Decoding Kotlin - Your guide to solving the mysterious in Kotlin.pptx
Novel In Vivo Concentration Detector
1.
Novel
In
Vivo
Lead
Concentration
Detector
Proposal
By:
Gerard
Trimberger
and
Felix
Ekness
June
2,
2012
Abstract
The
field
of
synthetic
biology
has
its
sights
set
on
designing
and
constructing
new
biological
functions
and
systems
not
found
in
nature.
Because
of
this,
we
are
proposing
a
novel
genetic
circuit
that
would
be
in
Escherichia
coli
(E.
coli)
that
would
detect
safe
and
harmful
lead
concentrations
within
liquid
samples.
This
novel
genetic
circuit
is
designed
so
that
phenotype
changes
within
E.
coli
will
represent
the
degree
of
biological
safety
of
liquid
samples
with
respect
to
aqueous
lead
concentrations.
The
proposed
genetic
circuit
utilizes
already
designed
lead
binding
proteins
and
lead
binding
protein
promoters
as
well
as
commonly
used
metabolite
signals,
fluorescent
reports,
and
terminator
sequences.
Although
actual
construction
of
the
lead
concentration
detector
genetic
circuit
isn’t
feasible
yet,
through
simulating
the
proposed
kinetics
of
the
circuit,
it
can
be
seen
that
the
genetic
circuit
could
be
possible
given
the
correct
biological
parts.
3.
3
Introduction
to
Synthetic
Biology
Before
the
age
of
digital
computers,
man
lived
a
simple
life.
Science
was
primarily
a
pencil
and
paper
type
of
exploration
with
observations
of
the
natural
world
deriving
from
actual
observations
of
nature.
Digital
computers
changed
all
of
this.
Currently
almost
all
complex
calculations,
modeling,
and
observations
are
aided
by
digital
computers.
It
was
predicted
by
Intel
co-‐founder
Gorgon
E.
Moore
that
the
number
of
transitions
that
can
be
placed
inexpensively
on
an
integrated
circuit
would
double
every
two
years
[1].
Since
Moore’s
law
was
realized
in
1965,
transistors
per
area
have
been
increasing
in
line
with
the
law’s
predictions,
giving
way
to
an
exponential
increase
in
computing
power
over
the
past
few
decades.
This
increase
in
computing
power
has
given
scientists
the
ability
to
effortlessly
create
numerical
models
of
complex
natural
processes,
shedding
new
insights
into
traditionally
difficult
to
explore
areas.
In
1990
the
Human
Genome
Project
(HGP)
was
announced
[2].
This
project
aimed
to
sequence
all
of
the
genes
of
the
human
genome.
Without
the
aid
of
digital
computers,
the
project
would
have
been
near
impossible.
It
was
expected,
at
the
time,
to
take
15
years
of
work
but
the
project
finished
in
2003,
2
years
early
[2].
The
early
completing
of
the
HGP
can
be
partly
attributed
to
the
exponential
increase
in
computing
power
between
1990
and
2003.
Since
that
time,
biologists
have
been
harnessing
digital
computers
more
and
more
to
help
acquire
data,
model
biological
processes,
sequence
organisms,
and
clone
DNA
and
RNA.
This
increase
in
digital
computing
power
and
prevalence
of
digital
computers
in
the
biology
community
has
given
way
to
a
new
field:
synthetic
biology.
Synthetic
biology
is
a
relatively
new
field
that
focuses
on
designing
and
constructing
new
biological
functions
and
systems
not
found
in
nature.
Without
digital
computers,
synthetic
biology
wouldn’t
be
the
field
it
is
today.
Computer
programs,
such
as
Fold
It
(a
numerical
modeling
program
for
proteins),
have
been
integral
to
synthetic
biologists’
understand
of
tertiary
and
quaternary
structures
of
normally
occurring,
as
well
as
engineered,
proteins
and
enzymes.
Natural
and
engineered
enzymatic
and
gene
pathways
are
actively
being
modeled
with
programs
such
as
MatLab,
Mathematica,
and
Jarnac.
Together,
the
use
of
these
modeling
programs
has
lead
to
quantization
of
traditionally
qualitative
biological
processes
and
functions.
Because
of
this,
the
field
of
biology
has
become
more
of
a
quantitative
science
as
well
as
leading
many
to
question
nature’s
autonomy.
Due
to
how
computers
have
shaped
the
field
synthetic
biology
thus
far,
many
synthetic
biologists
believe
that
through
the
use
of
computers
the
field
will
be
able
to
characterize
biology
to
the
point
where
the
construction
of
novel
genetic
circuits/pathways
within
organisms
is
as
straightforward
as
electrical
engineers
utilizing
capacitors,
resistors,
and
inductors
in
building
complex
electrical
circuits.
It
has
been
electrical
engineers
up
to
this
point
building
computers
but
as
Moore’s
law
becomes
increasingly
more
difficult
to
satisfy,
new
types
of
machinery
will
be
required,
some
of
which
is
bound
to
come
from
the
field
of
synthetic
biology.
Project
Overview
Aqueous
lead
is
a
major
problem
around
the
world.
When
lead
is
ingested
by
humans,
both
neurological
and
severe
tissue
damage
can
occur.
Although
lead
test
kits
are
readily
available
in
the
market
for
relatively
cheap
prices,
to
create
a
biologic
test
for
lead
in
bacteria
or
micro-‐organism
eukaryotes
would
yield
even
cheaper
tests
and
would
act
as
a
proof
of
concept
for
engineering
complex
genetic
circuits
within
bacteria
and/or
micro-‐organism
eukaryotes.
The
proposed
project
is
to
build
a
novel
genetic
circuit
within
Escherichia
coli
(E.
coli)
that
enables
lead
(Pb2+)
concentration
detection
within
liquid
environments.
The
circuit
is
designed
to
allow
varying
concentrations
of
lead
to
be
detected
in
liquid
samples
through
phenotypic
changes
in
the
E.
coli.
By
4.
4
visualizing
the
relative
levels
of
lead
within
sampled
liquids,
accurate
decisions
can
be
made
about
whether
or
not
the
liquids
are
safe
for
human
consumption.
With
the
creation
of
this
novel
genetic
circuit,
it
is
hoped
that
humans
will
gain
one
more
tool
in
monitoring
the
safety
of
their
environment.
Product
Design
Specifications
The
proposed
novel
genetic
lead
concentration
detector
circuit
works
within
E.
coli
that
is
in
a
liquid
environment.
Depending
on
the
initial
concentration
of
lead
imported
into
the
E.
coli,
one
of
two
incoherent
feed
forward
networks
will
activate
causing
a
regulated
double
negative
feedback
network
to
activate
one
of
two
fluorescence
outputs.
