The Algorithms of Life - Scientific Computing for Systems Biologyinside-BigData.com
In this deck from ISC 2019, Ivo Sbalzarini from TU Dresden presents: The Algorithms of Life - Scientific Computing for Systems Biology. In his talk, Sbalzarini mainly discussed the rapidly growing importance and influence in the life sciences for scientific high-performance computing.
"Scientific high-performance computing is of rapidly growing importance and influence in the life sciences. Thanks to the increasing knowledge about the molecular foundations of life, recent advances in biomedical data science, and the availability of predictive biophysical theories that can be numerically simulated, mechanistic understanding of the emergence of life comes within reach. Computing is playing a pivotal and catalytic role in this scientific revolution, both as a tool of investigation and hypothesis testing, but also as a school of thought and systems model. This is because a developing tissue, embryo, or organ can itself be seen as a massively parallel distributed computing system that collectively self-organizes to bring about behavior we call life. In any multicellular organism, every cell constantly takes decisions about growth, division, and migration based on local information, with cells communicating with each other via chemical, mechanical, and electrical signals across length scales from nanometers to meters. Each cell can therefore be understood as a mechano-chemical processing element in a complexly interconnected million- or billion-core computing system. Mechanistically understanding and reprogramming this system is a grand challenge. While the “hardware” (proteins, lipids, etc.) and the “source code” (genetic code) are increasingly known, we known virtually nothing about the algorithms that this code implements on this hardware. Our vision is to contribute to this challenge by developing computational methods and software systems for high-performance data analysis, inference, and numerical simulation of computer models of biological tissues, incorporating the known biochemistry and biophysics in 3D-space and time, in order to understand biological processes on an algorithmic basis. This ranges from real-time approaches to biomedical image analysis, to novel simulation languages for parallel high-performance computing, to virtual reality and machine learning for 3D microscopy and numerical simulations of coupled biochemical-biomechanical models. The cooperative, interdisciplinary effort to develop and advance our understanding of life using computational approaches not only places high-performance computing center stage, but also provides stimulating impulses for the future development of this field."
Watch the video: https://wp.me/p3RLHQ-kBB
Learn more: https://www.isc-hpc.com/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
We present an ab-initio real-time based computational approach to nonlinear optical properties in Condensed Matter systems. The equation of mot ions, and in particular the coupling of the electrons with the external electric field, are derived from the Berry phase formulation of the dynamical polarization. The zero-field Hamiltonian includes crystal local field effects, the renormalization of the independent particle energy levels by correlation and excitonic effects within the screened Hartree- Fock self-energy operator. The approach is validated by calculating the second-harmonic generation of SiC and AlAs bulk semiconductors : an excellent agreement is obtained with existing ab-initio calculations from response theory in frequency domain . We finally show applications to the second-harmonic generation of CdTe the third-harmonic generation of Si.
Reference :
Real-time approach to the optical properties of solids and nanostructures : Time-dependent Bethe-alpeter equation Phys. Rev. B 84, 245110 (2011)
Nonlinear optics from ab-initio by means of the dynamical Berry-phase
C. Attaccalite and M. Gruning Phys. Rev. B 88 (23), 235113 (2013)
Quantum field simulator for dynamics in curved spacetimeSérgio Sacani
In most cosmological models, rapid expansion of space marks the frst moments of
the Universe and leads to the amplifcation of quantum fuctuations1
. The description
of subsequent dynamics and related questions in cosmology requires an
understanding of the quantum felds of the standard model and dark matter in
curved spacetime. Even the reduced problem of a scalar quantum feld in an explicitly
time-dependent spacetime metric is a theoretical challenge2–5
, and thus a quantum
feld simulator can lead to insights. Here we demonstrate such a quantum feld
simulator in a two-dimensional Bose–Einstein condensate with a confgurable trap6,7
and adjustable interaction strength to implement this model system. We explicitly
show the realization of spacetimes with positive and negative spatial curvature by
wave-packet propagation and observe particle-pair production in controlled
power-law expansion of space, using Sakharov oscillations to extract amplitude
and phase information of the produced state. We fnd quantitative agreement with
analytical predictions for diferent curvatures in time and space. This benchmarks
and thereby establishes a quantum feld simulator of a new class. In the future,
straightforward upgrades ofer the possibility to enter unexplored regimes that give
further insight into relativistic quantum feld dynamics.
