Regulation.

regulation

course layout 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

introduction

electronic pathway

seoul subway

tokyo subway

pyrimidine pathway

protein pathway

from DNA to pathways

biological information
Two Types of Biological Information The genome, digital information Environmental, analog information

genome information Two types of digital genome information
Genes, the molecular machines of life Gene regulatory networks, specify the behavior of the genes

what is systems biology?
Biological System DNA RNA Biomodules Cells Networks Proteins

a gene network

a gene network in a physical network

what is a genetic circuit?
Jacob & Monod Model of the prokaryotic operon (1961) Repressor RNAP Inducer Gene A Promoter Operator

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” A Gene A B Promoter Operator Gene B Promoter Operator

electronic circuits Basic electrical engineering (digital):
A basic “flip-flop” = memory And A B C A B C Or A Nand C B A B C A B C A B C in1 Nand 1 out1 Stable states (with in1, in2 = 0): out1 out2 out2 Nand 2 in2

examples A genetic NAND Gate A genetic flip-flop Gene A out1 in1 in2

basic genetic engineering
How do you clone a gene? accessexcellence.com/AB/GG/plasmid.html

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 design 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 behavior and splice them together in a plasmid. Transform the plasmid into bacteria and observe the behavior of the system. Does it match predictions from the simulation? -- Back to 1

gene expression

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.

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.

Transcriptional control Translational control Post-translational control Lifespan of mRNA Protein activation (by chemical modification) Onset of transcription Protein Translation rate Ribosome DNA mRNA Figure: 14.1 Caption: Although this chapter focuses on how regulatory molecules affect the ability of RNA polymerase to initiate transcription, there are actually several important mechanisms of gene regulation in bacteria. Feedback inhibition (protein inhibits transcription of its own gene) RNA polymerase

Escherichia coli

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.

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.

Relative density of cells
gene regulation mechanism Food source: Glucose : Lactose Glucose : Lactose Glucose : Lactose 1:3 1:1 3:1 Second period of rapid growth with lactose as food source 70 60 50 29.5 14.0 40 43.5 Relative density of cells 30 20 26.5 39.0 Initial period of rapid growth with glucose as food source 10 13.5 Figure: 14.2 Caption: These graphs show the number of E. coli cells produced versus time in three different media. Each experiment began when a small number of cells were introduced to a medium containing a different ratio of glucose to lactose: 1:3 (left), 1:1 (center), or 3:1 (right). The numbers and double-headed arrows to the right of each graph indicate the increase in relative density of cells that occurred during each period of growth. Question What would the curves look like if E. coli preferred lactose as an energy source? What would they look like if the cells had no preference for one sugar over another? 1 2 3 4 5 1 2 3 4 5 6 1 2 3 4 5 6 7 Time (hours)

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?

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.

Galactosidase permease
gene regulation mechanism Regulatory function Cleaves lactose to glucose and galactose Membrane transport protein-imports lactose Regulatory protein Galactosidase permease ß-galactosidase Lacl LacZ LacY Section of E. coli chromosome lacl lacZ lacY Observations about regulation of lacZ and lacY: Glucose (1) Lacl protein and glucose shut down transcription of lacZ and lacY Lactose Figure: 14.4 Caption: Early experiments on lactose utilization in E. coli identified these three genes and documented the function of their protein products. When researchers mapped the physical location of lacI, lacZ, and lacY on the E. coli chromosome, they found that they are positioned close together. E. coli Galactose Galactosidase permease (2) Lactose induces transcription of lacZ andlacY Chromosome ß-galactosidase

Lac operon lacl promoter lacl Promoter Operator lacZ lacY lacA Figure: 14.8a Caption: (a) The lac operon consists of the lacZ, lacY, and lacA loci. Transcription of these genes is under the control of a single promoter. The lacI locus is nearby, but is under the control of a different promoter. Exercise: Circle the structural genes in the diagram in part (a). Draw a box around regulatory sequences. Label the sites where RNA polymerase binds. lac operon

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.