Once
activated,
the
fluorescent
output
will
auto
regulate
itself
to
stay
activated
until
the
E.
coli
runs
out
of
nutrients.
Only
concentrations
of
lead
that
exceed
harmful
levels
will
cause
the
E.
coli
to
fluoresce
red
while
lower
non-‐harmful
levels
of
lead
will
cause
the
E.
coli
to
fluoresce
green.
If
no
to
very
little
amounts
of
lead
are
present
in
the
liquid
sample,
the
E.
coli
will
not
fluoresce.
Internal
Design
Specifications
A) Design
Overview
Overview
The
engineered
lead
concentration
detector
circuit
is
comprised
of
a
concentration
detector,
a
memory
unit,
and
a
fluorescence
reporter
(Figure
1).
As
a
whole,
these
components
are
comprised
of
three
main
modules,
and
two
submodules:
two
incoherent
feedforward
networks
(concentration
detector),
a
regulated
double
negative
feedback
network
(memory
unit),
and
two
positive
autoregulation
modules
(signal
amplifying
fluorescent
reporters).
Figure
1
–
Component
overview
of
the
proposed
lead
concentration
detector
genetic
circuit
Concentration
Detector
The
circuit
will
activate
from
the
binding
of
Pb2+
molecules
to
lead
binding
proteins,
forming
lead-‐
binding
protein
dimers
(LBPD).
These
formed
dimers
act
to
bind
to
specially
designed
promoters
that
enable
transcription
of
two
initial
substrates
(S
and
P)
that
are
interfaced
with
the
designed
circuit
in
Figure
2.
It
can
be
seen
from
Figure
2
that
the
two
main
motifs
that
initial
substrates
S
and
P
interact
with
are
incoherent
feedforward
networks
A
and
B.
Incoherent
feedforward
networks
only
activate
when
an
initial
substrate
concentration
is
at
or
above
a
given
threshold
value
(threshold
value
dependent
on
network
tuning).
In
the
case
of
incoherent
feedforward
networks
A
and
B,
network
A
will
produce
S2
only
for
high
concentrations
of
initial
substrate
S
while
network
B
will
produce
P2
at
a
lower
initial
substrate
concentration
of
P.
Since
initial
substrates
S
and
P
are
equally
produced
from
the
transcription
initiated
by
the
binding
of
the
lead
protein
dimer
to
the
lead
binding
promoter
([S]
=
[P]),
network
A
will
be
active
when
network
B
is
active
but
when
B
is
active
A
will
not
be
(side
effect
of
Figure
2
–
Circuit
diagram
for
the
concentration
detector
module
5.
5
differing
activation
thresholds).
To
make
these
two
network
motifs
act
as
a
concentration
detector,
network
A‘s
product
must
inhibit
B’s
product,
causing
either
A
(high
initial
substrate
concentration
activation)
or
B
(lower
initial
substrate
concentration
activation)
to
produce
a
product
at
any
one
point
in
time.
With
this
in
effect,
networks
A
and
B
act
as
a
concentration
detector
for
lead.
Memory
Unit
In
order
to
produce
a
high
fidelity
visual
representation
of
the
concentration
of
lead
within
the
liquid
sample,
a
decision
must
be
made
within
the
gene
circuit.
The
regulated
double
negative
feedback
module
will
receive
the
signal
from
the
two
concentration
detectors,
and
will
decide
which
signal
to
transmit
to
the
fluorescence
reporter
module.
Depending
on
the
concentration
of
LBPD,
either
protein
S2
or
P2
will
be
produced
by
the
concentration
detector
module.
If
the
concentration
of
substrate
is
high,
above
the
“high
concentration”
threshold,
S2
will
be
produced,
however
if
the
concentration
of
the
substrate
is
low,
below
the
“high
concentration
”
threshold
but
above
zero,
P2
will
be
produced.
These
input
signals
will
activate
the
transcription
of
a
secondary
species,
either
S3
or
P3
depending
on
the
concentration
of
the
input
molecules.
This
set
of
species
will
activate
the
transcription
of
a
tertiary
species,
S4
or
P4,
and
inhibit
the
transcription
of
its
compliment
species
(i.e.
S3
will
activate
S4
production
and
repress
P4
production;
P3
will
activate
P4
production
and
inhibit
S4
production).
The
accumulation
of
either
tertiary
species,
S4
or
P4,
will
continuously
repress
the
production
of
the
other
unless
a
stimulus
is
great
enough
to
reverse
it.
In
this
way,
the
regulated
double
negative
feedback
module
will
act
as
a
memory
unit
that
remembers
which
tertiary
signal
it
should
display
given
an
input
signal
of
S2
or
P2.
Signal
Amplifying
Fluorescent
Reporter
Depending
on
the
upstream
effects,
one
of
the
tertiary
species
S4
or
P4
will
be
found
in
abundance.
This
species
will
then
be
amplified
via
its
auto
regulation
pathway
which
also
compliments
the
memory
unit
module
through
complete
inhibition
of
the
transcription
of
its
compliment
species
(i.e.
S4
will
self-‐replicate
and
shut
down
P4
or
P4
will
self-‐replicate
and
shut
down
S4
production).
In
order
to
visually
display
the
results
of
the
concentration
detector
module,
the
tertiary
species
will
activate
the
transcription
of
a
fluorescent
protein.
Red
fluorescent
protein
(RFP)
will
be
used
to
visually
represent
high
concentrations
of
lead.
Transcription
of
RFP
will
be
activated
by
tertiary
species
S4.
The
presence
of
low
concentrations
of
lead
will
be
designated
by
the
production
of
green
fluorescent
protein
(GFP),
which
will
be
activated
by
tertiary
species
P4.
If
no
lead
is
found
within
the
liquid,
neither
fluorescent
reporter
will
be
produced.
In
this
way,
the
auto
regulation
module
displays
the
behavior
of
a
single
amplifying
fluorescent
reporter.
Thus,
the
E.
Coli
will
continuously
present
its
detection
level,
ignoring
minimal
fluctuations
in
the
concentration
of
lead,
given
an
initial
concentration
of
lead.
Figure
3
-‐
Circuit
diagram
for
the
memory
unit
module
Figure
4
-‐
Circuit
diagram
for
the
signal
amplifying
fluorescence
module
6.
6
B) Specification
of
the
Proposed
Kinetic
Responses
Concentration
Detector
The
kinetics
of
the
concentration
detector
module
is
assumed
to
contain
both
mass-‐action
kinetics
and
Michaelis-‐Menten
kinetics.
