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regulation
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introduction molecular biology biotechnology bioMEMS bioinformatics bio-modeling cells and e-cells transcription and regulation cell communication neural networks dna computing fractals and patterns the birds and the bees ….. and ants
course layout
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introduction
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electronic pathway
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seoul subway
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tokyo subway
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pyrimidine pathway
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protein pathway
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from DNA to pathways
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Two Types of Biological Information The genome, digital information Environmental, analog information
biological information
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genome information
Two types of digital genome information Genes, the molecular machines of life Gene regulatory networks, specify the behavior of the genes
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Biological System
RNA
DNA
Proteins
Biomodules
Cells Networks
what is systems biology?
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a gene network
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a gene network in a physical network
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Gene A
PromoterOperator
RNAP
Repressor
Inducer
Jacob & Monod Model of the prokaryotic operon (1961)
what is a genetic circuit?
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PromoterOperator
OperatorPromoter
Gene B
A
Gene AB
what is a genetic circuit?
Jacob & Monod Model of the prokaryotic operon (1961) “It is obvious from analysis of these [bacterial genetic regulatory]
mechanisms that their known elements could be connected into a wide variety of ‘circuits’ endowed with any desired degree of stability”
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electronic circuits
AndAB
CAB
COr
A B C0 0 00 1 01 0 01 1 1
A B C0 0 00 1 11 0 11 1 1
AB C
A B C0 0 10 1 11 0 11 1 0
Nand
Nand 1
Nand2
in1
in2
out1
out2
Stable states (with in1, in2 = 0):out1 out20 1 1 0
Basic electrical engineering (digital):
A basic “flip-flop” = memory
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A genetic NAND Gate
A genetic flip-flop
Gene A
out2
out1
in2
in1
examples
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basic genetic engineering
accessexcellence.com/AB/GG/plasmid.html
How do you clone a gene?
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genetic circuit engineering paradigm
1. Design Design “genetic circuitry” that demonstrates a rudimentary control behavior,
such as oscillations, bistability (like the flip-flop), step activation, a spike, etc.
2. Simulate Build a simulation (deterministic or stochastic ODEs) encapsulating the desi
gn and examine its dynamic behavior (boundary conditions of different stability regimes, parameter sensitivity…).
3. Implement and Test Use the results of this simulation to pick genetic parts yielding the desired b
ehavior and splice them together in a plasmid. Transform the plasmid into bacteria and observe the behavior of the system. Does it match predictions fr
om the simulation? -- Back to 1
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gene expression
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gene regulation mechanism
Bacteria express only a subset of their genes at any given time.
Expression of all genes constitutively in bacteria would be energetically inefficient.
The genes that are expressed are essential for dealing with the current environmental conditions, such as the type of available food source.
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Regulation of gene expression can occur at several levels:
Transcriptional regulation: no mRNA is made. Translational regulation: control of whether or how
fast an mRNA is translated. Post-translational regulation: a protein is made in an
inactive form and later is activated.
gene regulation mechanism
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Transcriptional control
Translational control
Post-translational control
Onset of transcription
RNA polymerase
Translation rate
Lifespan of mRNA
Ribosome
mRNA
Protein
Protein activation (by chemical modification)
Feedback inhibition (protein inhibits transcription of its own gene)
DNA
gene regulation mechanism
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Escherichia coli
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gene regulation mechanism
Operon A controllable unit of transcription consisting of a
number of structural genes transcribed together. Contains at least two distinct regions: the operator and the promoter.
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Case study of the regulation of the lactose
operon in E. coli
E. coli utilizes glucose if it is available, but
can metabolize other sugars if glucose is
absent.
gene regulation mechanism
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Glucose : LactoseFood source:
706050
3020
40
100R
ela
tive d
en
s ity
of
c el ls
0 1 2 3 4 5 0 1 2 3 4 5 6
43.5
13.5
1:3
Glucose : Lactose
1:1
Glucose : Lactose
3:1
Time (hours)
29.5
26.5
0 1 2 3 4 5 6 7
14.0
39.0
Second period of rapid growth with lactose as food source
Initial period of rapid growth with glucose as food source
gene regulation mechanism
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Case study of the regulation of the lactose operon in E. coli
Genes that encode enzymes needed to break other sugars down are negatively regulated. Example: enzymes required to metabolize lactose
are only synthesized if glucose is depleted and
lactose is available.