We report on cosmological N-body
simulations which run over up to 4
supercomputers across the globe. We
achieved to run simulations on 60 to 750
cores distributed over a variety of
supercomputers. Regardless of the
network latency of 0.32 s and the
communication over 30.000 km of optical
network cable we are able to achieve up
to 92% of the performance compared to
an equal number of cores on a single
supercomputer.
This is a Powerpoint for basic understanding regarding Molecular dynamics and NAMD simulation to providing basic information, schematic representation, to understanding the mechanism or process of molecular dynamics ( MD), and NAMD simulation brief discussion.
Optimization of technological process to decrease dimensions of circuits xor ...ijfcstjournal
The paper describes an approach of increasing of integration rate of elements of integrated circuits. The
approach has been illustrated by example of manufacturing of a circuit XOR. Framework the approach one
should manufacture a heterostructure with specific configuration. After that several special areas of the
heterostructure should be doped by diffusion and/or ion implantation and optimization of annealing of dopant
and/or radiation defects. We analyzed redistribution of dopant with account redistribution of radiation
defects to formulate recommendations to decrease dimensions of integrated circuits by using analytical
approaches of modeling of technological process.
The Algorithms of Life - Scientific Computing for Systems Biologyinside-BigData.com
In this deck from ISC 2019, Ivo Sbalzarini from TU Dresden presents: The Algorithms of Life - Scientific Computing for Systems Biology. In his talk, Sbalzarini mainly discussed the rapidly growing importance and influence in the life sciences for scientific high-performance computing.
"Scientific high-performance computing is of rapidly growing importance and influence in the life sciences. Thanks to the increasing knowledge about the molecular foundations of life, recent advances in biomedical data science, and the availability of predictive biophysical theories that can be numerically simulated, mechanistic understanding of the emergence of life comes within reach. Computing is playing a pivotal and catalytic role in this scientific revolution, both as a tool of investigation and hypothesis testing, but also as a school of thought and systems model. This is because a developing tissue, embryo, or organ can itself be seen as a massively parallel distributed computing system that collectively self-organizes to bring about behavior we call life. In any multicellular organism, every cell constantly takes decisions about growth, division, and migration based on local information, with cells communicating with each other via chemical, mechanical, and electrical signals across length scales from nanometers to meters. Each cell can therefore be understood as a mechano-chemical processing element in a complexly interconnected million- or billion-core computing system. Mechanistically understanding and reprogramming this system is a grand challenge. While the “hardware” (proteins, lipids, etc.) and the “source code” (genetic code) are increasingly known, we known virtually nothing about the algorithms that this code implements on this hardware. Our vision is to contribute to this challenge by developing computational methods and software systems for high-performance data analysis, inference, and numerical simulation of computer models of biological tissues, incorporating the known biochemistry and biophysics in 3D-space and time, in order to understand biological processes on an algorithmic basis. This ranges from real-time approaches to biomedical image analysis, to novel simulation languages for parallel high-performance computing, to virtual reality and machine learning for 3D microscopy and numerical simulations of coupled biochemical-biomechanical models. The cooperative, interdisciplinary effort to develop and advance our understanding of life using computational approaches not only places high-performance computing center stage, but also provides stimulating impulses for the future development of this field."
Watch the video: https://wp.me/p3RLHQ-kBB
Learn more: https://www.isc-hpc.com/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
We present an ab-initio real-time based computational approach to nonlinear optical properties in Condensed Matter systems. The equation of mot ions, and in particular the coupling of the electrons with the external electric field, are derived from the Berry phase formulation of the dynamical polarization. The zero-field Hamiltonian includes crystal local field effects, the renormalization of the independent particle energy levels by correlation and excitonic effects within the screened Hartree- Fock self-energy operator. The approach is validated by calculating the second-harmonic generation of SiC and AlAs bulk semiconductors : an excellent agreement is obtained with existing ab-initio calculations from response theory in frequency domain . We finally show applications to the second-harmonic generation of CdTe the third-harmonic generation of Si.