The
activation
of
species
S1
and
P1
will
be
governed
by
the
Michaelis-‐Menten
equation
for
activation
based
on
the
concentration
of
the
LBPD:
𝑣 = (𝑉!"# ∗ 𝐿𝐵𝑃𝐷!
)/(𝐾! + 𝐿𝐵𝑃𝐷!
)
where
LBPD
represents
the
concentration
of
the
lead
binding
protein
dimer.
No
cooperativity
of
the
enzyme
is
assumed
in
this
particular
case;
therefore
the
hill
coefficient
of
this
reaction,
n,
is
expected
to
be
one.
The
production
of
species
S2
is
governed
by
mass
action
kinetics
as
well,
which
is
activated
by
LBPD
and
repressed
by
S1.
Therefore
the
appropriate
reaction
rate
for
S2
production
is
assumed
to
be:
𝑣 = (𝑘 ∗ 𝐿𝐵𝑃𝐷)/(1 + 𝑘 ∗ 𝐿𝐵𝑃𝐷 + 𝑘! ∗ 𝑆! + 𝑘 ∗ 𝑘! ∗ 𝐿𝐵𝑃𝐷 ∗ 𝑆!)
where
k
and
k1
are
set
to
a
value
of
one
to
simplify
the
kinetics.
The
production
of
species
P2
is
slightly
more
complicated
than
S2
due
to
the
additional
repression
by
species
S2.
Therefore,
the
reaction
rate
for
P2
production
is
presumed
to
follow
mass-‐action
kinetics
by
the
following
equation:
𝑣 = (𝑘 ∗ 𝐿𝐵𝑃𝐷)/(1 + 𝑘 ∗ 𝐿𝐵𝑃𝐷 + 𝑘! ∗ 𝑃! + 𝑘! ∗ 𝑆! + 𝑘 ∗ 𝑘! ∗ 𝐿𝐵𝑃𝐷 ∗ 𝑃!
+𝑘 ∗ 𝑘! ∗ 𝐿𝐵𝑃𝐷 ∗ 𝑆! + 𝑘! ∗ 𝑘! ∗ 𝑃! ∗ 𝑆! + 𝑘 ∗ 𝑘! ∗ 𝑘! ∗ 𝐿𝐵𝑃𝐷 ∗ 𝑃! ∗ 𝑆!)
Again
the
kinetic
constant
k
is
assumed
to
be
one,
but
the
kinetic
constant
k1
is
assumed
to
be
greater
to
enable
adequate
repression
of
the
production
P2
with
increased
concentrations
of
P1.
The
constant
k2
is
assumed
to
be
0.1,
which
will
enable
repression
of
S2
by
P2
at
only
significant
levels
of
S2.
Memory
Unit
The
kinetics
of
the
regulated
double
negative
feedback
module
(memory
unit)
are
assumed
to
be
mass
action
governed.
The
transduction
of
the
signal
from
the
incoherent
feed
forward
modules
to
the
regulated
double
negative
feedback
module
needs
to
be
quick
and
simple
with
high
signal
fidelity
to
accomplish
the
functionality
of
the
double
regulated
negative
feedback
network.
Simple
linear
mass
action
kinetics
enables
this
functionality.
These
kinetics
are
expected
to
be
(i.e.
S2
to
S3
and
P2
to
P3):
S3
production:
𝑣 = (𝑘! ∗ 𝑆!)
P3
production:
𝑣 = (𝑘! ∗ 𝑃!)
where
the
kinetic
coefficients
ks
and
kp
were
set
to
values
of
10
for
quick
reaction
response.
These
secondary
species
(i.e.
S3
and
P3)
will
influence
the
tertiary
components
(i.e.
S4
and
P4)
both
as
activators
and
repressors.
These
interactions
are
assumed
to
have
mass
action
kinetics
similar
to
those
in
the
concentration
detector.
Each
tertiary
species
will
be
activated
by
its
secondary
species
and
repressed
by
both
the
secondary
and
tertiary
species
of
its
compliment
species
(i.e.
S4
is
activated
by
S3
and
repressed
by
P3
and
P4
while
P4
is
activated
by
P3
and
repressed
by
S3
and
S4).
These
interactions
are
shown
in
the
following
equations:
S4
production:
𝑣 = (𝑘! ∗ 𝑆!)/(1 + 𝑘! ∗ 𝑆! + 𝑘! ∗ 𝑃! + 𝑘! ∗ 𝑃! + 𝑘! ∗ 𝑘! ∗ 𝑆! ∗ 𝑃!
+𝑘! ∗ 𝑘! ∗ 𝑆! ∗ 𝑃! + 𝑘! ∗ 𝑘! ∗ 𝑃! ∗ 𝑃! + 𝑘! ∗ 𝑘! ∗ 𝑘! ∗ 𝑆! ∗ 𝑃! ∗ 𝑃!)
P4
production:
𝑣 = (𝑘! ∗ 𝑃!)/(1 + 𝑘! ∗ 𝑃! + 𝑘! ∗ 𝑆! + 𝑘! ∗ 𝑆! + 𝑘! ∗ 𝑘! ∗ 𝑃! ∗ 𝑆!
+𝑘! ∗ 𝑘! ∗ 𝑃! ∗ 𝑆! + 𝑘! ∗ 𝑘! ∗ 𝑆! ∗ 𝑆! + 𝑘! ∗ 𝑘! ∗ 𝑘! ∗ 𝑃! ∗ 𝑆! ∗ 𝑆!)
where
k1
represents
the
kinetic
coefficient
for
activation
and
is
assumed
to
be
one.
k2
and
k3
represent
the
kinetic
coefficients
for
repression
and
are
assumed
to
be
greater
than
k1
to
allow
repression
of
S4
and
P4
production
to
be
greater
than
activation
of
S4
and
P4
production.
The
kinetic
coefficients
could
be
changed
for
the
different
species,
but
for
simplification
they
are
assumed
to
be
the
same
values.
7.
7
Signal
Amplifying
Fluorescent
Reporter
The
kinetics
of
the
signal
amplifying
fluorescent
reporter
are
similar
to
those
of
the
memory
unit
because
the
positive
autoregulation
of
the
species
S4
and
P4
is
assumed
to
be
repressed
by
the
secondary
and
tertiary
species
of
the
species
compliment
(i.e.
the
positive
autoregulation
of
S4
was
repressed
by
P3
and
P4
while
the
positive
autoregulation
of
P4
is
expected
to
be
repressed
by
the
presence
of
species
S3
and
S4).