In the absence of lactose, transcription of the genes that encode these enzymes is repressed. How does
this occur?
gene regulation mechanism
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Case study of the regulation of the lactose operon in E. coli
All the loci required for lactose metabolism are grouped together into an operon.
The lacZ locus encodes -galactosidase enzyme, which breaks down lactose.
The lacY locus encodes galactosidase permease, a transport protein for lactose.
The function of the lacA locus is unknown.
The lacI locus encodes a repressor that blocks transcription of the lac operon.
gene regulation mechanism
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Section of E. coli chromosome
(1) Lacl protein and glucose shut down transcription of lacZ and lacY
(2) Lactose induces transcription of lacZ andlacY
Regulatory function
Regulatoryprotein
Lacl
lacl
Cleaves lactoseto glucose and galactose
ß-galactosidase
LacZ
lacZ
E. coli
Chromosome
Glucose
Galactose
ß-galactosidase
Galactosidase permease
Lactose
Membrane transport protein-imports lactose
Galactosidase permease
lacY
LacY
Observations aboutregulation of lacZ and lacY:
gene regulation mechanism
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lacl promoterlacl Promoter Operator lacZ lacY
Lac operon
lacA
lac operon
gene regulation mechanism
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Repression and induction of the lactose operon.
The lac operon is under negative
regulation, i.e. , normally, transcription is
repressed.
Glucose represses transcription of the lac operon. Glucose inhibits cAMP synthesis in the cells.
At low cAMP levels, no cAMP is available to bind CAP.
Unless CAP is bound to the CAP site in the promoter, no transcription occurs.
gene regulation mechanism
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lacl
Functional repressor
RNA polymerase blocked
Operator (binding site for repressor)
lacZ lacY
NO TRANSCRIPTION
When no lactose is present, the repressor binds to DNA and blocks transcription.
gene regulation mechanism
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Lactose
lacl + lacZ lacY
TRANSCRIPTION BEGINS
-galactosidase
Permeaserepressor mRNA
Repressor plus lactose (an inducer) present. Transcription proceeds.
gene regulation mechanism
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lacl promoter
lacl Promoter Operator lacZ lacY lacA
RNA polymerase binds to promoter
lacZ message
"Polycistronic" mRNA
lacY message
lacA message
Operons produce mRNAs that code for functionally related proteins.
gene regulation mechanism
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cell programming
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E. coli
Diffusing signal
proteins
programming cell communities
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programming cell communities
Program cells to perform various tasks using Intra-cellular circuits
Digital & analog components Inter-cellular communication
Control outgoing signals, process incoming signals
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Biomedical combinatorial gene regulation with few inputs; tissue engineering
Environmental sensing and effecting recognize and respond to complex environmental conditions
Engineered crops toggle switches control expression of growth hormones, pesticides
Cellular-scale fabrication cellular robots that manufacture complex scaffolds
programmed cell applications
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pattern formation
programmed cell applications
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analyte source
analyte source detection
reporter rings
programmed cell applications
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biological cell programming
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biological cell programming
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cellular logic
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protein expression basics
Z Promoter Z Gene
RNA Polymerase
DNA
RNA polymerase binds to promoter RNAP transcribes gene into messenger RNA Ribosome translates messenger RNA into protein
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Z Promoter Z Gene
RNA Polymerase
DNA
protein expression basics
RNA polymerase binds to promoter RNAP transcribes gene into messenger RNA Ribosome translates messenger RNA into protein
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Z Promoter Z Gene
Transcription
RNA Polymerase
DNA
Messenger RNA
protein expression basics
RNA polymerase (RNAP) binds to promoter RNAP transcribes gene into messenger RNA Ribosome translates messenger RNA into protein
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Z
Z Promoter Z Gene
ProteinTranscription
RNA Polymerase
DNA
Translation
Messenger RNA
protein expression basics
RNA polymerase binds to promoter RNAP transcribes gene into messenger RNA Ribosome translates messenger RNA into protein
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regulation through repression