Reference :
Real-time approach to the optical properties of solids and nanostructures : Time-dependent Bethe-alpeter equation Phys. Rev. B 84, 245110 (2011)
Nonlinear optics from ab-initio by means of the dynamical Berry-phase
C. Attaccalite and M. Gruning Phys. Rev. B 88 (23), 235113 (2013)
Quantum field simulator for dynamics in curved spacetimeSérgio Sacani
In most cosmological models, rapid expansion of space marks the frst moments of
the Universe and leads to the amplifcation of quantum fuctuations1
. The description
of subsequent dynamics and related questions in cosmology requires an
understanding of the quantum felds of the standard model and dark matter in
curved spacetime. Even the reduced problem of a scalar quantum feld in an explicitly
time-dependent spacetime metric is a theoretical challenge2–5
, and thus a quantum
feld simulator can lead to insights. Here we demonstrate such a quantum feld
simulator in a two-dimensional Bose–Einstein condensate with a confgurable trap6,7
and adjustable interaction strength to implement this model system. We explicitly
show the realization of spacetimes with positive and negative spatial curvature by
wave-packet propagation and observe particle-pair production in controlled
power-law expansion of space, using Sakharov oscillations to extract amplitude
and phase information of the produced state. We fnd quantitative agreement with
analytical predictions for diferent curvatures in time and space. This benchmarks
and thereby establishes a quantum feld simulator of a new class. In the future,
straightforward upgrades ofer the possibility to enter unexplored regimes that give
further insight into relativistic quantum feld dynamics.
We report on cosmological N-body
simulations which run over up to 4
supercomputers across the globe. We
achieved to run simulations on 60 to 750
cores distributed over a variety of
supercomputers. Regardless of the
network latency of 0.32 s and the
communication over 30.000 km of optical
network cable we are able to achieve up
to 92% of the performance compared to
an equal number of cores on a single
supercomputer.
This is a Powerpoint for basic understanding regarding Molecular dynamics and NAMD simulation to providing basic information, schematic representation, to understanding the mechanism or process of molecular dynamics ( MD), and NAMD simulation brief discussion.
Optimization of technological process to decrease dimensions of circuits xor ...ijfcstjournal
The paper describes an approach of increasing of integration rate of elements of integrated circuits. The
approach has been illustrated by example of manufacturing of a circuit XOR. Framework the approach one
should manufacture a heterostructure with specific configuration. After that several special areas of the
heterostructure should be doped by diffusion and/or ion implantation and optimization of annealing of dopant
and/or radiation defects. We analyzed redistribution of dopant with account redistribution of radiation
defects to formulate recommendations to decrease dimensions of integrated circuits by using analytical
approaches of modeling of technological process.
Biological Apps: Rapidly Converging Technologies for Living Information Proce...Natalio Krasnogor
This is a plenary talk I gave at the 2018 International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems in Cadiz, Spain
Plenary Speaker slides at the 2016 International Workshop on Biodesign Automa...Natalio Krasnogor
In this talk I discuss recent work done in my lab and with collaborators abroad that contributes towards accelerating the specify -> design -> model -> build -> test & iterate biological engineering cycle. This will describe advances in biological programming languages for specifying combinatorial DNA libraries, the utilisation of off-the-shelf microfluidic devices to build the DNA libraries as well as data analysis techniques to accelerate computational simulations
Memetic Algorithms have become one of the key methodologies behind solvers that are capable of tackling very large, real-world, optimisation problems. They are being actively investigated in research institutions as well as broadly applied in industry. In this talk we provide a pragmatic guide on the key design issues underpinning Memetic Algorithms (MA) engineering. We begin with a brief contextual introduction to Memetic Algorithms and then move on to define a Pattern Language for MAs. For each pattern, an associated design issue is tackled and illustrated with examples from the literature. We then fast forward to the future and mention what, in our mind, are the key challenges that scientistis and practitioner will need to face if Memetic Algorithms are to remain a relevant technology in the next 20 years.
