, L. Stryer, A. N. Glazer, R. A. Mathies, Proc. Natl. Acad. Sci. U.S.A. 86, 4087 (1989). . W. B. Whitten, L. M. Ramsey, S. A. Arnold, B. V. Bronk, Anal. Chem. 63,1027 (1991); K. C. Ng, W. B. Whitten, S. A. Arnold, L. M. Ramsey, ibid. 64, 2914 (1992). . M. Eigen and R. Rigler, Proc. Natl. Acad. Sci. U.S.A. 91, 5740 (1994). . In fluorescence correlation spectroscopy, the inten- sity recorded at time t is multiplied by that recorded at t + At and the product is integrated over a finite period of time; see D. E. Koppel, Phys. Rev. A 10, 1938 (1974). . T. T. Perkins, D. E. Smith, S. Chu, Science 264, 819 (1994); T. T. Perkins, S. R. Quake, D. E. Smith, S. Chu, ibid., p. 822. . S. B. Smith, L. Finzi, C. Bustamante, ibid. 258,1122 (1992); C. Bustamante, Annu. Rev. Biophys. Bio- phys. Chem. 20, 415 (1991). . M. Washizu and 0. Kurosawa, IEEE Trans. Ind. Appl. 26,1165 (1990). . S. B. Smith, P. K. Aldridge, J. B. Callis, Science 243, 203 (1989). . N. J. Rampino and A. Chrambach, Anal. Biochem. 194, 278 (1991). . K. Morikawa and M. Yanagida, J. Biochem. 89, 693 (1981). . I. Auzanneau, C. Barreau, L. Salome, C. R. Acad. Sci. Paris 316, 459 (1993). . H. Kabata et al., Science 262, 1561 (1993). . D. A. Schafer, J. Gelles, M. P. Sheetz, R. Landick, Nature 352, 444 (1991). . Laser excitation at 488.0 and 514.5 nm was provided by an argon ion laser (Lexel Lasers, Fremont, CA). The laser beam entered the microscope through a back port and was directed to an oil-immersion objective (x100, NA = 1.3, Nikon Instrument Group, Melville, NY) by a dichroic beamsplitter (505DRLP02 or 22. M. B. Schneider and W. W. Webb, Appl. Opt. 20, 1382 (1981). 23. R. Rigler, U. Mets, J. Widengren, P. Kask, Eur. Bio- phys. J. 22,169 (1993). 24. W. Feller, An Introduction to Probability Theory and its Applications (Wiley, New York, ed. 3, 1968). 25. S. A. Soper, H. L. Nutter, R. A. Keller, L. M. Davis, E. B. Shera, Photochem. Photobiol. 57, 972 (1993). 34. W. P. Ambrose, P. M. Goodwin, J. C. Martin, R. A. Keller, ibid., p. 364. 35. S.N. acknowledges the Whitaker Foundation for a young investigator award. D.T.C. is a Beckman Cell Science Scholar of Stanford University. This work was supported by Beckman Instruments, Inc. 18 July 1994; accepted 19 September 1994 Molecular Computation of Solutions to Combinatorial Problems Leonard M. Adleman The tools of molecular biology were used to solve an instance of the directed Hamiltonian path problem. A small graph was encoded in molecules of DNA, and the "operations" of the computation were performed with standard protocols and enzymes. This experiment demonstrates the feasibility of carrying out computations at the molecular level. In 1959, Richard Feynman gave a visionary talk describing the possibility of building computers that were "sub-microscopic" (1). Despite remarkable progress in computer miniaturization, this goal has yet to be achieved. Here, the possibility of comput- ing directly with molecules is explored. A directed graph G with designated ver- tices vn and vout is said to have a Hamilto- nian path (2) if and only if there exists a sequence of compatible "one-way" edges el, e2, ... ., e, (that is, a path) that begins at in, ends at v., and enters every other vertex exactly once. Figure 1 shows a graph that for vn = 0 and v01u = 6 has a Hamiltonian path, given by the edges 0-*1, 1->2, 2->3, 3---4, 4->5, 5->6. If the edge 2->3 were removed from the graph, then the result- ing graph with the same designated verti- ces would not have a Hamiltonian path. Similarly, if the designated vertices were changed to vin = 3 and vout = 5 there Department of Computer Science and Institute for Molec- ular Medicine and Technology, University of Southern Cal- ifornia, 941 West 37th Place, Los Angeles, CA 90089, USA. SCIENCE * VOL. 266 * 11 NOVEMBER 1994 would be no Hamiltonian path (because, for example, there are no edges entering vertex 0). There are well-known algorithms for de- ciding whether an arbitrary directed graph with designated vertices has a Hamiltonian path or not. However, all known algorithms for this problem have exponential worst-case complexity, and hence there are instances of modest size for which these algorithms re- quire an impractical amount of computer time to render a decision. Because the direct- ed Hamiltonian path problem has been proven to be NP-complete, it seems likely that no efficient (that is, polynomial time) algorithm exists for solving it (2, 3). The following (nondeterministic) algo- rithm solves the directed Hamiltonian path problem: Step 1: Generate random paths through the graph. Step 2: Keep only those paths that begin with vin and end with v,,f. Step 3: If the graph has n vertices, then keep only those paths that enter exactly n vertices. Step 4: Keep only those paths that enter all of 1021 onFebruary25,2016DownloadedfromonFebruary25,2016DownloadedfromonFebruary25,2016DownloadedfromonFebruary25,2016Downloadedfrom Science 1994 02 TATCGG&TCGGTATATCCGA 0 GCTATTCGAQCTTAAAGCTA 04 GGCTAGGTACCAGCATGT 02-3 GTATATCCQGACTATTCQG 03-4 CTTA&AGCTAQGCTAQGTAC 03 CG&TA&GCTCGA.&TTTCG&T 0,), Fig. 1. Directed graph. When vi, = 0 and v,,t = 6, a unique Hamiltonian path exists: 0-s1, 1 -s2, 2-s3, 3->4, 4-s5, 5-s6. 03,4 start stop A unique hamiltonian path exists: 0>1,1>2,2>3,3>4,4>5,5>6 C -150 -100 -50 1 2 3 4 5 6 -150 -100 -50 1 2 3 4 5 6 7 -150 -100 -50 1 2 3 4 5 6 7 illonis ---- Formation of DNA molecules encode possible paths PCR & cut gel to select paths starting at 0, ending at 6 DNA paths entering all vertices found by 6 washings with magnetic beads 7 days of lab work
  • 6. DNA Computing ime study of the dynamics of a single mol- ule and the chemical and biochemical re- tions that such a molecule may undergo in lution. REFERENCES AND NOTES . W. E. Moerner, Science 265, 46 (1994), and refer- ences therein. . M. Orrit, J. Bernard, R. I. Personov, J. Phys. Chem. 97,10256(1993). . F. GOttler, T. Irngartinger, T. Plakhotnik, A. Renn, U. P. Wild, Chem. Phys. Lett. 217, 393 (1994). . M. Ishikawa, K. Hirano, T. Hayakawa, S. Hosoi, S. Brenner, Jpn. J. Appl. Phys. 33,1571 (1994). . E. Betzig and R. J. Chichester, Science 262, 1422 (1993). . J. K. Trautman, J. J. Macklin, L. E. Brus, E. Betzig, Nature 369, 40 (1994). . D. C. Nguyen, R. A. Keller, J. H. Jett, J. C. Martin, Anal. Chem. 59, 2158 (1987). . K. Peck, L. Stryer, A. N. Glazer, R. A. Mathies, Proc. Natl. Acad. Sci. U.S.A. 86, 4087 (1989). . W. B. Whitten, L. M. Ramsey, S. A. Arnold, B. V. Bronk, Anal. Chem. 63,1027 (1991); K. C. Ng, W. B. Whitten, S. A. Arnold, L. M. Ramsey, ibid. 64, 2914 (1992). . M. Eigen and R. Rigler, Proc. Natl. Acad. Sci. U.S.A. 91, 5740 (1994). . In fluorescence correlation spectroscopy, the inten- sity recorded at time t is multiplied by that recorded at t + At and the product is integrated over a finite period of time; see D. E. Koppel, Phys. Rev. A 10, 1938 (1974). . T. T. Perkins, D. E. Smith, S. Chu, Science 264, 819 (1994); T. T. Perkins, S. R. Quake, D. E. Smith, S. Chu, ibid., p. 822. . S. B. Smith, L. Finzi, C. Bustamante, ibid. 258,1122 (1992); C. Bustamante, Annu. Rev. Biophys. Bio- phys. Chem. 20, 415 (1991). . M. Washizu and 0. Kurosawa, IEEE Trans. Ind. Appl. 26,1165 (1990). . S. B. Smith, P. K. Aldridge, J. B. Callis, Science 243, 203 (1989). . N. J. Rampino and A. Chrambach, Anal. Biochem. 194, 278 (1991). . K. Morikawa and M. Yanagida, J. Biochem. 89, 693 (1981). . I. Auzanneau, C. Barreau, L. Salome, C. R. Acad. Sci. Paris 316, 459 (1993). . H. Kabata et al., Science 262, 1561 (1993). . D. A. Schafer, J. Gelles, M. P. Sheetz, R. Landick, Nature 352, 444 (1991). . Laser excitation at 488.0 and 514.5 nm was provided by an argon ion laser (Lexel Lasers, Fremont, CA). The laser beam entered the microscope through a back port and was directed to an oil-immersion objective (x100, NA = 1.3, Nikon Instrument Group, Melville, NY) by a dichroic beamsplitter (505DRLP02 or 22. M. B. Schneider and W. W. Webb, Appl. Opt. 20, 1382 (1981). 23. R. Rigler, U. Mets, J. Widengren, P. Kask, Eur. Bio- phys. J. 22,169 (1993). 24. W. Feller, An Introduction to Probability Theory and its Applications (Wiley, New York, ed. 3, 1968). 25. S. A. Soper, H. L. Nutter, R. A. Keller, L. M. Davis, E. B. Shera, Photochem. Photobiol. 57, 972 (1993). 34. W. P. Ambrose, P. M. Goodwin, J. C. Martin, R. A. Keller, ibid., p. 364. 35. S.N. acknowledges the Whitaker Foundation for a young investigator award. D.T.C. is a Beckman Cell Science Scholar of Stanford University. This work was supported by Beckman Instruments, Inc. 18 July 1994; accepted 19 September 1994 Molecular Computation of Solutions to Combinatorial Problems Leonard M. Adleman The tools of molecular biology were used to solve an instance of the directed Hamiltonian path problem. A small graph was encoded in molecules of DNA, and the "operations" of the computation were performed with standard protocols and enzymes. This experiment demonstrates the feasibility of carrying out computations at the molecular level. In 1959, Richard Feynman gave a visionary talk describing the possibility of building computers that were "sub-microscopic" (1). Despite remarkable progress in computer miniaturization, this goal has yet to be achieved. Here, the possibility of comput- ing directly with molecules is explored. A directed graph G with designated ver- tices vn and vout is said to have a Hamilto- nian path (2) if and only if there exists a sequence of compatible "one-way" edges el, e2, ... ., e, (that is, a path) that begins at in, ends at v., and enters every other vertex exactly once. Figure 1 shows a graph that for vn = 0 and v01u = 6 has a Hamiltonian path, given by the edges 0-*1, 1->2, 2->3, 3---4, 4->5, 5->6. If the edge 2->3 were removed from the graph, then the result- ing graph with the same designated verti- ces would not have a Hamiltonian path. Similarly, if the designated vertices were changed to vin = 3 and vout = 5 there Department of Computer Science and Institute for Molec- ular Medicine and Technology, University of Southern Cal- ifornia, 941 West 37th Place, Los Angeles, CA 90089, USA. SCIENCE * VOL. 266 * 11 NOVEMBER 1994 would be no Hamiltonian path (because, for example, there are no edges entering vertex 0). There are well-known algorithms for de- ciding whether an arbitrary directed graph with designated vertices has a Hamiltonian path or not. However, all known algorithms for this problem have exponential worst-case complexity, and hence there are instances of modest size for which these algorithms re- quire an impractical amount of computer time to render a decision. Because the direct- ed Hamiltonian path problem has been proven to be NP-complete, it seems likely that no efficient (that is, polynomial time) algorithm exists for solving it (2, 3). The following (nondeterministic) algo- rithm solves the directed Hamiltonian path problem: Step 1: Generate random paths through the graph. Step 2: Keep only those paths that begin with vin and end with v,,f. Step 3: If the graph has n vertices, then keep only those paths that enter exactly n vertices. Step 4: Keep only those paths that enter all of 1021 onFebruary25,2016DownloadedfromonFebruary25,2016DownloadedfromonFebruary25,2016DownloadedfromonFebruary25,2016Downloadedfrom Science 1994competing with silicon embedding control in molecular systems and cells