Z Promoter& Operator
Z GeneR Gene
R
R
R Promoter
TranscriptionTranslation
DNA Binding
RNA Polymerase
Repressor proteins can bind to the promoter and block the RNA polymerase from performing transcription
The DNA site near the promoter recognized by the repressor is called an operator
The target gene can code for another repression protein enabling regulatory cascades
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transcription-based inverter
Protein concentrations are analogous to electrical wires
Proteins are not physically isolated, so unique wires require unique proteins
01
10
R
R
Z
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simple inverter model
Repressor Binding R + O RO KR+R = (O)(R)/(RO)
Protein Synthesis O O + Z kx
Protein Decay Z kdeg
Chemical Equations
Total Concentration Equations
Total Operator (OT) = (O) + (RO)
Total Repressor (RT) = (R) + (RO) (R) if (RT) >> (O)
R
R
Operator Z Gene
Z
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simple inverter model
Transfer Function Derivation
(O)
=
(O)
=
1
=
1
(OT) (O) + (RO)1 +
(RO)/(O)1 + (R)/KR+R
d(Z)= kx • (O) – kdeg (Z) = 0 • at equilibrium
dt
(Z) =kx
(O) =kx
•(OT)
kdeg kdeg 1 + (R)/KR+R
R
R
Operator Z Gene
Z
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0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1
Input Protein Concentration
Out
put
Pro
tein
Con
cent
ratio
n
Repressor Binding R + O RO KR+R = (O)(R)/(RO)
Protein Synthesis O O + Z kx
Protein Decay Z kdeg
Chemical Equations
Total Concentration EquationsTotal Operator (OT) = (O) + (RO)
Total Repressor (RT) = (R) + (RO) (R) if (RT) >> (O)
simple inverter model
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cooperativity
Z Promoter& Operator
Z GeneR Gene
R
R
R Promoter
TranscriptionTranslation
CooperativeDNA Binding
RNA Polymerase
R
Cooperative DNA binding is where the binding of one protein increases the likelihood of a second protein binding
Cooperativity adds more non-linearity to the system Increases switching sensitivity Improves robustness to noise
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cooperative inverter model
Coop Binding R + R + O R2O KR2O = (O)(R)2/(R2O)
Protein Synthesis O O + Z kx
Protein Decay Z kdeg
Chemical Equations
Total Concentration Equations
Total Operator (OT) = (O) + (R2O)
Total Repressor (RT) = (R) + 2•(R2O) (R) if (RT) >> (O)
R
R
Operator Z Gene
Z
R
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cooperative inverter model
Transfer Function Derivation
(O)=
(O)=
1=
1
(OT) (O) + (RO)1 +
(RO)/(O)1 + (R)2/KR20
d(Z)= kx • (O) – kdeg (Z) = 0 • at equilibrium
dt
(Z) =kx
(O) =kx
•(OT)
kdeg kdeg 1 + (R)2/KR+R
Cooperative Non-Linearity
R
R
Operator Z Gene
Z
R
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cooperative inverter model
Coop Binding R + R + O R2O KR2O = (O)(R)2/(R2O)
Protein Synthesis O O + Z kx
Protein Decay Z kdeg
Chemical Equations
Total Concentration EquationsTotal Operator (OT) = (O) + (R2O)
Total Repressor (RT) = (R) + 2•(R2O) (R) if (RT) >> (O)
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cellular logic summary
Current systems are limited to less than a dozen gates Three inverter ring oscillator [ Elowitz00 ] RS latch [ Gardner00 ] Inter-cell communication [ Weiss01 ]
A natural repressor-based logic technology presents serious scalability issues Scavenging natural repressor proteins is time consuming Matching natural repressor proteins to work together is difficult
Sophisticated synthetic biological systems require a scalable cellular logic technology with good cooperativity Zinc-finger proteins can be engineered to create many unique protein
s relatively easily Zinc-finger proteins can be fused with dimerization domains to incre
ase cooperativity A cellular logic technology of only zinc-finger proteins should hop
efully be easier to characterize
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in vivo logic circuits
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E. coli
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logic gates
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a genetic circuit building block
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Proteins are the wires/signals Promoter + decay implement the gates NAND gate is a universal logic element:
any (finite) digital circuit can be built!
logic circuit based on inverters
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NAND and NOT gate
x y NAND
0 0 1
0 1 1
1 0 1
1 1 0
X
Y
XY
X X
x NOT
0 1
1 0
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=R1
R1X
Y
Zgene
gene
gene
X
Y
R1 Z
NAND NOT
logic circuit based on inverters
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why digital?
We know how to program with it Signal restoration + modularity = robust complex circuits
Cells do it Phage λ cI repressor: Lysis or Lysogeny?
[Ptashne, A Genetic Switch, 1992] Circuit simulation of phage λ
[McAdams & Shapiro, Science, 1995]
Also working on combining analog & digital circuitry
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why digital?