Darwin’s Magic: Evolutionary Computation in Nanoscience, Bioinformatics and S...Natalio Krasnogor
In this talk I will overview ten years of research in the application of evolutionary computation ideas in the natural sciences. The talk will take us on a tour that will cover problems in nanoscience, e.g. controlling self-‐organizing systems, optimizing scanning probe microscopy, etc., problems arising in bioinformatics, such as predicting protein structures and their features, to challenges emerging in systems and synthetic biology. Although the algorithmic solutions involved in these problems are different from each other, at their core, they retain Darwin’s wonderful insights. I will conclude the talk by giving a personal view on why EC has been so successful and where, in my mind, the future lies.
These slides were used for a tutorial I gave at GECCO 2010. These are similar, yet not identical, to the other tutorials. The keynote file is too large for slideshare but if anybody needs the original I would be happy to provide a url from where to download it.
Integrative analysis of transcriptomics and proteomics data with ArrayMining ...Natalio Krasnogor
These slides are part of a presentation I gave on March 2010 at the BioInformatics and Genome Research Open Club at the Weizmann Institute of Science, Israel.
In these slides my student and I describe two web-applications for microarray and gene/protein set analysis,
ArrayMining.net and TopoGSA. These use ensemble and consensus methods as well as the
possibility of modular combinations of different analysis techniques for an integrative view of
(microarray-based) gene sets, interlinking transcriptomics with proteomics data sources. This integrative process uses tools from different fields, e.g. statistics, optimisation and network
topological studies. As an example for these integrative techniques, we use a microarray
consensus-clustering approach based on Simulated Annealing, which is part of the ArrayMining.net
Class Discovery Analysis module, and show how this approach can be combined in a modular
fashion with a prior gene set analysis. The results reveal that improved cluster validity indices can be obtained by merging the two methods, and provide pointers to distinct sub-classes within pre-defined tumour categories for a breast cancer dataset by the Nottingham Queens Medical Centre.
In the second part of the talk, I show how results from a supervised
microarray feature selection analysis on ArrayMining.net can be investigated in further detail with
TopoGSA, a new web-tool for network topological analysis of gene/protein sets mapped on a
comprehensive human protein-protein interaction network. I discuss results from a TopoGSA
analysis of the complete set of genes currently known to be mutated in cancer.
A Genetic Programming Challenge: Evolving the Energy Function for Protein Str...Natalio Krasnogor
In this talk I introduce a computational challenge for GP researchers, namely, the automated synthesis of energy functions for protein structure prediction.
Building Executable Biology Models for Synthetic BiologyNatalio Krasnogor
The leveraging of today's unprecedented capability to manipulate biological systems by state-of-the-art computational, mathematical and engineering techniques , may profoundly affect the way we approach the solution to pressing grand challenges such as the development of sustainable green energy, next generation healthcare, etc. The conceptual cornerstone of Synthetic Biology a field very much on its infancy- is that methodologies commonly used to design and construct non-biological artefacts (e.g. computer programs, airplanes, bridges, etc) might also be mastered to create designer living entities. Computational methods for modeling in Synthetic Biology consist of a list of instructions detailing an algorithm that can be executed and whose computation resembles the behavior of the biological system under study. This computational approach to modelling biological systems has been termed executable biology. In this talk I will describe current approaches for the automated generation and testing of executable biology models for synthetic biology.
This was a colloquioum talk at the Computer Science Department, Ben-Gurion University of the Negev, Israel (30/June/2009)
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimen...Natalio Krasnogor
In this talk we demonstrate an ECGA and LCS pipeline for reducing protein alphabets from the standard 20 to 5 or less symbols without significant loss of information. The pipeline tailors the reduction to different problems thus resulting on different optimal minimal alphabets.