Similar
to
the
memory
unit
these
reaction
rates
are
assumed
to
follow
mass-‐action
kinetics
and
are
simulated
by
the
following
equations:
S4
positive
autoregulation:
𝑣 = (𝑘! ∗ 𝑆!)/(1 + 𝑘! ∗ 𝑆! + 𝑘! ∗ 𝑃! + 𝑘! ∗ 𝑃! + 𝑘! ∗ 𝑘! ∗ 𝑆! ∗ 𝑃!
+𝑘! ∗ 𝑘! ∗ 𝑆! ∗ 𝑃! + 𝑘! ∗ 𝑘! ∗ 𝑃! ∗ 𝑃! + 𝑘! ∗ 𝑘! ∗ 𝑘! ∗ 𝑆! ∗ 𝑃! ∗ 𝑃!)
P4
positive
autoregulation:
𝑣 = (𝑘! ∗ 𝑃!)/(1 + 𝑘! ∗ 𝑃! + 𝑘! ∗ 𝑆! + 𝑘! ∗ 𝑆! + 𝑘! ∗ 𝑘! ∗ 𝑃! ∗ 𝑆!
+𝑘! ∗ 𝑘! ∗ 𝑃! ∗ 𝑆! + 𝑘! ∗ 𝑘! ∗ 𝑆! ∗ 𝑆! + 𝑘! ∗ 𝑘! ∗ 𝑘! ∗ 𝑃! ∗ 𝑆! ∗ 𝑆!)
where
the
kinetic
coefficients
for
the
different
species
could
be
represented
by
different
values
but
are
assumed
to
be
constant
for
both
species.
The
activation
coefficient,
k1,
is
set
to
a
value
of
one,
while
the
inhibition
coefficients,
k2
and
k3,
are
set
to
a
value
of
two
to
represent
repression
governing
activation.
In
this
particular
case
this
was
necessary
because
the
positive
autoregulation
is
expected
to
be
suppressed
by
the
presence
of
the
compliment
species.
The
production
of
the
fluorescent
species,
RFP
or
GFP,
are
assumed
to
be
linearly
correlated
with
their
respective
tertiary
species,
S4
or
P4,
through
mass
action
kinetics
by
the
following
equations:
RFP
production:
𝑣 = (𝑘! ∗ 𝑆!)
GFP
production:
𝑣 = (𝑘! ∗ 𝑃!)
The
kinetic
coefficients
for
these
reactions
are
assumed
to
be
at
unity
so
that
the
production
of
RFP
or
GFP
does
not
dominate
over
the
other
given
equal
S2
and
P2
concentrations.
Degradation
The
majority
of
the
species
produced
in
this
genetic
circuit
are
assumed
to
have
similar
degradation
rates.
The
degradation
for
all
species
is
assumed
to
follow
linear
mass-‐action
kinetics
by
the
following
equation:
Degradation
rates:
𝑣 = (𝑘! ∗ 𝐴!)
where
Ai
represents
all
species
in
the
genetic
circuit
(i.e.
LBPD,
S1
to
S4,
P1
to
P4,
RFP,
and
GFP).
The
kinetic
degradation
coefficient
for
all
species
besides
S4,
P4,
RFP,
and
GFP
are
assumed
to
be
a
value
of
one.
The
degradation
kinetic
coefficient
for
these
other
species
must
be
a
value
of
0.1
to
allow
for
the
signal
to
remain
within
the
E.
Coli
for
long
periods
of
time
(200+
seconds).
8.
8
Computer
Simulation
Test
Implementation
Complete
Circuit
Simulations
Using
the
kinetic
equations
for
the
concentration
detector,
memory
unit,
and
signal
amplifying
fluorescence
unit
as
well
as
the
kinetic
equations
for
degradation,
the
bellow
simulations
were
carried
out.
These
simulations
illustrate
the
projected
characteristics
of
the
proposed
novel
lead
concentration
detector
genetic
circuit
engineered
into
E.
coli.
Given
no
initial
lead
concentration,
the
circuit
does
not
turn
on
(Figure
5).
At
low
levels
of
normalized
initial
lead
concentration
(0.5
units),
the
circuit
activates,
producing
GFP
as
the
reported
molecule
to
signify
safe
initial
concentrations
of
lead
(Figure
6).
At
medium
levels
of
normalized
initial
lead
concentration
(2
units)
the
circuit
activates,
producing
RFP
to
signify
dangerous
levels
of
initial
lead
concentration
(Figure
7).
It
can
be
seen
from
this
graph
that
it
takes
longer
than
at
lower
levels
of
initial
lead
concentration
to
reach
a
steady
state
signaling
molecule
concentration,
indicating
that
the
initial
normalized
lead
concentration
is
close
to
safe
and
unsafe
levels
of
lead
concentration.
At
high
levels
of
normalized
initial
lead
concentration
(10
units),
the
circuit
activates,
producing
RFP
to
signify
dangerous
levels
of
initial
lead
concentration
(Figure
8).
Figures
5
–
8
together
illustrate
the
complete
proposed
dynamics
of
the
lead
concentration
detector
genetic
circuit.
The
Jarnac
script
used
to
generate
Figures
5
–
8
can
be
found
in
the
Jarnac
Script
section
of
the
Appendix.
Figure
5
–
With
no
initial
lead
concentration
(p.G),
the
lead
Figure
6
–
With
a
small
amount
of
initial
lead
concentration
(p.G),
the
lead
concentration
circuit
does
not
activate.
concentration
circuit
activates,
with
GFP
dominated
the
output
signal
(p.GFPa).
Figure
7
–
With
elevated
levels
of
initial
lead
concentration
(p.G)
Figure
8
–
At
high
levels
of
initial
lead
concentration
(p.G)
the
the
lead
concentration
circuit
fluoresces
red
(p.RFPa).
lead
concentration
circuit
fluoresces
red
(p.RFPa).
9.
9
Concentration
Detector
Module
Simulations
The
concentration
detector
module
makes
up
the
decision
making
portion
of
the
lead
concentration
detector
genetic
circuit.
It
can
be
seen
from
the
first
peak
in
Figure
9
that
at
low
levels
of
normalized
initial
lead
concentrations
(0.5
units)
production
of
P2
is
higher
than
S2
(p.P2a
and
p.S2a
respectively).
The
greater
production
of
P2
translates
to
GFP
production
in
the
finalized
circuit
(Figure
6).
At
normalized
initial
lead
concentrations
of
2
units,
production
of
P2
and
S2
are
very
similar,
with
S2
just
barely
out
producing
P2
(second
peak
in
Figure
9).
This
slightly
greater
production
of
P2
leads
to
RFP
production
from
the
circuit
as
a
whole
(Figure
7).
At
normalized
initial
lead
concentrations
of
10
units,
signifying
dangerous
levels
of
initial
lead
concentration,
S2
production
largely
out
weighs
P2
production
(third
peak
in
Figure
9),
which
leads
to
the
quick
reach
of
steady
state
production
of
RFP
in
the
completed
circuit
(Figure
8).