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SPICE
http://bwrc.eecs.berkeley.edu/classes/icbook/SPICE/
BioCircuit CAD
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steady state dynamics intercellular
BioSPICE a prototype biocircuit CAD tool simulates protein and chemical concentrations intracellular circuits, intercellular communication single cells, small cell aggregates
BioCircuit CAD
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inputmRNA ribosome
promoter
outputmRNA ribosome
operator
translation
transcription
RNAp
RBS RBS
genetic circuit elements
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input
output
repressor
promoter
modeling a biochemical inverter
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input
output
repressor
promoter
a BioSPICE inverter simulation
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The output a of the R-S latch can be set to 1 by momentarily setting S to 0 while keeping R at 1.
When S is set back to 1 the output a stays at 1.
Conversely, the output a can be set to 0 by keeping S at 1 and momentarily setting R to 0.
When R is set back to 1, the output a stays at 0.
10
1 0
1 0
10
smallest memory: RS-latch flip-flop
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~Q = S + Q
R
SQ
~Q
Q = R + ~Q
R S Q(n+1) ~Q(n+1) 0 0 Q(n) ~Q(n) 0 1 1 0 1 0 0 1 1 1 0 0
RS-latch flip-flop truth table
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RS-latch timing diagram
R
S
~Q1
0
1
0
Q
1
0
1
0
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RS-latch dangerous transition
R
S
Q
~Q
1 0
1 0 0
0
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They work in vivo Flip-flop [Gardner & Collins, 2000] Ring oscillator [Elowitz & Leibler, 2000]
However, cells are very complex environments Current modeling techniques poorly predict behavior
time (x100 sec)
[A]
[C]
[B]
B_S
_R
A
_[R]
[B]
_[S]
[A]
time (x100 sec)
time (x100 sec)
RS-Latch (“flip-flop”) Ring oscillator
Work in BioSPICE simulations [Weiss, Homsy, Nagpal, 1998]
proof of concept in BioSPICE
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the IMPLIES gate
Inducers that inactivate repressors: IPTG (Isopropylthio-ß-galactoside) Lac repressor aTc (Anhydrotetracycline) Tet repressor
Use as a logical IMPLIES gate: (NOT R) OR I
Repressor Inducer Output
0 0 10 1 11 0 01 1 1
Repressor
InducerOutput
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operatorpromoter gene
RNAP
activerepressor
operatorpromoter gene
RNAP
inactiverepressor
inducerno transcription transcription
the IMPLIES gate
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pIKE = lac/tetpTAK = lac/cIts
[Gardner & Collins, 2000]
the toggle switch
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promoter
protein coding sequence
[Gardner & Collins, 2000]
the toggle switch
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[Elowitz, Leibler 2000]
the ring oscillator
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The repressilator is a cyclic negative-feedback loop composed of
three repressor genes and their corresponding promoters, as shown
schematically in the centre of the left-hand plasmid. It uses PLlacO1
and PLtetO1, which are strong, tightly repressible promoters
containing lac and tet operators, respectively6, as well as PR, the
right promoter from phage l. The stability of the three repressors is
reduced by the presence of destruction tags (denoted `lite'). The
compatible reporter plasmid (right) expresses an intermediate-
stability GFP variant11 (gfp-aav). In both plasmids, transcriptional
units are isolated from neighbouring regions by T1 terminators from
the E. coli rrnB operon (black boxes).
the ring oscillator
The repressilator network
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the ring oscillator
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[Elowitz, Leibler 2000]
evaluation of the ring oscillator
Comparison of the repressilator dynamics exhibited by sibling cells. In each case, the fluorescence timecourse of the cell depicted in the Fig is redrawn in red as a reference, and two of its siblings are shown in blue and green.
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evaluation of the ring oscillator
a, Siblings exhibiting post-septation phase delays relative to the reference cell. b, Examples where phase is approximately maintained but amplitude varies significantly after division. c, Examples of reduced period (green) and long delay (blue). d, Two other examples of oscillatory cells from data obtained in different experiments, under conditions similar to those of a±c. There is a large variability in period and amplitude of oscillations. e, f, Examples of negative control experiments. e, Cells containing the repressilator were disrupted by growth in media containing 50mM IPTG. f, Cells containing only the reporter plasmid.
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[Elowitz, Leibler 2000]
Reliable long-term oscillation doesn’t work yet: Will matching gates help? Need to better understand noise Need better models for circuit design
evaluation of the ring oscillator
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three repressors
LacI is a repressor protein made from the lacI gene, the lactose inhibitor gene of E. coli. TetR is a repressor protein made from the tetR gene. CI is a repressor protein made from the cI gene of phage.