  • 7. DNA Computing ime study of the dynamics of a single mol- ule and the chemical and biochemical re- tions that such a molecule may undergo in lution. REFERENCES AND NOTES . W. E. Moerner, Science 265, 46 (1994), and refer- ences therein. . M. Orrit, J. Bernard, R. I. Personov, J. Phys. Chem. 97,10256(1993). . F. GOttler, T. Irngartinger, T. Plakhotnik, A. Renn, U. P. Wild, Chem. Phys. Lett. 217, 393 (1994). . M. Ishikawa, K. Hirano, T. Hayakawa, S. Hosoi, S. Brenner, Jpn. J. Appl. Phys. 33,1571 (1994). . E. Betzig and R. J. Chichester, Science 262, 1422 (1993). . J. K. Trautman, J. J. Macklin, L. E. Brus, E. Betzig, Nature 369, 40 (1994). . D. C. Nguyen, R. A. Keller, J. H. Jett, J. C. Martin, Anal. Chem. 59, 2158 (1987). . K. Peck, L. Stryer, A. N. Glazer, R. A. Mathies, Proc. Natl. Acad. Sci. U.S.A. 86, 4087 (1989). . W. B. Whitten, L. M. Ramsey, S. A. Arnold, B. V. Bronk, Anal. Chem. 63,1027 (1991); K. C. Ng, W. B. Whitten, S. A. Arnold, L. M. Ramsey, ibid. 64, 2914 (1992). . M. Eigen and R. Rigler, Proc. Natl. Acad. Sci. U.S.A. 91, 5740 (1994). . In fluorescence correlation spectroscopy, the inten- sity recorded at time t is multiplied by that recorded at t + At and the product is integrated over a finite period of time; see D. E. Koppel, Phys. Rev. A 10, 1938 (1974). . T. T. Perkins, D. E. Smith, S. Chu, Science 264, 819 (1994); T. T. Perkins, S. R. Quake, D. E. Smith, S. Chu, ibid., p. 822. . S. B. Smith, L. Finzi, C. Bustamante, ibid. 258,1122 (1992); C. Bustamante, Annu. Rev. Biophys. Bio- phys. Chem. 20, 415 (1991). . M. Washizu and 0. Kurosawa, IEEE Trans. Ind. Appl. 26,1165 (1990). . S. B. Smith, P. K. Aldridge, J. B. Callis, Science 243, 203 (1989). . N. J. Rampino and A. Chrambach, Anal. Biochem. 194, 278 (1991). . K. Morikawa and M. Yanagida, J. Biochem. 89, 693 (1981). . I. Auzanneau, C. Barreau, L. Salome, C. R. Acad. Sci. Paris 316, 459 (1993). . H. Kabata et al., Science 262, 1561 (1993). . D. A. Schafer, J. Gelles, M. P. Sheetz, R. Landick, Nature 352, 444 (1991). . Laser excitation at 488.0 and 514.5 nm was provided by an argon ion laser (Lexel Lasers, Fremont, CA). The laser beam entered the microscope through a back port and was directed to an oil-immersion objective (x100, NA = 1.3, Nikon Instrument Group, Melville, NY) by a dichroic beamsplitter (505DRLP02 or 22. M. B. Schneider and W. W. Webb, Appl. Opt. 20, 1382 (1981). 23. R. Rigler, U. Mets, J. Widengren, P. Kask, Eur. Bio- phys. J. 22,169 (1993). 24. W. Feller, An Introduction to Probability Theory and its Applications (Wiley, New York, ed. 3, 1968). 25. S. A. Soper, H. L. Nutter, R. A. Keller, L. M. Davis, E. B. Shera, Photochem. Photobiol. 57, 972 (1993). 34. W. P. Ambrose, P. M. Goodwin, J. C. Martin, R. A. Keller, ibid., p. 364. 35. S.N. acknowledges the Whitaker Foundation for a young investigator award. D.T.C. is a Beckman Cell Science Scholar of Stanford University. This work was supported by Beckman Instruments, Inc. 18 July 1994; accepted 19 September 1994 Molecular Computation of Solutions to Combinatorial Problems Leonard M. Adleman The tools of molecular biology were used to solve an instance of the directed Hamiltonian path problem. A small graph was encoded in molecules of DNA, and the "operations" of the computation were performed with standard protocols and enzymes. This experiment demonstrates the feasibility of carrying out computations at the molecular level. In 1959, Richard Feynman gave a visionary talk describing the possibility of building computers that were "sub-microscopic" (1). Despite remarkable progress in computer miniaturization, this goal has yet to be achieved. Here, the possibility of comput- ing directly with molecules is explored. A directed graph G with designated ver- tices vn and vout is said to have a Hamilto- nian path (2) if and only if there exists a sequence of compatible "one-way" edges el, e2, ... ., e, (that is, a path) that begins at in, ends at v., and enters every other vertex exactly once. Figure 1 shows a graph that for vn = 0 and v01u = 6 has a Hamiltonian path, given by the edges 0-*1, 1->2, 2->3, 3---4, 4->5, 5->6. If the edge 2->3 were removed from the graph, then the result- ing graph with the same designated verti- ces would not have a Hamiltonian path. Similarly, if the designated vertices were changed to vin = 3 and vout = 5 there Department of Computer Science and Institute for Molec- ular Medicine and Technology, University of Southern Cal- ifornia, 941 West 37th Place, Los Angeles, CA 90089, USA. SCIENCE * VOL. 266 * 11 NOVEMBER 1994 would be no Hamiltonian path (because, for example, there are no edges entering vertex 0). There are well-known algorithms for de- ciding whether an arbitrary directed graph with designated vertices has a Hamiltonian path or not. However, all known algorithms for this problem have exponential worst-case complexity, and hence there are instances of modest size for which these algorithms re- quire an impractical amount of computer time to render a decision. Because the direct- ed Hamiltonian path problem has been proven to be NP-complete, it seems likely that no efficient (that is, polynomial time) algorithm exists for solving it (2, 3). The following (nondeterministic) algo- rithm solves the directed Hamiltonian path problem: Step 1: Generate random paths through the graph. Step 2: Keep only those paths that begin with vin and end with v,,f. Step 3: If the graph has n vertices, then keep only those paths that enter exactly n vertices. Step 4: Keep only those paths that enter all of 1021 onFebruary25,2016DownloadedfromonFebruary25,2016DownloadedfromonFebruary25,2016DownloadedfromonFebruary25,2016Downloadedfrom Science 1994 AND/OR logic gates Source: Qian & Winfree 2011 saw gate with a few wires can create a catalytic cycle in which input transforms free fuel into free output without being consumed in the process (Fig. 1D and fig. S1, B and C). Initially, the output signal is bound to the right side of the gate; the input and fuel signals are free (in our analogy, the output is riding on the right side of the seesaw board; the input and fuel are wandering around). The input signal first releases the output signal and binds to the gate instead (the input jumps onto the left side of the board and makes the output jump off). The fuel signal then displaces the input signal by binding to the gate in the same way (the fuel pushes off the input). A catalytic cycle has been completed. In general, a free signal on one side of a seesaw gate can catalyze the exchange of signals on the other side, and this exchange will not hap- pen without the catalyst. These reactions are driv- en forward by the entropy of equilibration for the seesawing reactions. A small amount of free input can catalyze the release of a large amount of free output (fig. S2). Thresholding can be directly combined with a seesaw catalyst to support a digital abstraction— which is the basic principle underlying digital logic in electronics—by pushing the intrinsically analog signal toward either the ideal ON or OFF value. Fluorescence kinetics experiments (Fig. 1E) demonstrated the circuit in Fig. 1A connected to the reporter in Fig. 1B. The input-versus-output relationship (plotted in Fig. 1F) reveals a sharp threshold, ideal for signal restoration. A cascade of two seesaw gates can compute the logic function OR or AND. To explain this, we introduce two composable seesaw compo- nents for digital circuits. We first define the gross production of signal X as the total amount eventually released from the gate: 〈X〉 ¼ ∫ þ∞ 0 Xprod ðtÞdt ð1Þ Motivated by sequence design constraints (figs. S3 and S4), we then define two types of feedforward seesaw gates, each assuming an irreversible down- stream drain. The first type is called an amplifying gate. It has a threshold and fuel. If the gross production of its input is greater than the initial amount of threshold, the output will keep being released catalytically until it reaches the max- imum, which is the initial amount of bound arrives at the gate (negative number). A reporter that transforms a DNA signal into a fluorescence signal is represented by half a node with a zigzag arrow (Fig. 1B), with its initial relative concen- tration written similar to a threshold. Each signal is a single-stranded DNA mole- cule that has two recognition domains identify- ing the two gates it connects, one on either side of a central toehold domain. Each gate is asso- ciated with a gate base strand that has (the com- plement of ) one recognition domain flanked by two toehold domains. When a signal strand is bound to a gate, it forms a gate:signal complex with the gate’s base strand. At any given mo- ment (not counting the transient states during reactions shown in fig. S1C), a gate base strand always has a signal strand bound to one side, leaving the toehold on the other side uncovered. There are three basic reactions involved in a seesaw network (Fig. 1C and fig. S1C). The first one is seesawing: A free signal on one side of a gate can release a signal bound on the other side of the gate by toehold-mediated strand dis- placement. The process starts with the free signal strand (e.g., w2,5) hybridizing to the gate:signal complex (e.g., G5:5,6) at the uncovered toehold domain (e.g., T*) and then undergoing branch mi- gration through the recognition domain (e.g., S5). The previously bound signal will fall off when it is attached to the gate base strand only by the short toehold. The resulting gate:signal complex (e.g., G2,5:5) will have an uncovered toehold on the other side, and therefore the now-free signal (e.g., w5,6) can reverse the process symmetrically. The second reaction is thresholding: A thresh- old species associated