The
equations
used
to
simulate
these
proposed
characteristics
of
the
concentration
detector
module
are
those
found
in
Internal
Design
Specifications
section.
The
Jarnac
scrip
for
these
simulations
can
be
found
in
the
Jarnac
Scrip
section
of
the
Appendix.
Memory
Unit
Module
Simulations
The
memory
unit
module
acts
as
a
temporary
state
chooser.
When
S3
dominates
P3,
the
production
of
S4
occurs
while
no
production
of
P4
is
seen
(first
peak
in
Figure
10).
The
opposite
is
also
true,
if
the
concentration
of
P3
is
greater
than
S3,
P4
is
produced
while
no
S4
is
produced
(second
peak
in
Figure
10).
The
equations
used
to
simulate
these
proposed
characteristics
of
the
memory
unit
are
those
found
in
Internal
Design
Specifications
section.
The
Jarnac
scrip
for
these
simulations
can
be
found
in
the
Jarnac
Script
section
of
the
Appendix.
Signal
Amplifying
Fluorescent
Reporter
Module
Simulations
The
signal
amplifying
fluorescent
reporter
module
causes
the
“decisions”
that
the
memory
unit
module
makes
to
become
permanent.
When
a
decision
is
made
by
the
memory
unit
the
corresponding
output
molecule
S4
or
P4
becomes
constitutively
produced
from
the
autoregulation
inherent
within
this
module
(Figure
4).
It
can
be
seen
from
Figure
11
that
when
P4
is
produced,
it
autoregulates
itself
to
saturation.
The
same
is
true
for
S4
and
can
be
seen
in
Figure
12.
This
autoregulation
is
tied
to
fluorescence,
causing
saturated
P4
concentrations
to
enable
large
amounts
of
GFP
production
as
well
as
saturated
S4
concentrations
enables
large
amounts
of
RFP
production.
In
this
manner,
the
signal
amplifying
fluorescent
reporter
module
acts
as
a
final
memory
unit
and
reporter
of
the
initial
lead
concentration.
The
equations
used
to
simulate
these
proposed
characteristics
of
the
signal
amplifying
fluorescent
Figure
9
-‐
Concentration
detector
module
simulations;
the
peaks
correspond
to
low
(0.5
u),
medium
(2
u,)
and
high
(10
u)
normalized
initial
concentrations
of
lead
respectively.
Figure
10
–
Memory
unit
simulations
illustrating
that
when
one
initial
substrate
(p.S3
or
p.P3)
is
greater
than
the
other
(p.S4a(green)
peak
corresponds
to
p.S3
>
p.P3
and
p.P4a(purple))
a
spike
in
the
corresponding
reporter
molecule
occurs.
10.
10
reporter
module
are
those
found
in
Internal
Design
Specifications
section.
The
Jarnac
script
for
these
simulations
can
be
found
in
the
Jarnac
Script
section
of
the
Appendix.
Figure
11
–
At
elevated
levels
of
P4
(p.P4a),
it
self
regulates
itself
Figure
12
-‐
At
elevated
levels
of
S4
(p.S4a),
it
self
regulates
itself
to
saturation.
to
saturation.
Implementation
Details
Some
of
the
parts
that
could
be
used
to
build
this
lead
concentration
detector
genetic
circuit
are:
Name:
BioBrick
ID:
Description:
Length:
*Cost:
Genes:
Lead
Binding
Protein
BBa_I721002
This
gene
expresses
a
protein
that
forms
a
protein
dimer
with
Pb2+.
Useful
in
initiating
transcription
of
initial
substrates.
399
bp
$199.50
Superfolder
GFP
(sfGFP)
BBa_I746916
This
gene
expresses
sfGFP
that
acts
as
a
reporter
protein.
Useful
in
reporting
safe
concentrations
of
aqueous
lead.
720
bp
$360.00
mCherry
(RFP)
BBa_K180008
This
gene
expresses
a
form
of
RFP
that
acts
as
a
reporter
protein.
Useful
in
reporting
dangerous
concentrations
of
aqueous
lead.
708
bp
$356.00
Promoters
Lead
Binding
Promoter
BBa_I721001
This
coding
sequence
allows
for
the
lead
binding
protein-‐dimer
to
bind
to
DNA
and
instigate
transcription.
Useful
in
initiating
transcription
of
initial
substrates.
94
bp
$47.00
LacI
Regulated
Promoter
BBa_R0010
This
promoter
allows
for
transcription
inhibition
caused
by
LacI
and
CAP.
Will
be
useful
in
negative
feedback
loops
200
bp
$100.00
Terminators
T1
from
E.
coli
rrnB
BBa_B0010
This
DNA
sequence
initiates
transcription
termination.
Useful
in
stopping
transcription
at
desired
areas.
64
bp
$32.00
*Cost
was
calculated
based
off
of
50
cents
per
base
pair
**Total
cost
for
all
parts
listed
above:
$1,094.50
***Total
length
of
proposed
genetic
circuit
would
be
>
3000
bp
†**
All
parts
found
within
the
Standard
parts
registry
[3]
11.
11
Appendix
Device
Pricing
in
2025
Employees:
12
people
at
$120,000/year
Fixed
Costs:
Building,
Electricity,
Water,
etc.
=
$1,000,000/year
Estimated
Market
Size:
1000
units/year
12 𝑝𝑒𝑜𝑝𝑙𝑒 ∗
$120,000
𝑝𝑒𝑜𝑝𝑙𝑒 𝑎 𝑦𝑒𝑎𝑟
+
$1,000,000
𝑦𝑒𝑎𝑟
=
1,000 𝑢𝑛𝑖𝑡𝑠
𝑦𝑒𝑎𝑟
∗ 𝑿
𝑃𝑟𝑖𝑐𝑒
𝑢𝑛𝑖𝑡
Thus
total
price
per
unit
=
$2,440
It
can
be
seen
from
the
above
numbers
that
in
order
for
the
company
to
break
even
given
the
expenses
and
total
units
sold
in
the
fiscal
year
of
2025,
each
unit
would
need
to
be
sold
at
$2,440.
Along
with
this,
the
actual
production
of
the
E.
coli
strain
that
harbors
the
lead
concentration
genetic
circuit
does
not
factor
into
the
total
company
expenditures,
meaning
that
as
long
as
the
price
per
unit
can
be
maintained,
the
actual
production
costs
of
the
E.
coli
strain
are
irrelevant
in
the
year
2025.
Design
Specification
Sheet
Overview
Final
schematic
of
the
lead
concentration
detector
genetic
circuit.