Evolutionary Algorithms for Self-Organising SystemsNatalio Krasnogor
Talk I gave at Ben Gurion University of the Negev in Israel on the 24rd/June/2009. These are a series of talks for the period in which I visited BGU as a distinguished visiting scientist
ANAMOLOUS SECONDARY GROWTH IN DICOT ROOTS.pptxRASHMI M G
Abnormal or anomalous secondary growth in plants. It defines secondary growth as an increase in plant girth due to vascular cambium or cork cambium. Anomalous secondary growth does not follow the normal pattern of a single vascular cambium producing xylem internally and phloem externally.
Toxic effects of heavy metals : Lead and Arsenicsanjana502982
Heavy metals are naturally occuring metallic chemical elements that have relatively high density, and are toxic at even low concentrations. All toxic metals are termed as heavy metals irrespective of their atomic mass and density, eg. arsenic, lead, mercury, cadmium, thallium, chromium, etc.
What is greenhouse gasses and how many gasses are there to affect the Earth.moosaasad1975
What are greenhouse gasses how they affect the earth and its environment what is the future of the environment and earth how the weather and the climate effects.
ESR spectroscopy in liquid food and beverages.pptxPRIYANKA PATEL
With increasing population, people need to rely on packaged food stuffs. Packaging of food materials requires the preservation of food. There are various methods for the treatment of food to preserve them and irradiation treatment of food is one of them. It is the most common and the most harmless method for the food preservation as it does not alter the necessary micronutrients of food materials. Although irradiated food doesn’t cause any harm to the human health but still the quality assessment of food is required to provide consumers with necessary information about the food. ESR spectroscopy is the most sophisticated way to investigate the quality of the food and the free radicals induced during the processing of the food. ESR spin trapping technique is useful for the detection of highly unstable radicals in the food. The antioxidant capability of liquid food and beverages in mainly performed by spin trapping technique.
DERIVATION OF MODIFIED BERNOULLI EQUATION WITH VISCOUS EFFECTS AND TERMINAL V...Wasswaderrick3
In this book, we use conservation of energy techniques on a fluid element to derive the Modified Bernoulli equation of flow with viscous or friction effects. We derive the general equation of flow/ velocity and then from this we derive the Pouiselle flow equation, the transition flow equation and the turbulent flow equation. In the situations where there are no viscous effects , the equation reduces to the Bernoulli equation. From experimental results, we are able to include other terms in the Bernoulli equation. We also look at cases where pressure gradients exist. We use the Modified Bernoulli equation to derive equations of flow rate for pipes of different cross sectional areas connected together. We also extend our techniques of energy conservation to a sphere falling in a viscous medium under the effect of gravity. We demonstrate Stokes equation of terminal velocity and turbulent flow equation. We look at a way of calculating the time taken for a body to fall in a viscous medium. We also look at the general equation of terminal velocity.
hematic appreciation test is a psychological assessment tool used to measure an individual's appreciation and understanding of specific themes or topics. This test helps to evaluate an individual's ability to connect different ideas and concepts within a given theme, as well as their overall comprehension and interpretation skills. The results of the test can provide valuable insights into an individual's cognitive abilities, creativity, and critical thinking skills
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
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The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems.
Exposé invité Journées Nationales du GDR GPL 2024
Travis Hills' Endeavors in Minnesota: Fostering Environmental and Economic Pr...Travis Hills MN
Travis Hills of Minnesota developed a method to convert waste into high-value dry fertilizer, significantly enriching soil quality. By providing farmers with a valuable resource derived from waste, Travis Hills helps enhance farm profitability while promoting environmental stewardship. Travis Hills' sustainable practices lead to cost savings and increased revenue for farmers by improving resource efficiency and reducing waste.
Travis Hills' Endeavors in Minnesota: Fostering Environmental and Economic Pr...
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
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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
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phys. J. 22,169 (1993).
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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*
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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
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
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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