with a gate and an imping- ing signal can react with the signal by means of a longer toehold (e.g., s2*T*), producing only inert waste species that have no exposed toehold. Thresholding is much faster than see- sawing because the toehold-mediated strand dis- placement rate grows exponentially with toehold length for short toeholds (7, 8). As a result, see- sawing effectively only happens when the input signal exceeds the threshold. The third reaction is reporting: A reporter species similar to a thresh- old, but modified with a fluorophore and quench- er pair, can absorb an impinging signal while generating a fluorescence signal. Unlike thresh- binds to the gate instead (the input jumps onto the left side of the board and makes the output jump off). The fuel signal then displaces the input signal by binding to the gate in the same way (the fuel pushes off the input). A catalytic cycle has been completed. In general, a free signal on one side of a seesaw gate can catalyze the exchange of signals on the other side, and this exchange will not hap- pen without the catalyst. These reactions are driv- en forward by the entropy of equilibration for the seesawing reactions. A small amount of free input can catalyze the release of a large amount of free output (fig. S2). Thresholding can be directly combined with a seesaw catalyst to support a digital abstraction— which is the basic principle underlying digital we introduce two composable seesaw comp nents for digital circuits. We first define th gross production of signal X as the total amou eventually released from the gate: 〈X〉 ¼ ∫ þ∞ 0 Xprod ðtÞdt ð Motivated by sequence design constraints (figs. S and S4), we then define two types of feedforwa seesaw gates, each assuming an irreversible dow stream drain. The first type is called an amplifyin gate. It has a threshold and fuel. If the gro production of its input is greater than the initi amount of threshold, the output will keep bein released catalytically until it reaches the ma imum, which is the initial amount of boun Fig. 2. Digital logic gates implemented with the seesaw DNA motif. (A) Abstract diagram of
  • 8. DNA Computing ime study of the dynamics of a single mol- ule and the chemical and biochemical re- tions that such a molecule may undergo in lution. REFERENCES AND NOTES . W. E. Moerner, Science 265, 46 (1994), and refer- ences therein. . M. Orrit, J. Bernard, R. I. Personov, J. Phys. Chem. 97,10256(1993). . F. GOttler, T. Irngartinger, T. Plakhotnik, A. Renn, U. P. Wild, Chem. Phys. Lett. 217, 393 (1994). . M. Ishikawa, K. Hirano, T. Hayakawa, S. Hosoi, S. Brenner, Jpn. J. Appl. Phys. 33,1571 (1994). . E. Betzig and R. J. Chichester, Science 262, 1422 (1993). . J. K. Trautman, J. J. Macklin, L. E. Brus, E. Betzig, Nature 369, 40 (1994). . D. C. Nguyen, R. A. Keller, J. H. Jett, J. C. Martin, Anal. Chem. 59, 2158 (1987). . K. Peck, L. Stryer, A. N. Glazer, R. A. Mathies, Proc. Natl. Acad. Sci. U.S.A. 86, 4087 (1989). . W. B. Whitten, L. M. Ramsey, S. A. Arnold, B. V. Bronk, Anal. Chem. 63,1027 (1991); K. C. Ng, W. B. Whitten, S. A. Arnold, L. M. Ramsey, ibid. 64, 2914 (1992). . M. Eigen and R. Rigler, Proc. Natl. Acad. Sci. U.S.A. 91, 5740 (1994). . In fluorescence correlation spectroscopy, the inten- sity recorded at time t is multiplied by that recorded at t + At and the product is integrated over a finite period of time; see D. E. Koppel, Phys. Rev. A 10, 1938 (1974). . T. T. Perkins, D. E. Smith, S. Chu, Science 264, 819 (1994); T. T. Perkins, S. R. Quake, D. E. Smith, S. Chu, ibid., p. 822. . S. B. Smith, L. Finzi, C. Bustamante, ibid. 258,1122 (1992); C. Bustamante, Annu. Rev. Biophys. Bio- phys. Chem. 20, 415 (1991). . M. Washizu and 0. Kurosawa, IEEE Trans. Ind. Appl. 26,1165 (1990). . S. B. Smith, P. K. Aldridge, J. B. Callis, Science 243, 203 (1989). . N. J. Rampino and A. Chrambach, Anal. Biochem. 194, 278 (1991). . K. Morikawa and M. Yanagida, J. Biochem. 89, 693 (1981). . I. Auzanneau, C. Barreau, L. Salome, C. R. Acad. Sci. Paris 316, 459 (1993). . H. Kabata et al., Science 262, 1561 (1993). . D. A. Schafer, J. Gelles, M. P. Sheetz, R. Landick, Nature 352, 444 (1991). . Laser excitation at 488.0 and 514.5 nm was provided by an argon ion laser (Lexel Lasers, Fremont, CA). The laser beam entered the microscope through a back port and was directed to an oil-immersion objective (x100, NA = 1.3, Nikon Instrument Group, Melville, NY) by a dichroic beamsplitter (505DRLP02 or 22. M. B. Schneider and W. W. Webb, Appl. Opt. 20, 1382 (1981). 23. R. Rigler, U. Mets, J. Widengren, P. Kask, Eur. Bio- phys. J. 22,169 (1993). 24. W. Feller, An Introduction to Probability Theory and its Applications (Wiley, New York, ed. 3, 1968). 25. S. A. Soper, H. L. Nutter, R. A. Keller, L. M. Davis, E. B. Shera, Photochem. Photobiol. 57, 972 (1993). 34. W. P. Ambrose, P. M. Goodwin, J. C. Martin, R. A. Keller, ibid., p. 364. 35. S.N. acknowledges the Whitaker Foundation for a young investigator award. D.T.C. is a Beckman Cell Science Scholar of Stanford University. This work was supported by Beckman Instruments, Inc. 18 July 1994; accepted 19 September 1994 Molecular Computation of Solutions to Combinatorial Problems Leonard M. Adleman The tools of molecular biology were used to solve an instance of the directed Hamiltonian path problem. A small graph was encoded in molecules of DNA, and the "operations" of the computation were performed with standard protocols and enzymes. This experiment demonstrates the feasibility of carrying out computations at the molecular level. In 1959, Richard Feynman gave a visionary talk describing the possibility of building computers that were "sub-microscopic" (1). Despite remarkable progress in computer miniaturization, this goal has yet to be achieved. Here, the possibility of comput- ing directly with molecules is explored. A directed graph G with designated ver- tices vn and vout is said to have a Hamilto- nian path (2) if and only if there exists a sequence of compatible "one-way" edges el, e2, ... ., e, (that is, a path) that begins at in, ends at v., and enters every other vertex exactly once. Figure 1 shows a graph that for vn = 0 and v01u = 6 has a Hamiltonian path, given by the edges 0-*1, 1->2, 2->3, 3---4, 4->5, 5->6. If the edge 2->3 were removed from the graph, then the result- ing graph with the same designated verti- ces would not have a Hamiltonian path. Similarly, if the designated vertices were changed to vin = 3 and vout = 5 there Department of Computer Science and Institute for Molec- ular Medicine and Technology, University of Southern Cal- ifornia, 941 West 37th Place, Los Angeles, CA 90089, USA. SCIENCE * VOL. 266 * 11 NOVEMBER 1994 would be no Hamiltonian path (because, for example, there are no edges entering vertex 0). There are well-known algorithms for de- ciding whether an arbitrary directed graph with designated vertices has a Hamiltonian path or not. However, all known algorithms for this problem have exponential worst-case complexity, and hence there are instances of modest size for which these algorithms re- quire an impractical amount of computer time to render a decision. Because the direct- ed Hamiltonian path problem has been proven to be NP-complete, it seems likely that no efficient (that is, polynomial time) algorithm exists for solving it (2, 3). The following (nondeterministic) algo- rithm solves the directed Hamiltonian path problem: Step 1: Generate random paths through the graph. Step 2: Keep only those paths that begin with vin and end with v,,f. Step 3: If the graph has n vertices, then keep only those paths that enter exactly n vertices. Step 4: Keep only those paths that enter all of 1021 onFebruary25,2016DownloadedfromonFebruary25,2016DownloadedfromonFebruary25,2016DownloadedfromonFebruary25,2016Downloadedfrom Science 1994 a b 11 * 5* 6* 4 3* 2* 1* 76*3* 4 1 1 52 1 1 1* 5*2* 1* Track base Track base Track base Track base 1*1 7 2* 3* 1*1 6 7 11* 5* 1 5 6*3 4 11* 2* 1 2 3*6 7 11* 5* 1 5 6* 7* 1* 4* 1* 1* 7* 1 7*5 6 Walker Hairpin H1 Hairpin H2 1* 2* 5* 1*1 25 11 4 3* 6* 7 2* 3* 3* 2*76*3* 1 52 1 1* 5*2* 1* 1 4 1 2 33 4 11* 2* 5* 1*1 2 5 1 3* 6* 7 2* 3* 1 2 1 1*1 1*1 1* 4* Expended track Fresh track Direction of motion 3 2 4* 1*1 1* 3 2 4* REVIEW ARTICLENATURE CHEMISTRY DOI: 10.1038/NCHEM.957 Source: Omabegho, T., Sha, R. & Seeman, N. C. 2009 Molecular walkers
  • 9. DNA Computing ime study of the dynamics of a single mol- ule and the chemical and biochemical re- tions that such a molecule may undergo in lution. REFERENCES AND NOTES . W. E. Moerner, Science 265, 46 (1994), and refer- ences therein. . M. Orrit, J. Bernard, R. I. Personov, J. Phys. Chem. 97,10256(1993). . F. GOttler, T. Irngartinger, T. Plakhotnik, A. Renn, U. P. Wild, Chem. Phys. Lett. 217, 393 (1994). . M. Ishikawa, K. Hirano, T. Hayakawa, S. Hosoi, S. Brenner, Jpn. J. Appl. Phys. 33,1571 (1994). . E. Betzig and R. J. Chichester, Science 262, 1422 (1993). . J. K. Trautman, J. J. Macklin, L. E. Brus, E. Betzig, Nature 369, 40 (1994). . D. C. Nguyen, R. A. Keller, J. H. Jett, J. C. Martin, Anal. Chem. 59, 2158 (1987). . K. Peck, L. Stryer, A. N. Glazer, R. A. Mathies, Proc. Natl. Acad. Sci. U.S.A. 86, 4087 (1989). . W. B. Whitten, L. M. Ramsey, S. A. Arnold, B. V. Bronk, Anal. Chem. 63,1027 (1991); K. C. Ng, W. B. Whitten, S. A. Arnold, L. M. Ramsey, ibid. 64, 2914 (1992). . M. Eigen and R. Rigler, Proc. Natl. Acad. Sci. U.S.A. 91, 5740 (1994). . In fluorescence correlation spectroscopy, the inten- sity recorded at time t is multiplied by that recorded at t + At and the product is integrated over a finite period of time; see D. E. Koppel, Phys. Rev. A 10, 1938 (1974). . T. T. Perkins, D. E. Smith, S. Chu, Science 264, 819 (1994); T. T. Perkins, S. R. Quake, D. E. Smith, S. Chu, ibid., p. 822. . S. B. Smith, L. Finzi, C. Bustamante, ibid. 258,1122 (1992); C. Bustamante, Annu. Rev. Biophys. Bio- phys. Chem. 20, 415 (1991). . M. Washizu and 0. Kurosawa, IEEE Trans. Ind. Appl. 26,1165 (1990). . S. B. Smith, P. K. Aldridge, J. B. Callis, Science 243, 203 (1989). . N. J. Rampino and A. Chrambach, Anal. Biochem. 194, 278 (1991). . K. Morikawa and M. Yanagida, J. Biochem. 89, 693 (1981). . I. Auzanneau, C. Barreau, L. Salome, C. R. Acad. Sci. Paris 316, 459 (1993). . H. Kabata et al., Science 262, 1561 (1993). . D. A. Schafer, J. Gelles, M. P. Sheetz, R. Landick, Nature 352, 444 (1991). . Laser excitation at 488.0 and 514.5 nm was provided by an argon ion laser (Lexel Lasers, Fremont, CA). The laser beam entered the microscope through a back port and was directed to an oil-immersion objective (x100, NA = 1.3, Nikon Instrument Group, Melville, NY) by a dichroic beamsplitter (505DRLP02 or 22. M. B. Schneider and W. W. Webb, Appl. Opt. 20, 1382 (1981). 