Module
A
and
B
comprise
the
concentration
detector
module
and
are
both
incoherent
feedforward
networks,
module
C
is
the
memory
unit
and
is
comprised
of
a
regulated
double
negative
feedback
network,
and
modules
D
and
E
comprise
the
signal
amplifying
fluorescent
reporter
module
and
are
both
autoregulation
networks.
12.
12
Bellow
are
the
simulated
responses
of
the
lead
concentration
detector
genetic
circuit
with
0.0
units,
0.5
units,
2
units,
and
10
units
of
normalized
initial
lead
concentration
(from
left
to
right)
where
production
of
GFP
(p.GFPa)
resembles
safe
concentrations
of
lead
and
production
of
RFP
(p.RFPa)
resembles
unsafe
initial
lead
concentrations.
Concentration
Detector
Bellow
is
the
schematic
diagram
for
the
concentration
detector
module
of
the
lead
concentration
detector
genetic
circuit.
Modules
A
and
B
are
incoherent
feedfoward
networks.
13.
13
Bellow
are
the
simulated
results
of
the
concentration
detector
given
0.5
units,
2
units,
and
10
units
of
normalized
initial
lead
concentration
(from
left
to
right).
Memory
Unit
Bellow
is
the
schematic
of
the
memory
unit
for
the
lead
concentration
detector
genetic
circuit,
which
is
a
double
regulated
negative
feedback
network.
14.
14
Bellow
are
the
simulated
results
of
the
memory
unit
given
greater
concentration
of
S3
or
P3
(from
left
to
right).
At
greater
initial
S3
concentrations
than
P3
concentrations,
only
S4
is
produced
(p.S4a)
and
at
greater
initial
P3
concentrations
than
S3
concentrations,
only
P4
is
produced
(p.P4a).
Signal
Amplifying
Fluorescent
Reporter
Bellow
is
the
schematic
diagram
for
the
signal
amplifying
fluorescent
reporter
module
of
the
lead
concentration
detector
genetic
circuit.
Once
either
P4
or
S4
is
produced,
it
up
regulates
itself,
causing
either
GFP
or
RFP
to
be
constitutively
produced,
respectively.
Bellow
are
the
simulated
response
of
the
signal
amplifying
fluorescent
reporter
module
for
initial
substrate
P3
being
in
greater
quantity
(left
graph)
than
S3,
and
S3
being
in
greater
initial
quantity
than
P3
(right
graph).
With
either
P3
or
S3
being
initially
produced
in
greater
quantity,
P4
or
S4
respectively
will
be
autoregulated
to
a
maximum
sustained
value
as
seen
in
the
graphs
bellow.
15.
15
Jarnac
Script
Overview
(Complete
System
Simulation)
p
=
defn
cell
$S1
-‐>
S1a;
Vm1*G/(Km1
+
G);
//
productions
of
activated
S1
given
michaelis-‐menten
kinetics
//
activation
of
S2
given
michaelis-‐menten
kinetics
with
substrate
inhibition
$S2
-‐>
S2a;
k*G/(1
+
k*G
+
ks1*S1a
+
k*ks1*S1a*G);
S1a
-‐>
$W;
S1a*d;
//
degradation
of
activated
S1
via
mass
action
S2a
-‐>
$W;
S2a*d;
//
degradation
of
activated
S2
via
mass
action
$P1
-‐>
P1a;
Vm2*G/(Km2
+
G);
//
production
of
activated
P1
given
michaelis-‐menten
kinetics
//
activation
of
S2
given
michaelis-‐menten
kinetics
with
substrate
inhibition
$P2
-‐>
P2a;
k*G/(1
+
k*G
+
kp1*P1a
+
k*kp1*P1a*G
+
ksp*S2a
+
k*kp1*ksp*P1a*G*S2a
+
kp1*ksp*P1a*S2a
+
k*ksp*G*S2a);
G
-‐>
$W;
G*d;
//
degradation
of
initial
substrate
(lead
binding
protein)
P1a
-‐>
$W;
P1a*d;
//
degradation
of
activated
P1
P2a
-‐>
$W;
P2a*d;
//
degradation
of
activated
P2
$S3-‐>
S3a;
kp*S2a;
//
production
of
activated
S3
via
mass
action
kinetics
$P3
-‐>
P3a;
ks*P2a;
//
production
of
activated
P3
via
mass
action
kinetics
//
activation
of
S4
via
michaelis-‐menten
kinetics
with
substrate
inhibition
$S4
-‐>
S4a;
(k1*S3a)/(1+k1*S3a+k2*P3a+k3*P4a+k1*k2*S3a*P3a+k1*k3*S3a*P4a+
k2*k3*P3a*P4a+k1*k2*k3*S3a*P3a*P4a);
//
activation
of
P4
via
michaelis-‐menten
kinetics
with
substrate
inhibition
$P4
-‐>
P4a;
(k4*P3a)/(1+k4*P3a+k5*S3a+k6*S4a+k4*k5*P3a*S3a+k4*k6*P3a*S4a+
k5*k6*S3a*S4a+k4*k5*k6*P3a*S3a*S4a);
S3a
-‐>
$w;
d1*S3a;
//
degradation
of
activated
S3
via
mass
action
kinetics
S4a
-‐>
$w;
d2*S4a;
//
degradation
of
activated
S4
via
mass
action
kinetics
P3a
-‐>
$w;
d3*P3a;
//
degradation
of
activated
P3
via
mass
action
kinetics
P4a
-‐>
$w;
d4*P4a;
//
degradation
of
activated
P4
via
mass
action
kinetics
//
autoregulation
production
of
activated
S4
via
michaelis-‐menten
kinetics
with
substrate
inhibition
$S4
-‐>
S4a;
(k7*S4a)/(1+k7*S4a+k8*P3a+k9*P4a+k7*k8*S4a*P3a+k7*k9*S4a*P4a+
k8*k9*P3a*P4a+k7*k8*k9*S4a*P3a*P4a);
//
autoregulation
production
of
activated
P4
via
michaelis-‐menten
kinetics
with
substrate
inhibition
$P4
-‐>
P4a;
(k10*P4a)/(1+k10*P4a+k11*S3a+k12*S4a+k10*k11*P4a*S3a+k10*k12*P4a*S4a+
k11*k12*S3a*S4a+k10*k11*k12*P4a*S3a*S4a);
$RFP-‐>
RFPa;
kr*S4a;
//
production
of
activated
RFP
via
mass
action
kinetics
$GFP
-‐>
GFPa;
kg*P4a;
//
production
of
activated
GFP
via
mass
action
kinetics
S4a
-‐>
$w;
d5*S4a;
//
additional
degradation
of
activated
S4
via
mass
action
kinetics
P4a
-‐>
$w;
d6*P4a;
//
additional
degradation
of
activated
P4
via
mass
action
kinetics
RFPa
-‐>
$w;
d7*RFPa;
//
degradation
of
activated
RFP
via
mass
action
kinetics
GFPa
-‐>
$w;
d8*GFPa;
//
degradation
of
activated
GFP
via
mass
action
kinetics
end;
//
rate
kinetics
and
initial
conditions
for
the
given
model
p.d
=
0.1;
p.Vm1
=
1;
p.Km1
=
0.5;
p.k
=
1;
p.ks1
=
1;
p.Vm2
=
1;
p.Km2
=
5;
p.kp1
=
3;
p.ksp
=
0.1;
p.ks
=
10;
p.kp
=
10;
p.k1
=
1;
p.k2
=
2;
p.k3
=
2;
p.k4
=
1;
p.k5
=
2;
p.k6
=
2;
p.d1
=
0.1;
p.d2
=
0.1;
p.d3
=
0.1;
p.d4
=
0.1;
p.kr
=
1;
p.kg
=
1;
p.k7
=
1;
p.k8
=
2;
16.