Each one of these, with its cognate promoter, will stop production of whatever gene is ‘downstream’ from the promoter.
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A = original cI/λP(R)
B = repressor binding 3X weakerC = transcription 2X stronger
ring oscillator with mismatched inverters
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Transfer curve gain (flat,steep,flat) adequate noise margins
[input]
“gain”
0 1
[output]
Curve can be achieved with certain dna-binding proteins Inverters with these properties can be used to build complex
circuits
“Ideal” inverter
device physics in steady state
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Also, need to normalize CFP vs YFP
“drive” gene output gene
R YFPCFP
inverter
measuring a transfer curve
Construct a circuit that allows: Control and observation of input protein levels Simultaneous observation of resulting output levels
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flow cytometry (FACS)
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0
100
1000
IPTG
YFP
lacI[high]0
(Off) P(lac)P(lacIq)
lacIP(lacIq)
YFPP(lac)
IPTG
IPTG (uM)
promoter
protein coding sequence
drive input levels by varying inducer
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aTc
YFPlacICFP
tetR[high]0
(Off) P(LtetO-1)
P(R)
P(lac)
measure TC
tetRP(R)
P(Ltet-O1)
aTcYFPP(lac)
lacI CFP
for lacI/p(lac)
measuring a transfer curve
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01 10
1 ng/ml aTc
0
200
400
600
800
1,000
1,200
1,400
1 10 100 1,000 10,000
Fluorescence (FL1)
Eve
nts
undefined
10 ng/ml aTc 100 ng/ml aTc
0
200
400
600
800
1,000
1,200
1,400
1 10 100 1,000 10,000
Fluorescence (FL1)
Eve
nts
0
200
400
600
800
1,000
1,200
1,400
1 10 100 1,000 10,000
Fluorescence (FL1)
Eve
nts
transfer curve data points
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1
10
100
1000
1 10 100 1000
Input (Normalized CFP)
Ou
tpu
t (Y
FP)
aTc
YFPlacICFP
tetR[high]0
(Off) P(LtetO-1)
P(R)
P(lac)
gain = 4.72gain = 4.72
lacl/p(lac) transfer curve
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Noise margins
0
200
400
600
800
1,000
1,200
1,400
1 10 100 1,000
FluorescenceE
ven
ts
30 ng/mlaTc
3 ng/mlaTc
1
10
100
1,000
0.1 1.0 10.0 100.0
aTc (ng/ml)
Flu
ore
scen
ce
Gain / Signal restoration
high gainhigh gain
*note: graphing vs. aTc (i.e. transfer curve of 2 gates)
evaluating the transfer curve
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applications
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some possibilities
“Forward Engineering” as a means of learning about natural genetic regulation.
Biotechnology Experimental systems Validation of models
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forward engineering
Reductionism + Simulation = reverse engineering. Main Difficulty: system is WAY to complex
reductionism will never be finished when it is, models/ parameter-space will be too huge we don’t have much intuition for parallelism, processes interactin
g at different scales... Possible modes of attack:
Engineering math: sensitivity analysis, control theory “Complex Systems” analysis
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Forward Engineering Approach: “We learned more about how birds fly from trying to build airplanes than fro
m studying structural anatomy of birds.” - ?(ai)
Try to build something that has same functionality as system under study. Learn what some of the critical component requirements are, what the main design challenges.
Generate testable hypotheses about how natural genetic regulation functions.
Use forward and reverse engineering techniques in parallel.
forward engineering
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biotechnology
Genetic engineering applications: production of antibiotics and other drugs production of proteins for: detergents, solvents, aminos… bioremediation
Metabolic Control Analysis, directed evolution and other techniques used to optimize design of metabolic pathways for given task.
Genetic circuit engineering could yield finer more sophisticated control.
Genetic circuits as sensors.
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Perhaps genetic circuits can be used as clever assays/probes, similar to the Yeast Two-Hybrid system used to detect interacting proteins. A Transcription Factor
Fuse domains to putative interacting proteins
Is TF active?
Or Genetic circuits could be used to examine a system’s response to complex controllable inputs.
experimental systems
ActivationDNA
Binding
ActivationDNA
Bindingfish bait
GFP
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Many competing techniques for modeling biochemical systems: kinetics-based, stochastic kinetics, graph theoretical, discrete-event…
Ultimate gold-standard would be to design a system using a simulation technique, build it, and verify predictions of model.
validation of modeling techniques