23. R. Rigler, U. Mets, J. Widengren, P. Kask, Eur. Bio- phys. J. 22,169 (1993). 24. W. Feller, An Introduction to Probability Theory and its Applications (Wiley, New York, ed. 3, 1968). 25. S. A. Soper, H. L. Nutter, R. A. Keller, L. M. Davis, E. B. Shera, Photochem. Photobiol. 57, 972 (1993). 34. W. P. Ambrose, P. M. Goodwin, J. C. Martin, R. A. Keller, ibid., p. 364. 35. S.N. acknowledges the Whitaker Foundation for a young investigator award. D.T.C. is a Beckman Cell Science Scholar of Stanford University. This work was supported by Beckman Instruments, Inc. 18 July 1994; accepted 19 September 1994 Molecular Computation of Solutions to Combinatorial Problems Leonard M. Adleman The tools of molecular biology were used to solve an instance of the directed Hamiltonian path problem. A small graph was encoded in molecules of DNA, and the "operations" of the computation were performed with standard protocols and enzymes. This experiment demonstrates the feasibility of carrying out computations at the molecular level. In 1959, Richard Feynman gave a visionary talk describing the possibility of building computers that were "sub-microscopic" (1). Despite remarkable progress in computer miniaturization, this goal has yet to be achieved. Here, the possibility of comput- ing directly with molecules is explored. A directed graph G with designated ver- tices vn and vout is said to have a Hamilto- nian path (2) if and only if there exists a sequence of compatible "one-way" edges el, e2, ... ., e, (that is, a path) that begins at in, ends at v., and enters every other vertex exactly once. Figure 1 shows a graph that for vn = 0 and v01u = 6 has a Hamiltonian path, given by the edges 0-*1, 1->2, 2->3, 3---4, 4->5, 5->6. If the edge 2->3 were removed from the graph, then the result- ing graph with the same designated verti- ces would not have a Hamiltonian path. Similarly, if the designated vertices were changed to vin = 3 and vout = 5 there Department of Computer Science and Institute for Molec- ular Medicine and Technology, University of Southern Cal- ifornia, 941 West 37th Place, Los Angeles, CA 90089, USA. SCIENCE * VOL. 266 * 11 NOVEMBER 1994 would be no Hamiltonian path (because, for example, there are no edges entering vertex 0). There are well-known algorithms for de- ciding whether an arbitrary directed graph with designated vertices has a Hamiltonian path or not. However, all known algorithms for this problem have exponential worst-case complexity, and hence there are instances of modest size for which these algorithms re- quire an impractical amount of computer time to render a decision. Because the direct- ed Hamiltonian path problem has been proven to be NP-complete, it seems likely that no efficient (that is, polynomial time) algorithm exists for solving it (2, 3). The following (nondeterministic) algo- rithm solves the directed Hamiltonian path problem: Step 1: Generate random paths through the graph. Step 2: Keep only those paths that begin with vin and end with v,,f. Step 3: If the graph has n vertices, then keep only those paths that enter exactly n vertices. Step 4: Keep only those paths that enter all of 1021 onFebruary25,2016DownloadedfromonFebruary25,2016DownloadedfromonFebruary25,2016DownloadedfromonFebruary25,2016Downloadedfrom Science 1994 Universal substrate for reaction kinetics DNA as a universal substrate for chemical kinetics David Soloveichika,1 , Georg Seeliga,b,1 , and Erik Winfreec,1 a Department of Computer Science and Engineering, University of Washington, Seattle, WA 98195; b Department of Electrical Engineering, University of Washington, Seattle, WA 98195; and c Departments of Computer Science, Computation and Neural Systems, and Bioengineering, California Institute of Technology, Pasadena, CA 91125 Edited by José N. Onuchic, University of California San Diego, La Jolla, CA, and approved January 29, 2010 (received for review August 18, 2009) Molecular programming aims to systematically engineer molecular and chemical systems of autonomous function and ever-increasing complexity. A key goal is to develop embedded control circuitry within a chemical system to direct molecular events. Here we show that systems of DNA molecules can be constructed that closely ap- proximate the dynamic behavior of arbitrary systems of coupled chemical reactions. By using strand displacement reactions as a primitive, we construct reaction cascades with effectively unimole- cular and bimolecular kinetics. Our construction allows individual reactions to be coupled in arbitrary ways such that reactants can participate in multiple reactions simultaneously, reproducing the desired dynamical properties. Thus arbitrary systems of chemical equations can be compiled into real chemical systems. We illustrate our method on the Lotka–Volterra oscillator, a limit-cycle oscillator, a chaotic system, and systems implementing feedback digital logic and algorithmic behavior. molecular programming ∣ mass-action kinetics ∣ strand displacement cascades ∣ chemical reaction networks ∣ nonlinear chemical dynamics Chemical reaction equations and mass-action kinetics provide a powerful mathematical language to describe and analyze chemical systems. For well over a century, mass-action kinetics has been used to model chemical experiments and to predict and explain their dynamical properties. Both biological and non- biological chemical systems can exhibit complex behaviors such as oscillations, memory, logic and feedback control, chaos, and pat- tern formation—all of which can be explained by the correspond- ing systems of coupled chemical reactions (1–4). Whereas the use wire the components to achieve particular functions. Attempts to systematically understand what functional behaviors can be ob- tained by using such components have targeted connections to analog and digital electronic circuits (10, 18, 19), neural networks (20–22), and computing machines (15, 20, 23, 24); in each case, complex systems are theoretically constructed by composing modular chemical subsystems that carry out key functions, such as boolean logic gates, binary memories, or neural computing units. Despite its apparent difficulty, we directly targeted CRNs for three reasons. First, shoehorning the design of synthetic chemical circuits into familiar but possibly inappropriate comput- ing models may not capture the natural potential and limitations of the chemical substrate. Second, there is a vast literature on the theory of CRNs (25, 26) and even on general methods to im- plement arbitrary polynomial ordinary differential equations as CRNs (27, 28). Third, as a fundamental model that captures the essential formal structure of chemistry, implementation of CRNs could provide a useful programming paradigm for mole- cular systems. Here we propose a method for compiling an arbitrary CRN into nucleic-acid-based chemistry. Given a formal specification of coupled chemical kinetics, we systematically design DNA mo- lecules implementing an approximation of the system scaled to an appropriate temporal and concentration regime. Formal species are identified with certain DNA strands, whose interactions are mediated by a set of auxiliary DNA complexes. Nonconserving CRNs can be implemented because the auxiliary species implic- itly supply energy and mass. OGY Soloveichik et al. 2010 1: 12: 3: 2 3 buffering module time (hrs) concentration(nM) unscaled 1.5 1 1 5.105 /M/s 1/300 /s 1/300 /s scaled 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 5 10 15 20 A B CIdeal chemical reactions DNA reaction modules Simulation of ideal and DNA reactions ideal DNA
  • 10. DNA Computing NAM data storage ime study of the dynamics of a single mol- ule and the chemical and biochemical re- tions that such a molecule may undergo in lution. REFERENCES AND NOTES . W. E. Moerner, Science 265, 46 (1994), and refer- ences therein. . M. Orrit, J. Bernard, R. I. Personov, J. Phys. Chem. 97,10256(1993). . F. GOttler, T. Irngartinger, T. Plakhotnik, A. Renn, U. P. Wild, Chem. Phys. Lett. 217, 393 (1994). . M. Ishikawa, K. Hirano, T. Hayakawa, S. Hosoi, S. Brenner, Jpn. J. Appl. Phys. 33,1571 (1994). . E. Betzig and R. J. Chichester, Science 262, 1422 (1993). . J. K. Trautman, J. J. Macklin, L. E. Brus, E. Betzig, Nature 369, 40 (1994). . D. C. Nguyen, R. A. Keller, J. H. Jett, J. C. Martin, Anal. Chem. 59, 2158 (1987). . K. Peck, L. Stryer, A. N. Glazer, R. A. Mathies, Proc. Natl. Acad. Sci. U.S.A. 86, 4087 (1989). . W. B. Whitten, L. M. Ramsey, S. A. Arnold, B. V. Bronk, Anal. Chem. 63,1027 (1991); K. C. Ng, W. B. Whitten, S. A. Arnold, L. M. Ramsey, ibid. 64, 2914 (1992). . M. Eigen and R. Rigler, Proc. Natl. Acad. Sci. U.S.A. 91, 5740 (1994). . In fluorescence correlation spectroscopy, the inten- sity recorded at time t is multiplied by that recorded at t + At and the product is integrated over a finite period of time; see D. E. Koppel, Phys. Rev. A 10, 1938 (1974). . T. T. Perkins, D. E. Smith, S. Chu, Science 264, 819 (1994); T. T. Perkins, S. R. Quake, D. E. Smith, S. Chu, ibid., p. 822. . S. B. Smith, L. Finzi, C. Bustamante, ibid. 258,1122 (1992); C. Bustamante, Annu. Rev. Biophys. Bio- phys. Chem. 20, 415 (1991). . M. Washizu and 0. Kurosawa, IEEE Trans. Ind. Appl. 26,1165 (1990). . S. B. Smith, P. K. Aldridge, J. B. Callis, Science 243, 203 (1989). . N. J. Rampino and A. Chrambach, Anal. Biochem. 194, 278 (1991). . K. Morikawa and M. Yanagida, J. Biochem. 89, 693 (1981). . I. Auzanneau, C. Barreau, L. Salome, C. R. Acad. Sci. Paris 316, 459 (1993). . H. Kabata et al., Science 262, 1561 (1993). . D. A. Schafer, J. Gelles, M. P. Sheetz, R. Landick, Nature 352, 444 (1991). . Laser excitation at 488.0 and 514.5 nm was provided by an argon ion laser (Lexel Lasers, Fremont, CA). The laser beam entered the microscope through a back port and was directed to an oil-immersion objective (x100, NA = 1.3, Nikon Instrument Group, Melville, NY) by a dichroic beamsplitter (505DRLP02 or 22. M. B. Schneider and W. W. Webb, Appl. Opt. 20, 1382 (1981). 23. R. Rigler, U. Mets, J. Widengren, P. Kask, Eur. Bio- phys. J. 22,169 (1993). 24. W. Feller, An Introduction to Probability Theory and its Applications (Wiley, New York, ed. 3, 1968). 25. S. A. Soper, H. L. Nutter, R. A. Keller, L. M. Davis, E. B. Shera, Photochem. Photobiol. 57, 972 (1993). 