16
p.k9
=
2;
p.k10
=
1;
p.k11
=
2;
p.k12
=
2;
p.d5
=
0.1;
p.d6
=
0.1;
p.d7
=
0.1;
p.d8
=
0.1;
h1
=
10;
//
modular
time
step
interval
//
simulation
of
given
model
p.G
=
0.5;
//
0.5
units
of
normalized
initial
lead
concentration
m1
=
p.sim.eval(0,h1,50,[<p.Time>,<p.G>,<p.RFPa>,<p.GFPa>]);
p.G
=
0;
m2
=
p.sim.eval(h1,300,50,[<p.Time>,<p.G>,<p.RFPa>,<p.GFPa>]);
p.G
=
2;
//
2
units
of
normalized
initial
lead
concentration
m3
=
p.sim.eval(200,200+h1,50,[<p.Time>,<p.G>,<p.RFPa>,<p.GFPa>]);
p.G
=
0;
m4
=
p.sim.eval(200+h1,300,50,[<p.Time>,<p.G>,<p.RFPa>,<p.GFPa>]);
p.G
=
10;
//
10
units
of
normalized
initial
lead
concentration
m5
=
p.sim.eval(300,300+h1,50,[<p.Time>,<p.G>,<p.RFPa>,<p.GFPa>]);
p.G
=
0;
m6
=
p.sim.eval(300+h1,400,50,[<p.Time>,<p.G>,<p.RFPa>,<p.GFPa>]);
//
list
augmentations
m
=
augr(m1,
m2);
m
=
augr(m,
m4);
m
=
augr(m,
m5);
m
=
augr(m,
m6);
graph(m);
//graphed
simulated
results
Concentration
Detector
p
=
defn
cell
//
low
sensitivity
incoherent
feedforward
network
$S1
-‐>
S1a;
Vm1*G/(Km1
+
G);
//
activation
of
S1
via
michaelis-‐menten
kinetics
//
activation
of
S2
via
michaelis-‐menten
kinetics
with
substrate
inhibition
$S2
-‐>
S2a;
k*G/(1
+
k*G
+
ks1*S1a
+
k*ks1*S1a*G);
S1a
-‐>
$W;
S1a*d;
//
degradation
of
activated
S1
via
mass
action
kinetics
S2a
-‐>
$W;
S2a*d;
//
degradation
of
activated
S2
via
mass
action
kinetics
//
high
sensitivity
incoherent
feedforward
network
$P1
-‐>
P1a;
Vm2*G/(Km2
+
G);
//
activation
of
P1
via
michaelis-‐menten
kinetics
//
activation
of
P2
via
michaelis-‐menten
kinetics
with
substrate
inhibition
$P2
-‐>
P2a;
k*G/(1
+
k*G
+
kp1*P1a
+
k*kp1*P1a*G
+
ksp*S2a
+
k*kp1*ksp*P1a*G*S2a
+
kp1*ksp*P1a*S2a
+
k*ksp*G*S2a);
G
-‐>
$W;
G*d;
//
degradation
of
initial
lead
concentration
bound
protein
via
mass
action
kinetics
P1a
-‐>
$W;
P1a*d;
//
degradation
of
activated
P1
via
mass
action
kinetics
P2a
-‐>
$W;
P2a*d;
//
degradation
of
activated
P2
via
mass
action
kinetics
end;
//
rate
kinetics
and
initial
conditions
for
the
given
model
p.d
=
0.1;
p.Vm1
=
1;
p.Km1
=
0.5;
p.k
=
1;
p.ks1
=
1;
p.Vm2
=
1;
p.Km2
=
5;
p.kp1
=
3;
p.ksp
=
0.1;
//
modular
time
intervals
for
simulation
h1
=
10;
h2
=
10;
h3
=
10;
p.G
=
0;
//
simulation
of
given
model
m1
=
p.sim.eval(0,
100,
100,
[<p.Time>,
<p.S2a>,
<p.P2a>]);
p.G
=
0.5;
//
0.5
units
of
normalized
initial
lead
concentration
m2
=
p.sim.eval(100,
100+h1,
100,
[<p.Time>,
<p.S2a>,
<p.P2a>]);
17.