When no lactose is present, the repressor binds to DNA and blocks transcription. NO TRANSCRIPTION Functional repressor lacl lacZ lacY Figure: 14.7a Caption: (a) Jacob and Monod hypothesized that the repressor is a protein that binds to a DNA sequence called the operator, which is just “upstream” from the lacZ locus. When the repressor is bound to the operator, RNA polymerase is physically blocked from transcribing lacZ. RNA polymerase blocked Operator (binding site for repressor)

Repressor plus lactose (an inducer) present. Transcription proceeds. Lactose TRANSCRIPTION BEGINS mRNA -galactosidase Permease repressor Figure: 14.5c Caption: (c) The negative control hypothesis contends that lactose acts as an inducer by interacting with the repressor and that this interaction prevents the repressor from functioning. lacl + lacZ lacY

Operons produce mRNAs that code for functionally related proteins. "Polycistronic" mRNA lacZ message lacY message RNA polymerase binds to promoter lacA message lacl promoter Figure: 14.8b Caption: (b) When RNA polymerase transcribes the lac operon, a “polycistronic” mRNA is produced. When this mRNA is translated, three different proteins are produced. lacl Promoter Operator lacZ lacY lacA

cell programming

programming cell communities
E. coli Diffusing signal proteins Interest: prog cells to perform tasks Focus: use inter. Comm. for coordinated behavior Explain substrate pic Chromosome = cell OS Design DNA, put into cell Signals=protein concentrations Describe communication – diffusion of signal chemicals

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 Interest: prog cells to perform tasks Focus: use inter. Comm. for coordinated behavior Explain substrate pic Chromosome = cell OS Design DNA, put into cell Signals=protein concentrations Describe communication – diffusion of signal chemicals

programmed cell applications
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 Cells are a novel substrate for engineering design -- providing an outstanding interface to the worlds of chemistry and nanotechnology. They have unique features that make them attractive for a wide variety of applications. These features include a miniature scale, energy efficiency, the ability to self reproduce, and the ability to manufacture biochemical products. But in order to effectively harness cells for our purposes, we need to address the following problem, which is how to engineer complex behaviors in cells in a fashion that is both predictable and reliable. Addressing these issues is especially important with biological substrates, where it’s often very difficult to obtain reliable and reproducible results. Effective solutions to engineering complex cell behaviors will enable applications such as sensing of complex environmental conditions, etc…

Cells are a novel substrate for engineering design -- providing an outstanding interface to the worlds of chemistry and nanotechnology. They have unique features that make them attractive for a wide variety of applications. These features include a miniature scale, energy efficiency, the ability to self reproduce, and the ability to manufacture biochemical products. But in order to effectively harness cells for our purposes, we need to address the following problem, which is how to engineer complex behaviors in cells in a fashion that is both predictable and reliable. Addressing these issues is especially important with biological substrates, where it’s often very difficult to obtain reliable and reproducible results. Effective solutions to engineering complex cell behaviors will enable applications such as sensing of complex environmental conditions, etc… pattern formation

analyte source reporter rings Cells are a novel substrate for engineering design -- providing an outstanding interface to the worlds of chemistry and nanotechnology. They have unique features that make them attractive for a wide variety of applications. These features include a miniature scale, energy efficiency, the ability to self reproduce, and the ability to manufacture biochemical products. But in order to effectively harness cells for our purposes, we need to address the following problem, which is how to engineer complex behaviors in cells in a fashion that is both predictable and reliable. Addressing these issues is especially important with biological substrates, where it’s often very difficult to obtain reliable and reproducible results. Effective solutions to engineering complex cell behaviors will enable applications such as sensing of complex environmental conditions, etc… analyte source detection

biological cell programming

cellular logic

protein expression basics
RNA polymerase binds to promoter RNAP transcribes gene into messenger RNA Ribosome translates messenger RNA into protein RNA Polymerase DNA Z Promoter Z Gene

RNA polymerase (RNAP) binds to promoter RNAP transcribes gene into messenger RNA Ribosome translates messenger RNA into protein Transcription RNA Polymerase Messenger RNA DNA Z Promoter Z Gene

RNA polymerase binds to promoter RNAP transcribes gene into messenger RNA Ribosome translates messenger RNA into protein Translation RNA Polymerase Z Protein Transcription Messenger RNA DNA Z Promoter Z Gene

regulation through repression
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 RNA Polymerase R Transcription Translation DNA Binding R R Promoter R Gene Z Promoter & Operator Z Gene

transcription-based inverter
Protein concentrations are analogous to electrical wires Proteins are not physically isolated, so unique wires require unique proteins R 1 R Z 1

simple inverter model Chemical Equations Total Concentration Equations
Repressor Binding R + O  RO KR+R = (O)(R)/(RO) Protein Synthesis O  O + Z kx Protein Decay Z  kdeg R Operator Z Gene Z Total Concentration Equations Total Operator (OT) = (O) + (RO) Total Repressor (RT) = (R) + (RO)  (R) if (RT) >> (O)