34. W. P. Ambrose, P. M. Goodwin, J. C. Martin, R. A. Keller, ibid., p. 364. 35. S.N. acknowledges the Whitaker Foundation for a young investigator award. D.T.C. is a Beckman Cell Science Scholar of Stanford University. This work was supported by Beckman Instruments, Inc. 18 July 1994; accepted 19 September 1994 Molecular Computation of Solutions to Combinatorial Problems Leonard M. Adleman The tools of molecular biology were used to solve an instance of the directed Hamiltonian path problem. A small graph was encoded in molecules of DNA, and the "operations" of the computation were performed with standard protocols and enzymes. This experiment demonstrates the feasibility of carrying out computations at the molecular level. In 1959, Richard Feynman gave a visionary talk describing the possibility of building computers that were "sub-microscopic" (1). Despite remarkable progress in computer miniaturization, this goal has yet to be achieved. Here, the possibility of comput- ing directly with molecules is explored. A directed graph G with designated ver- tices vn and vout is said to have a Hamilto- nian path (2) if and only if there exists a sequence of compatible "one-way" edges el, e2, ... ., e, (that is, a path) that begins at in, ends at v., and enters every other vertex exactly once. Figure 1 shows a graph that for vn = 0 and v01u = 6 has a Hamiltonian path, given by the edges 0-*1, 1->2, 2->3, 3---4, 4->5, 5->6. If the edge 2->3 were removed from the graph, then the result- ing graph with the same designated verti- ces would not have a Hamiltonian path. Similarly, if the designated vertices were changed to vin = 3 and vout = 5 there Department of Computer Science and Institute for Molec- ular Medicine and Technology, University of Southern Cal- ifornia, 941 West 37th Place, Los Angeles, CA 90089, USA. SCIENCE * VOL. 266 * 11 NOVEMBER 1994 would be no Hamiltonian path (because, for example, there are no edges entering vertex 0). There are well-known algorithms for de- ciding whether an arbitrary directed graph with designated vertices has a Hamiltonian path or not. However, all known algorithms for this problem have exponential worst-case complexity, and hence there are instances of modest size for which these algorithms re- quire an impractical amount of computer time to render a decision. Because the direct- ed Hamiltonian path problem has been proven to be NP-complete, it seems likely that no efficient (that is, polynomial time) algorithm exists for solving it (2, 3). The following (nondeterministic) algo- rithm solves the directed Hamiltonian path problem: Step 1: Generate random paths through the graph. Step 2: Keep only those paths that begin with vin and end with v,,f. Step 3: If the graph has n vertices, then keep only those paths that enter exactly n vertices. Step 4: Keep only those paths that enter all of 1021 onFebruary25,2016DownloadedfromonFebruary25,2016DownloadedfromonFebruary25,2016DownloadedfromonFebruary25,2016Downloadedfrom Science 1994 commentary Nucleic acid memory Victor Zhirnov, Reza M. Zadegan, Gurtej S. Sandhu, George M. Church and William L. Hughes Nucleic acid memory has a retention time far exceeding electronic memory. As an alternative storage media, DNA surpasses the information density and energy of operation offered by flash memory. I nformation and communication technologies generate vast amounts of data that will far eclipse today’s data flows In this Commentary, we discuss the information retention, density and energetics of NAM — specifically related to unfathomable technological advances — such as those from the Human Genome Project — with the scaling expertise of the Zhirnov et al. 2016 10cm 10cm 10cm
  • 11. Abstract Data Types Container List Associative array Multimap Set Stack Queue Double-ended queue Priority queue Tree Graph
  • 12. DNA Molecular Stack Recorder Record occurence of events in a cell Interfere with cellular mRNA in an ordered way Release a “trigger” signal for downstream processes after certain cellular events have transpired ……
  • 13. b' aa' d'c' g' ff' e' b c a a' b d e hh' lk x c' push write-X pop b a'a dc g f'f e report x'm 0 1 GAAG U G U G U G CGGGAGAU G G C U C U C C C G A A G U G G U G U C C G C C G G G C A GCGGCGG U U G G U C UCCC b' aa' d'c' g' ff' e' c a a' b d e hh' lk x push write-X pop b a'a dc g f'f e report x'm 0 1 G G G AG A C C A A CCGCCGC U G C C C G G C G G A C A C C A C U U C G G G A G A G C C AUCUCCCG C A C A C ACUUC d' c'e' start push read pop b a'a dc g f'f e 0 1 GA A G U G G U G U U U G G U C U C C C G A A G U G U G U G C DNA Bricks b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'm 0 1 GGGAGAG C C A U C U C C C G C A C A C A CUU C b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x start push write-X b g 0 1 G G G AG A G C C A UCUCCCG C AC A C A C U U C G G G A G A C C A A A U U A G U A G G U A G A C A A A A A A A G A C C G C U A A A C UCUAA U C A CA C C U A C U A A U A C A C C ACUUC (27nt) (31nt) (64nt) (64nt) (98nt)
  • 14. b' aa' d'c' g' ff' e' b c a a' b d e hh' lk x c' push write-X pop b a'a dc g f'f e report x'm 0 1 GAAG U G U G U G CGGGAGAU G G C U C U C C C G A A G U G G U G U C C G C C G G G C A GCGGCGG U U G G U C UCCC b' aa' d'c' g' ff' e' c a a' b d e hh' lk x push write-X pop b a'a dc g f'f e report x'm 0 1 G G G AG A C C A A CCGCCGC U G C C C G G C G G A C A C C A C U U C G G G A G A G C C AUCUCCCG C A C A C ACUUC DNA Bricks b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'm 0 1 GGGAGAG C C A U C U C C C G C A C A C A CUU C b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x start push write-X b g 0 1 G G G AG A G C C A UCUCCCG C AC A C A C U U C G G G A G A C C A A A U U A G U A G G U A G A C A A A A A A A G A C C G C U A A A C UCUAA U C A CA C C U A C U A A U A C A C C ACUUC (27nt) (64nt) (64nt) (98nt) Genetic Algorithm - MOO Find a set of brick sequences such that: Individual bricks have the required secondary structure (e.g. hairpins and ss/ds segments) Desired brick-pair reactions have a maximally negative Gibbs free energy of binding Undesired brick-pair reactions have close to 0 or positive Gibbs free energy of binding
  • 15. DNA Molecular Stack Recorder c a a* b ca a* b c*a* a b* d*f* f g* e* c a b a* c* a* b* a d*f* f g* e* * b RECORDING Start UCNC2016, Annunziata Lopiccolo – Newcastle University stack empty Recording signals
  • 16. DNA Molecular Stack Recorder c a a* b ca a* b c*a* a b* d*f* f g* e* c a b a* c* a* b* a d*f* f g* e* * b c a a* b c*a* a b* d*f* f g* e* ca a* b c*a* a b* d*f* f g* e* c a b a* c* a* b* a d*f* f g* e* d e c a Start Push UCNC2016, Annunziata Lopiccolo – Newcastle University stack empty Recording signals
  • 17. DNA Molecular Stack Recorder c a a* b ca a* b c*a* a b* d*f* f g* e* c a b a* c* a* b* a d*f* f g* e* a a* b c a b a* c* a* b* a d*f* f g* e* d e c a a* b b Push binds by toehold UCNC2016, Annunziata Lopiccolo – Newcastle University stack empty Recording signals
  • 18. DNA Molecular Stack Recorder c a a* b ca a* b c*a* a b* d*f* f g* e* c a b a* c* a* b* a d*f* f g* e* * b c a b a* c* a* b* a d*f* f g* e* d e c a a* b c a b a* c* a* b* a d*f* f g* e* d e ca a* b c*a* a b* d*f* f g* e* Branch migration UCNC2016, Annunziata Lopiccolo – Newcastle University stack empty Recording signals
  • 19. DNA Molecular Stack Recorder c a a* b ca a* b c*a* a b* d*f* f g* e* c a b a* c* a* b* a d*f* f g* e* * b c a b a* c* a* b* a d*f* f g* e* d e c a a* b c a b a* c* a* b* a d*f* f g* e* d e ca a* b c*a* a b* d*f* f g* e* a* b c*a* a b* d*f* f g* e* ca a* b c*a* a b* d*f* f g* e* c a b a* c* a* b* a d*f* f g* e* d e c a a* b c a b a* c* a* b* a d*f* f g* e* d e c a a* b c*a* a b* d*f* f g* e* c a b a* c* a* b* a d*f* f g* e* d e ca a* b c*a* a b* d*f* f g* e* c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* d e c a a* b c a b a* d e c a b a* d X UCNC2016, Annunziata Lopiccolo – Newcastle University stack empty X Recording signals
  • 20. DNA Molecular Stack Recorder c a a* b ca a* b c*a* a b* d*f* f g* e* c a b a* c* a* b* a d*f* f g* e* b c a b a* c* a* b* a d*f* f g* e* d e c a a* b c a b a* c* a* b* a d*f* f g* e* d e ca a* b c*a* a b* d*f* f g* e* c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* b X Hybridisation UCNC2016, Annunziata Lopiccolo – Newcastle University 1 signal on stack X Recording signals
  • 21. DNA Molecular Stack Recorder c a a* b ca a* b c*a* a b* d*f* f g* e* c a b a* c* a* b* a d*f* f g* e* b c a b a* c* a* b* a d*f* f g* e* d e c a a* b c a b a* c* a* b* a d*f* f g* e* d e ca a* b c*a* a b* d*f* f g* e* c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* b c a a* b c*a* a b* d*f* f g* e* ca a* b c*a* a b* d*f* f g* e* c a b a* c* a* b* a d*f* f e* Push X UCNC2016, Annunziata Lopiccolo – Newcastle University 1 signal on stack X Recording signals
  • 22. DNA Molecular Stack Recorder ca a* c*a* a b* d*f* f g* e* c a b a* c* a* b* a d*f* f g* e* b c a b a* c* a* b* a d*f* f g* e* d e c a a* b c a b a* c* a* b* a d*f* f g* e* d e ca a* b c*a* a b* d*f* f g* e* c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* b c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b X Push binds by toehold UCNC2016, Annunziata Lopiccolo – Newcastle University 1 signal on stack X Recording signals
  • 23. DNA Molecular Stack Recorder c a b a* c* a* b* a d*f* f g* e* b c a b a* c* a* b* a d*f* f g* e* d e c a a* b c a b a* c* a* b* a d*f* f g* e* d e ca a* b c*a* a b* d*f* f g* e* c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* b c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e ca a* b c*a* a b* d*f* f g* e* X Branch migration UCNC2016, Annunziata Lopiccolo – Newcastle University 1 signal on stack X Recording signals
  • 24. DNA Molecular Stack Recorder c a b a* c* a* b* a d*f* f g* e* b c a b a* c* a* b* a d*f* f g* e* d e c a a* b c a b a* c* a* b* a d*f* f g* e* d e ca a* b c*a* a b* d*f* f g* e* c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* b c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e ca a* b c*a* a b* d*f* f g* e* a* b c*a* a b* d*f* f g* e* ca a* b c*a* a b* d*f* f g* e* c a b a* c* a* b* a d*f* g d e c a a* b c a b a* c* a* b* a d*f* g d c*a* a b* d*f* f g* e* c a b a* c* a* b* a d*f* g d c a b a* c* a* b* a d*f* g d d e c a a* b c a b a* d X Y Signal Y UCNC2016, Annunziata Lopiccolo – Newcastle University 1 signal on stack X Y Recording signals