17
p.G
=
0;
m3
=
p.sim.eval(100+h1,
200,
100,
[<p.Time>,
<p.S2a>,
<p.P2a>]);
p.G
=
2;
//
2
units
of
normalized
initial
lead
concentration
m4
=
p.sim.eval(200,
200+h1,
100,
[<p.Time>,
<p.S2a>,
<p.P2a>]);
p.G
=
0;
m5
=
p.sim.eval(200+h1,
300,
100,
[<p.Time>,
<p.S2a>,
<p.P2a>]);
p.G
=
10;
//
10
units
of
normalized
initial
lead
concentration
m6
=
p.sim.eval(300,
300+h3,
100,
[<p.Time>,
<p.S2a>,
<p.P2a>]);
p.G
=
0;
m7
=
p.sim.eval(300+h3,
400,
100,
[<p.Time>,
<p.S2a>,
<p.P2a>]);
//
list
augmentations
m
=
augr(m1,m2);
m
=
augr(m,m3);
m
=
augr(m,m4);
m
=
augr(m,m5);
m
=
augr(m,m6);
m
=
augr(m,m7);
graph(m);
//
graphed
simulated
results
Memory
Unit
p
=
defn
cell
$S3-‐>
S3;
kp*S3a;
//
production
of
additional
S3
from
activated
S3
via
mass
action
kinetics
$P3
-‐>
P3;
ks*P3a;
//
production
of
additional
P3
from
activated
P3
via
mass
action
kinetics
//
activation
of
S4
via
michaelis-‐menten
kinetics
with
substrate
inhibition
$S4
-‐>
S4a;
(k1*S3a)/(1+k1*S3a+k2*P3a+k3*P4a+k1*k2*S3a*P3a+k1*k3*S3a*P4a+
k2*k3*P3a*P4a+k1*k2*k3*S3a*P3a*P4a);
//
activation
of
P4
via
michaelis-‐menten
kinetics
with
substrate
inhibition
$P4
-‐>
P4a;
(k4*P3a)/(1+k4*P3a+k5*S3a+k6*S4a+k4*k5*P3a*S3a+k4*k6*P3a*S4a+
k5*k6*S3a*S4a+k4*k5*k6*P3a*S3a*S4a);
S3a
-‐>
$w;
d1*S3a;
//degradation
of
activated
S3
via
mass
action
kinetics
S4a
-‐>
$w;
d2*S4a;
//degradation
of
activated
S4
via
mass
action
kinetics
P3a
-‐>
$w;
d3*P3a;
//degradation
of
activated
P3
via
mass
action
kinetics
P4a
-‐>
$w;
d4*P4a;
//degradation
of
activated
P4
via
mass
action
kinetics
end;
//
rate
kinetics
and
initial
conditions
for
the
given
model
p.ks
=
10;
p.kp
=
10;
p.k1
=
1;
p.k2
=
2;
p.k3
=
2;
p.k4
=
1;
p.k5
=
2;
p.k6
=
2;
p.d1
=
0.1;
p.d2
=
0.1;
p.d3
=
0.1;
p.d4
=
0.1;
p.S3
=
0;
p.P3
=
0;
//
modular
time
intervals
for
simulation
h1
=
10;
h2
=
10;
//
simulation
of
given
model
m1
=
p.sim.eval(0,100,50,[<p.Time>,<p.S3>,<p.P3>,<p.S4a>,<p.P4a>]);
p.S3a
=
2;
//
initial
substrate
of
activated
S3
fed
into
the
memory
unit
m2
=
p.sim.eval(100,100+h1,50,[<p.Time>,<p.S3>,<p.P3>,<p.S4a>,<p.P4a>]);
p.S3a
=
0;
m3
=
p.sim.eval(100+h1,200,50,[<p.Time>,<p.S3>,<p.P3>,<p.S4a>,<p.P4a>]);
p.P3a
=
2;
//
initial
substrate
fed
of
activated
P3
into
the
memory
unit
m4
=
p.sim.eval(200,200+h2,50,[<p.Time>,<p.S3>,<p.P3>,<p.S4a>,<p.P4a>]);
p.P3a
=
0;
m5
=
p.sim.eval(200+h2,300,50,[<p.Time>,<p.S3>,<p.P3>,<p.S4a>,<p.P4a>]);
//
list
augmentations
m
=
augr(m1,
m2);
m
=
augr(m,
m3);
m
=
augr(m,
m4);
m
=
augr(m,
m5);
graph(m);
//graphed
simulated
results
18.
18
Signal
Amplifying
Fluorescent
Reporter
p
=
defn
cell
//
activation
of
S4
via
michaelis-‐menten
kinetics
with
substrate
inhibition
$S4
-‐>
S4a;
(k7*S4a)/(1+k7*S4a+k8*P3a+k9*P4a+k7*k8*S4a*P3a+k7*k9*S4a*P4a+
k8*k9*P3a*P4a+k7*k8*k9*S4a*P3a*P4a);
//
activation
of
P4
via
michaelis-‐menten
kinetics
with
substrate
inhibition
$P4
-‐>
P4a;
(k10*P4a)/(1+k10*P4a+k11*S3a+k12*S4a+k10*k11*P4a*S3a+k10*k12*P4a*S4a+
k11*k12*S3a*S4a+k10*k11*k12*P4a*S3a*S4a);
$RFP-‐>
RFPa;
kr*S4a;
//
activation
of
RFP
via
mass
action
kinetics
$GFP
-‐>
GFPa;
kg*P4a;
//
activation
of
GFP
via
mass
action
kinetics
S4a
-‐>
$w;
d5*S4a;
//
degradation
of
activated
S4
via
mass
action
kinetics
P4a
-‐>
$w;
d6*P4a;
//
degradation
of
activated
P4
via
mass
action
kinetics
RFPa
-‐>
$w;
d7*RFPa;
//
degradation
of
activated
RFP
via
mass
action
kinetics
GFPa
-‐>
$w;
d8*GFPa;
//
degradation
of
activated
GFP
via
mass
action
kinetics
end;
//
rate
kinetics
and
initial
conditions
for
the
given
model
p.kr
=
1;
p.kg
=
1;
p.k7
=
1;
p.k8
=
2;
p.k9
=
2;
p.k10
=
1;
p.k11
=
2;
p.k12
=
2;
p.d5
=
0.1;
p.d6
=
0.1;
p.d7
=
0.1;
p.d8
=
0.1;
p.P3a
=
0;
p.S3a
=
0;
p.S4a
=
0;
p.P4a
=
0;
//
modular
time
intervals
for
simulation
h1
=
10;
h2
=
10;
//
simulation
of
given
model
m1
=
p.sim.eval(0,10,50,[<p.Time>,<p.S4a>,<p.P4a>]);
p.P4a
=
0;
m2
=
p.sim.eval(10,10+h1,50,[<p.Time>,<p.S4a>,<p.P4a>]);
m3
=
p.sim.eval(10+h1,100,50,[<p.Time>,<p.S4a>,<p.P4a>]);
m4
=
p.sim.eval(100,100+h2,50,[<p.Time>,<p.S4a>,<p.P4a>]);
m5
=
p.sim.eval(100+h2,1000,50,[<p.Time>,<p.S4a>,<p.P4a>]);
//
list
augmentations
m
=
augr(m1,
m2);
m
=
augr(m,
m3);
m
=
augr(m,
m4);
m
=
augr(m,
m5);
graph(m);
//
graphing
of
simulated
results
References
[1]
G.
E.
Moore,
“Cramming
more
components
onto
integrated
circuits,”
Electronics,
vol.
38,
no.
8,
pp.
1-‐4,
April
1965.
[2]
US
Department
of
Energy
Genome
(2011,
Sept.
19).
Human
Genome
Project
[Online].
Available:
http://www.ornl.gov/sci/techresources/Human_Genome/home.shtml
[3]
Registry
of
Standard
Biological
Parts.
Available:
http://partsregistry.org/