  • 25. DNA Molecular Stack Recorder c a b a* c* a* b* a d*f* f g* e* d e c a a* b c a b a* c* a* b* a d*f* f g* e* d e ca a* b c*a* a b* d*f* f g* e* c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* b c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e ca a* b c*a* a b* d*f* f g* e* c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* b X Y Hybridisation UCNC2016, Annunziata Lopiccolo – Newcastle University 2 signals on stack X Y Recording signals
  • 26. DNA Molecular Stack Recorder c a b a* c* a* b* a d*f* f g* e* d e c a a* b c a b a* c* a* b* a d*f* f g* e* d e ca a* b c*a* a b* d*f* f g* e* c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* b c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e ca a* b c*a* a b* d*f* f g* e* c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* b c a a* b c*a* a b* d*f* f g* e* ca a* b c*a* a b* d*f* f g* e* c a b a* c* a* b* a d Push X Y UCNC2016, Annunziata Lopiccolo – Newcastle University 2 signals on stack X Y Recording signals
  • 27. DNA Molecular Stack Recorder c a b a* c* a* b* a d*f* f g* e* d e ca a* c*a* a b* d*f* f g* e* c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* b c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e ca a* b c*a* a b* d*f* f g* e* c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* b c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b X Y Push binds by toehold UCNC2016, Annunziata Lopiccolo – Newcastle University 2 signals on stack X Y Recording signals
  • 28. DNA Molecular Stack Recorder c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* b c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e ca a* b c*a* a b* d*f* f g* e* c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* b c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b Branch migration X Y UCNC2016, Annunziata Lopiccolo – Newcastle University 2 signals on stack X Y Recording signals
  • 29. DNA Molecular Stack Recorder c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* b c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e ca a* b c*a* a b* d*f* f g* e* c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* b c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c*a* a b* d*f* f g* e* ca c*a c c* d e c a a* b c c* c*a* a b* d*f* f g* e* c c* c c* d e c a a* b c c* X Y X Signal X UCNC2016, Annunziata Lopiccolo – Newcastle University 2 signals on stack X Y X Recording signals
  • 30. DNA Molecular Stack Recorder c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e ca a* b c*a* a b* d*f* f g* e* c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* * b c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b X Y X Hybridisation UCNC2016, Annunziata Lopiccolo – Newcastle University 3 signals on stack X Y X Recording signals
  • 31. DNA Molecular Stack Recorder c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e ca a* b c*a* a b* d*f* f g* e* c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* * b c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b X Y X Hybridisation UCNC2016, Annunziata Lopiccolo – Newcastle University 3 signals on stack X Y X Popping signals
  • 32. DNA Molecular Stack Recorder c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c*e* d* c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a*d* e* c* ac Y XX c a c* a* d* e* c* c a c* a* c a Read UCNC2016, Annunziata Lopiccolo – Newcastle University 3 signals on stack X Y X Popping signals
  • 33. DNA Molecular Stack Recorder c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c*e* d* c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a*d* e* c* c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* e f* g fd a* b ac Y XX Read binds by toehold, Branch migration follows UCNC2016, Annunziata Lopiccolo – Newcastle University 3 signals on stack X Y X Popping signals
  • 34. DNA Molecular Stack Recorder b a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b b a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c*e* d* b a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b d* e* c* b a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* e f* g fd a* b ac b a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c a b a* d f g f* e c* a* b* a d* f* g* f e* b a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c*e* d* Y X X Strand displacement c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c*e* d* c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a*d* e* c* c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* e f* g fd a* b ac c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c a b a* c* a* b* a Signal X released UCNC2016, Annunziata Lopiccolo – Newcastle University 2 signals on stack X Y X Popping signals
  • 35. DNA Molecular Stack Recorderd* e* c* c c* c c* ca a* b df f* g e c c* c c* d* e* c* c c* c d*f* f g* e* c* a* b* a d*f* f g* e* a* d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c*e* d* d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b d* e* c* d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* e f* g fd a* b ac d*f* f g* e* d e c a a* b c a b a* d f g f* e c* a* b* a d* f* g* f e* d*f* f g* e* d e c a a* b c*e* d* d e c a Y X X Pop c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c*e* d* c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a*d* e* c* c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* e f* g fd a* b ac c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c a b a* c* a* b* a UCNC2016, Annunziata Lopiccolo – Newcastle University 2 signals on stack X Y Popping signals
  • 36. DNA Molecular Stack Recorder c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c*e* d* c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a*d* e* c* c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* e f* g fd a* b ac c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c a b a* c* a* b* a c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c*e* d* d*f* f g* e* c* a* b* a d*f* f g* e* a* d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c*e* d* d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b d* e* c* d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* e f* g fd a* b ac d*f* f g* e* d e c a a* b c a b a* d f g f* e c* a* b* a d* f* g* f e* d*f* f g* e* d e c a a* b c*e* d* d e c a Y X X Pop binds by toehold, Branch migration follows UCNC2016, Annunziata Lopiccolo – Newcastle University 2 signals on stack X Y Popping signals
  • 37. DNA Molecular Stack Recorder c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c*e* d* c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a*d* e* c* c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* e f* g fd a* b ac c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c a b a* c* a* b* a c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c*e* d* c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a*d* e* c* ac d*f* f g* e* c* a* b* a d*f* f g* e* a* d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c*e* d* d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b d* e* c* d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* e f* g fd a* b ac d*f* f g* e* d e c a a* b c a b a* d f g f* e c* a* b* a d* f* g* f e* d*f* f g* e* d e c a a* b c*e* d* d e c a Y X X Hairpin reforms * c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b * c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c*e* d* * c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b d* e* c* * c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* e f* g fd a* b ac * c a b a* c* a* b* a d*f* f g* e* d e c a a* b c a b a* d f g f* e c* a* b* a d* f* g* f e* * c a b a* c* a* b* a d*f* f g* e* d e c a a* b c*e* d* Pop-Push double strand UCNC2016, Annunziata Lopiccolo – Newcastle University 2 signals on stack X Y Popping signals
  • 38. DNA Molecular Stack Recorder c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c*e* d* c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a*d* e* c* c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* e f* g fd a* b ac c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c a b a* c* a* b* a c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c*e* d* c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a*d* e* c* ac d*f* f g* e* c* a* b* a d*f* f g* e* a* d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c*e* d* d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b d* e* c* d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* e f* g fd a* b ac d*f* f g* e* d e c a a* b c a b a* d f g f* e c* a* b* a d* f* g* f e* d*f* f g* e* d e c a a* b c*e* d* d e c a Y X X c a b a* c* a* b* a d*f* f g* e* d e c c* d* e* c* c a b a* c* a* b* a d*f* f g* e* d e c c* c a b a* d e c Read UCNC2016, Annunziata Lopiccolo – Newcastle University 2 signals on stack X Y d e c a a* b d* e* c* c a b a* d f g f* e c* a* b* a d* f* g* f e* Popping signals
  • 39. DNA Molecular Stack Recorder c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a*d* e* c* c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* e f* g fd a* b ac c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c a b a* c* a* b* a c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c*e* d* c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a*d* e* c* c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* e f* g fd a* b ac c a b a* d e c a b c a b a* d*f* f g* e* c* a* b* a d*f* f g* e* a* d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c*e* d* d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b d* e* c* d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* e f* g fd a* b ac d*f* f g* e* d e c a a* b c a b a* d f g f* e c* a* b* a d* f* g* f e* d*f* f g* e* d e c a a* b c*e* d* d e c a Y X X Read binds by toehold, Branch migration follows UCNC2016, Annunziata Lopiccolo – Newcastle University 2 signals on stack X Y d e c a a* b d* e* c* c a b a* d f g f* e c* a* b* a d* f* g* f e* Popping signals
  • 40. DNA Molecular Stack Recorder a b a* * a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b a b a* * a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c*e* d* a b a* * a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b d* e* c* a b a* * a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* e f* g fd a* b ac a b a* * a* b* a d*f* f g* e* d e c a a* b c a b a* d f g f* e c* a* b* a d* f* g* f e* a b a* * a* b* a d*f* f g* e* d e c a a* b c*e* d* c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* e f* g c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c a b a* c* a* b* a c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c*e* d* c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a*d* e* c* c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* e f* g fd a* b ac c a b a* c* a* b* a d*f* f g* e* d e c a a* b c a b a* c* a* b* a c a b a* d e c a d*f* f g* e* a* d*f* f g* e* d e c a a* b c*e* d* d*f* f g* e* d e c a a* b d* e* c* d*f* f g* e* e f* g fd a* b ac c a b a* d f g f* e c* a* b* a d* f* g* f e* d e c a Y X X Strand displacement Signal Y released UCNC2016, Annunziata Lopiccolo – Newcastle University 1 signal on stack X Y d e c a a* b d* e* c* c a b a* d f g f* e c* a* b* a d* f* g* f e* Popping signals
  • 41. DNA Molecular Stack Recorder * f g* e* e c a b a* c* a* b* a d*f* f g* e* d e c a a* b * f g* e* e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c*e* d* * f g* e* e c a b a* c* a* b* a d*f* f g* e* d e c a a* b d* e* c* * f g* e* e c a b a* c* a* b* a d*f* f g* e* e f* g fd a* b ac * f g* e* e c a a* b c a b a* d f g f* e c* a* b* a d* f* g* f e* * f g* e* e c a a* b c*e* d* c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* e f* g c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c a b a* c* a* b* a c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c*e* d* c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a*d* e* c* c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* e f* g fd a* b ac c a b a* c* a* b* a d*f* f g* e* d e c a a* b c a b a* c* a* b* a c a b a* d e c a * e* a* * e* e c a a* b c*e* d* * e* d e c a a* b d* e* c* * e* e f* g fd a* b ac c a b a* d f g f* e c* a* b* a d* f* g* f e* d e c a Y X X f* d* e* c* c a b a* c* a* b* a d*f* d c a b a* c* a* b* a d*f* d ca a* b df f* g e c a b a* c* a* b* a d*f* d c a b a* c* a* b* a d*f* d d* e* c* c a b a* c* a* b* a d*f* d c a b a* d Pop UCNC2016, Annunziata Lopiccolo – Newcastle University 1 signal on stack X d e c a a* b d* e* c* c a b a* d f g f* e c* a* b* a d* f* g* f e* Popping signals
  • 42. DNA Molecular Stack Recorder c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c a b a* c* a* b* a c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c*e* d* c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a*d* e* c* c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* e f* g fd a* b ac c a b a* c* a* b* a d*f* f g* e* d e c a a* b c a b a* c* a* b* a c a b a* c* a* b* a d*f* f g* e* d e c a a* b c*e* d* c a b a* d e c a * f g* e* e c a b a* c* a* b* a d*f* f g* e* d e c a a* b * f g* e* e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c*e* d* * f g* e* e c a b a* c* a* b* a d*f* f g* e* d e c a a* b d* e* c* * f g* e* e c a b a* c* a* b* a d*f* f g* e* e f* g fd a* b ac * f g* e* e c a a* b c a b a* d f g f* e c* a* b* a d* f* g* f e* * f g* e* e c a a* b c*e* d* * e* a* * e* e c a a* b c*e* d* * e* d e c a a* b d* e* c* * e* e f* g fd a* b ac c a b a* d f g f* e c* a* b* a d* f* g* f e* d e c a Y X X Pop binds by toehold, Branch migration follows UCNC2016, Annunziata Lopiccolo – Newcastle University 1 signal on stack X d e c a a* b d* e* c* c a b a* d f g f* e c* a* b* a d* f* g* f e* Popping signals
  • 43. DNA Molecular Stack Recorder * f g* e* e c a b a* c* a* b* a d*f* f g* e* d e c a a* b * f g* e* e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c*e* d* * f g* e* e c a b a* c* a* b* a d*f* f g* e* d e c a a* b d* e* c* * f g* e* e c a b a* c* a* b* a d*f* f g* e* e f* g fd a* b ac * f g* e* e c a a* b c a b a* d f g f* e c* a* b* a d* f* g* f e* * f g* e* e c a a* b c*e* d* * e* a* * e* e c a a* b c*e* d* * e* d e c a a* b d* e* c* * e* e f* g fd a* b ac c a b a* d f g f* e c* a* b* a d* f* g* f e* d e c a Y X X c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c*e* d* c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a*d* e* c* c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* e f* g fd a* b ac c a b a* c* a* b* a d*f* f g* e* d e c a a* b c a b a* c* a* b* a c a b a* c* a* b* a d*f* f g* e* d e c a a* b c*e* d* c a b a* c* a* b* a d*f* f g* e* d e c a a*d* e* c* ac Hairpin reforms ** f g* e* e c a b a* c* a* b* a d*f* f g* e* d e c a a* b ** f g* e* e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c*e* d* ** f g* e* e c a b a* c* a* b* a d*f* f g* e* d e c a a* b d* e* c* ** f g* e* e c a b a* c* a* b* a d*f* f g* e* e f* g fd a* b ac ** f g* e* e c a a* b c a b a* d f g f* e c* a* b* a d* f* g* f e* ** f g* e* e c a a* b c*e* d* Pop-Push double strand UCNC2016, Annunziata Lopiccolo – Newcastle University 1 signal on stack X d e c a a* b d* e* c* c a b a* d f g f* e c* a* b* a d* f* g* f e* Popping signals
  • 44. DNA Molecular Stack Recorder * f g* e* e c a b a* c* a* b* a d*f* f g* e* d e c a a* b * f g* e* e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c*e* d* * f g* e* e c a b a* c* a* b* a d*f* f g* e* d e c a a* b d* e* c* * f g* e* e c a b a* c* a* b* a d*f* f g* e* e f* g fd a* b ac * f g* e* e c a a* b c a b a* d f g f* e c* a* b* a d* f* g* f e* * f g* e* e c a a* b c*e* d* * e* a* * e* e c a a* b c*e* d* * e* d e c a a* b d* e* c* * e* e f* g fd a* b ac c a b a* d f g f* e c* a* b* a d* f* g* f e* d e c a Y X X c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c*e* d* c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a*d* e* c* c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* e f* g fd a* b ac c a b a* c* a* b* a d*f* f g* e* d e c a a* b c a b a* c* a* b* a c a b a* c* a* b* a d*f* f g* e* d e c a a* b c*e* d* c a b a* c* a* b* a d*f* f g* e* d e c a a*d* e* c* ac c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c c* d* e* c* c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c c* c a b a* d e c a b a* d e c Read UCNC2016, Annunziata Lopiccolo – Newcastle University 1 signal on stack X d e c a a* b d* e* c* c a b a* d f g f* e c* a* b* a d* f* g* f e* d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c*e* d* d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b d* e* c* d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* e f* g fd a* b ac d*f* f g* e* d e c a a* b c a b a* d f g f* e c* a* b* a d* f* g* f e* Popping signals
  • 45. DNA Molecular Stack Recorderc a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a*d* e* c* c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* e f* g fd a* b ac c a b a* c* a* b* a d*f* f g* e* d e c a a* b c a b a* c* a* b* a c a b a* c* a* b* a d*f* f g* e* d e c a a* b c*e* d* c a b a* c* a* b* a d*f* f g* e* d e c a a*d* e* c* c a b a* c* a* b* a d*f* f g* e* e f* g fd a* b ac * f g* e* e c a b a* c* a* b* a d*f* f g* e* d e c a a* b * f g* e* e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c*e* d* * f g* e* e c a b a* c* a* b* a d*f* f g* e* d e c a a* b d* e* c* * f g* e* e c a b a* c* a* b* a d*f* f g* e* e f* g fd a* b ac * f g* e* e c a a* b c a b a* d f g f* e c* a* b* a d* f* g* f e* * f g* e* e c a a* b c*e* d* * e* a* * e* e c a a* b c*e* d* * e* d e c a a* b d* e* c* * e* e f* g fd a* b ac c a b a* d f g f* e c* a* b* a d* f* g* f e* d e c a Y X X Read binds by toehold, Branch migration follows UCNC2016, Annunziata Lopiccolo – Newcastle University 1 signal on stack X d e c a a* b d* e* c* c a b a* d f g f* e c* a* b* a d* f* g* f e* d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c*e* d* d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b d* e* c* d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* e f* g fd a* b ac d*f* f g* e* d e c a a* b c a b a* d f g f* e c* a* b* a d* f* g* f e* Popping signals
  • 46. DNA Molecular Stack Recorder b a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b b a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c*e* d* b a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b d* e* c* b a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* e f* g fd a* b ac b a* b* a d*f* f g* e* d e c a a* b c a b a* d f g f* e c* a* b* a d* f* g* f e* * f g* e* e c a b a* c* a* b* a d*f* f g* e* d e c a a* b * f g* e* e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c*e* d* * f g* e* e c a b a* c* a* b* a d*f* f g* e* d e c a a* b d* e* c* * f g* e* e c a b a* c* a* b* a d*f* f g* e* e f* g fd a* b ac * f g* e* e c a a* b c a b a* d f g f* e c* a* b* a d* f* g* f e* * f g* e* e c a a* b c*e* d* * e* a* * e* e c a a* b c*e* d* * e* d e c a a* b d* e* c* * e* e f* g fd a* b ac c a b a* d f g f* e c* a* b* a d* f* g* f e* d e c a Y X X c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* e f* g fd a* b ac c a b a* c* a* b* a d*f* f g* e* d e c a a* b c a b a* c* a* b* a c a b a* c* a* b* a d*f* f g* e* d e c a a* b c*e* d* c a b a* c* a* b* a d*f* f g* e* d e c a ad* e* c* c a b a* c* a* b* a d*f* f g* e* e f* g fd a* b ac c a a* b c a b a* c* a* b* a Strand displacement Signal X released UCNC2016, Annunziata Lopiccolo – Newcastle University stack empty X d e c a a* b d* e* c* c a b a* d f g f* e c* a* b* a d* f* g* f e* d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c*e* d* d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b d* e* c* d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* e f* g fd a* b ac d*f* f g* e* d e c a a* b c a b a* d f g f* e c* a* b* a d* f* g* f e* Popping signals
  • 47. Naive Chemical View S + P + Xb' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'm b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'm b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'm
  • 48. Real Well-Mixed Chemistry S + P + Xb' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'm b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'm b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'm — partly formed complexes — partially bound complexes — unintended side reactions — DNA complexes have finite diffusion and reaction rates
  • 49. Real Well-Mixed Chemistry S + P + Xb' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'm b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'm b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'm — partly formed complexes — partially bound complexes — unintended side reactions — DNA complexes have finite diffusion and reaction rates
  • 50. + P b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'm + Xb' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'm S b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'm Real Well-Mixed Chemistry Required: Rule-based stochastic model to rigorously capture the reactions happening in-vitro To know if stack chemistry is operating correctly:
  • 51. core reactions Which Rules? — Reaction Space all reactions with full domains bound all possible reactions, including partially bound complexes b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'm
  • 52. core reactions all reactions with full domains bound all possible reactions, including partially bound complexes Microsoft DSD 2.0 Which Rules? — Reaction Space Multi-strand thermodynamic prediction software b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'm
  • 54. pop push read X…stack stack…push stack…X…stack stack…Xpush stack…push stack…push Core Reactions: 1-Step Rules stack… …stack start push X readRate parameters pop start …stack push X…stack + + + (1) (2) (3) (4) + …stack + X …stack + …stack + poppush Recording Popping kf(h) ⌦ kr kf(h+b) ⌦ kr kf(h+b) ⌦ kr ! ! kf(h) = 106 M 1 s 1 kf(h+b) = 105 M 1 s 1 core reactions (5) kf(s1) = 105 M 1 s 1 kf(s2) = 104 M 1 s 1 kf(s2) kf(s1) kr = 0 (No ring complexes allowed)
  • 55. Simulation Results Recording signals t = 5 min t = 1 hour S 200nM b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'm b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'mP 200nM c a a* b c*a* a b* d*f* f g* e* ca a* b c*a* a b* d*f* f g* e* c a b a* c* a* b* a d*f* f g* e* d e c a a* b c a b a* c* a* b* a d*f* f g* e* d e c a a* b c*a* a b* d*f* f g* e* c a b a* c* a* b* a d*f* f g* e* d e ca a* b c*a* a b* d*f* f g* e* c a b a* c* a* b* a d*f* f e* d e c a b a* c* a* b* a d*f* f e* sp sp p
  • 56. Simulation Results Recording signals S 200nM b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'm b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'mP 200nM b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'm X 200nM c*a* a b* d*f* f g* e* ca a* b c*a* a b* d*f* f g* e* c a b a* c* a* b* a d*f* f g* e* d e c a a* b c a b a* c* a* b* a d*f* f g* e* d e c a a* b c*a* a b* d*f* f g* e* c a b a* c* a* b* a d*f* f g* e* d e ca a* b c*a* a b* d*f* f g* e* c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c a b a* c* a* b* a d*f* f e* d e c a b a* c* a* b* a d*f* f e* d e c a a* b spx spxpx spx t = 5 min t = 1 hour
  • 57. Simulation Results Recording signals S 200nM b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'm b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'mP 200nM b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'm X 200nM b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'mP 200nM spx spxpxp t = 5 min t = 1 hour spxp p sp spxp d e c a a* b c a b a* c* a* b* a d*f* f g* e* d e c a a* b c*a* a b* d*f* f g* e* c a b a* c* a* b* a d*f* f g* e* d e ca a* b c*a* a b* d*f* f g* e* c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c*a* a b* d*f* f g* e* c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e ca a* b c*a* a b* d*f* f g* e* c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a
  • 58. Simulation Results Recording signals S 200nM b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'm b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'mP 200nM b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'm X 200nM b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'mP 200nM b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'm X 200nM t = 5 min t = 1 hour spx spxpx spxpxpx spxpxpxpx spxpx spxpxpx g* c*a* a b* d*f* f g* e* c a b a* c* a* b* a d*f* f g* e* d e ca a* b c*a* a b* d*f* f g* e* c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c*a* a b* d*f* f g* e* c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e ca a* b c*a* a b* d*f* f g* e* c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b
  • 59. Simulation Results Recording signals S 200nM b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'm b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'mP 200nM b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'm X 200nM b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'mP 200nM b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'm X 200nM P 200nM b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'm t = 5 min t = 1 hour g* g* e c a a* b c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b * a b* d*f* f g* e* c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e ca a* b c*a* a b* d*f* f g* e* c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* e c a a* b c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b p spxp spxpx spxpxp spxpxpx spxpxpxp spxpxpxpxp spxpxp spxpxpxp
  • 60. Simulation Results Recording signals S 200nM b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'm b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'mP 200nM b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'm X 200nM b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'mP 200nM b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'm X 200nM P 200nM b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'mX 200nM b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'm t = 5 min t = 1 hour g* g* d*f* f g* e* c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e ca a* b c*a* a b* d*f* f g* e* c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* e c a a* b c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b spxpxpx spxpx spxpxpxpx spxpxpxpxpx spxpxpxpxpxpx spxpxpx spxpxpxpxspxpx
  • 61. Simulation Results R 200nM b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'm Popping signals t = 5 min t = 1 hour [xr] spxp spxpx spxpxp spxpxp spxpxpx spxpxpxp spxpxpxpx spxpxpxpxp [xr] spxp spxpxpxp g* g* e c a a* b c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b * a b* d*f* f g* e* c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e ca a* b c*a* a b* d*f* f g* e* c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* e c a a* b c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b
  • 62. Simulation Results R 200nM b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'm Q 200nM b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'm Popping signals t = 5 min t = 1 hour g* c*a* a b* d*f* f g* e* c a b a* c* a* b* a d*f* f g* e* d e ca a* b c*a* a b* d*f* f g* e* c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c*a* a b* d*f* f g* e* c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e ca a* b c*a* a b* d*f* f g* e* c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b q [xr] spx spxp spxpx spxpxp spxpxpx spxpxpxp spxpxpxpx spxpxpxpxp [xr] [pq] [pq] spxpx spxpxp spxpxpx
  • 63. Simulation Results R 200nM b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'm Q 200nM b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'm R 200nM b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'm Popping signals t = 5 min t = 1 hour d e c a a* b c a b a* c* a* b* a d*f* f g* e* d e c a a* b c*a* a b* d*f* f g* e* c a b a* c* a* b* a d*f* f g* e* d e ca a* b c*a* a b* d*f* f g* e* c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c*a* a b* d*f* f g* e* c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e ca a* b c*a* a b* d*f* f g* e* c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a r q sp [xr] [pq] spxp spxpx spxpxp spxpxpxp spxpxpxpxp sp [xr] [pq] spxp spxpxp
  • 64. Simulation Results R 200nM b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'm Q 200nM b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'm R 200nM b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'm Q 200nM b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'm Popping signals t = 5 min t = 1 hour [xr] [pq] [xr] [pq] c*a* a b* d*f* f g* e* ca a* b c*a* a b* d*f* f g* e* c a b a* c* a* b* a d*f* f g* e* d e c a a* b c a b a* c* a* b* a d*f* f g* e* d e c a a* b c*a* a b* d*f* f g* e* c a b a* c* a* b* a d*f* f g* e* d e ca a* b c*a* a b* d*f* f g* e* c a b a* c* a* b* a d*f* f g* e* d e c a b a* c* a* b* a d*f* f g* e* d e c a a* b c a b a* c* a* b* a d*f* f e* d e c a b a* c* a* b* a d*f* f e* d e c a a* b spx spx spxp spxpx spxpxp spxpxpx spxpxpxp spxpxpxpxp spxp spxpx
  • 65. Simulation Results R 200nM b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'm Q 200nM b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'm R 200nM b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'm Q 200nM b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'm R 200nM b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'm Popping signals t = 5 min t = 1 hour c a a* b c*a* a b* d*f* f g* e* ca a* b c*a* a b* d*f* f g* e* c a b a* c* a* b* a d*f* f g* e* d e c a a* b c a b a* c* a* b* a d*f* f g* e* d e c a a* b c*a* a b* d*f* f g* e* c a b a* c* a* b* a d*f* f g* e* d e ca a* b c*a* a b* d*f* f g* e* c a b a* c* a* b* a d*f* f e* d e c a b a* c* a* b* a d*f* f e* [xr] [pq] sp [xr] [pq] spxp spxpxp spxpxpxp sp spxp
  • 66. Simulation Results R 200nM b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'm Q 200nM b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'm R 200nM b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'm Q 200nM b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'm R 200nM b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'm Q 200nM b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'm Popping signals t = 5 min t = 1 hour g* ca a*df f* e c a b a* c* a* b* a d*f* f g* e* e f* g fd a* b ac c a a* b s q sp [xr] [pq] [xr] [pq] s q sp spxp spxpxp spxpxpxp
  • 67. Experimental Results ●But results are hard to interpret because of the superposition of stacks. ●Concentration of read and pop needs to be controlled. 1 signal 2 signals 3 signals 1 signal2 signals SPX SPXP SPXPX SPXPXP SPXPXPX P RX PQ Reading and Popping UCNC2016, Annunziata Lopiccolo – Newcastle University Recording Popping Selected bioanalyzer results qualitatively show same pattern as simulations PAGE gel results still pose some questions…! P P P P P X X X X XP S S P P P S S S S dimer S dimer S SP SP SP SP SP SPP? SSP? S S dimer P SP SSP? X SPP? XP P+X S+P+X+P S+PS+X+P+P
  • 68. Simulation Results Brick order matters! t = 1 hour t = 1 hour S b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'm b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'm P b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'm X b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'm P b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'm X spxpx spxpxpx S b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'm b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'm X b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'm X b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'm P b' aa' d'c' g' ff' e'c a a' b c a a' b d e hh' lk x d' c'e' start push write-X read pop b a'a dc g f'f e report x'm P In order Out of order spxpx spxpxpx spx s spxpxpxpx spxpxpxpxpx spxpxpxpxpxpx spxpxpxpxpxpxpx
  • 69. Challenges Establishing In-Silico/In-Vitro Equivalence CHEMISTRY MODEL spxpxpx spxpxpx EXPERIMENT “a qualitative agreement exists” “pure” output: all species and concentrations known “proxy” output: chemistry state encoded by indirect variables y scale: nt y scale: migration time
  • 70. Challenges Establishing In-Silico/In-Vitro Equivalence CHEMISTRY MODEL DETECTOR
 MODEL spxpxpx spxpxpx EXPERIMENT y scale: nt y scale: migration time