simple inverter model Transfer Function Derivation (O) = 1 (OT)
(O) + (RO) 1 + (RO)/(O) 1 + (R)/KR+R d(Z) = kx • (O) – kdeg • (Z) = 0 at equilibrium dt R Operator Z Gene Z (Z) = kx (O) • (OT) kdeg 1 + (R)/KR+R

simple inverter model Chemical Equations Total Concentration Equations
Repressor Binding R + O  RO KR+R = (O)(R)/(RO) Protein Synthesis O  O + Z kx Protein Decay Z  kdeg Total Concentration Equations Total Operator (OT) = (O) + (RO) Total Repressor (RT) = (R) + (RO)  (R) if (RT) >> (O)

cooperativity 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 RNA Polymerase R Transcription Translation Cooperative DNA Binding R R R Promoter R Gene Z Promoter & Operator Z Gene

cooperative inverter model
Chemical Equations Coop Binding R + R + O  R2O KR2O = (O)(R)2/(R2O) Protein Synthesis O  O + Z kx Protein Decay Z  kdeg R Operator Z Gene Z Total Concentration Equations Total Operator (OT) = (O) + (R2O) Total Repressor (RT) = (R) + 2•(R2O)  (R) if (RT) >> (O)

Transfer Function Derivation (O) = 1 (OT) (O) + (RO) 1 + (RO)/(O) 1 + (R)2/KR20 d(Z) = kx • (O) – kdeg • (Z) = 0 at equilibrium dt R Operator Z Gene Z (Z) = kx (O) • (OT) kdeg 1 + (R)2/KR+R Cooperative Non-Linearity

Chemical Equations Coop Binding R + R + O  R2O KR2O = (O)(R)2/(R2O) Protein Synthesis O  O + Z kx Protein Decay Z  kdeg Total Concentration Equations Total Operator (OT) = (O) + (R2O) Total Repressor (RT) = (R) + 2•(R2O)  (R) if (RT) >> (O)

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 proteins relatively easily Zinc-finger proteins can be fused with dimerization domains to increase cooperativity A cellular logic technology of only zinc-finger proteins should hopefully be easier to characterize

in vivo logic circuits

E. coli

logic gates

a genetic circuit building block
Title genetic circuit building block Threshold detector Amplifier delay

logic circuit based on inverters
Proteins are the wires/signals Promoter + decay implement the gates NAND gate is a universal logic element: any (finite) digital circuit can be built!

NAND and NOT gate x y NAND 1 X XY Y x NOT 1 X X

logic circuit based on inverters
X Y Z gene X Y R1 Z NAND NOT =

why digital? We know how to program with it Cells do 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

why digital?

BioCircuit CAD SPICE

BioCircuit CAD steady state dynamics intercellular
BioSPICE a prototype biocircuit CAD tool simulates protein and chemical concentrations intracellular circuits, intercellular communication single cells, small cell aggregates

genetic circuit elements
input mRNA ribosome promoter output operator translation transcription RNAp RBS

modeling a biochemical inverter
input repressor promoter output

a BioSPICE inverter simulation
input repressor promoter output

smallest memory: RS-latch flip-flop
1 1 1 1 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.

RS-latch flip-flop truth table
Q ~Q (n+ 1 ) (n+ 1 ) Q ~Q Q = R + ~Q (n) (n) 1 1 ~Q = S + Q 1 1 1 1

RS-latch timing diagram

RS-latch dangerous transition
1  0 0 

proof of concept in BioSPICE
RS-Latch (“flip-flop”) Ring oscillator _ [R] [A] _ R _ [S] A [B] time (x100 sec) [B] _ S B [C] [A] time (x100 sec) time (x100 sec) 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 Work in BioSPICE simulations [Weiss, Homsy, Nagpal, 1998]

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 Output Inducer

the IMPLIES gate active repressor inactive repressor RNAP inducer
no transcription transcription RNAP promoter operator gene promoter operator gene

the toggle switch pIKE = lac/tet pTAK = lac/cIts
[Gardner & Collins, 2000]

the toggle switch promoter protein coding sequence
[Gardner & Collins, 2000]

the ring oscillator [Elowitz, Leibler 2000]

the ring oscillator 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 repressilator network

the ring oscillator

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. [Elowitz, Leibler 2000]

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.

Reliable long-term oscillation doesn’t work yet: Will matching gates help? Need to better understand noise Need better models for circuit design [Elowitz, Leibler 2000]

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.

ring oscillator with mismatched inverters
A = original cI/λP(R) B = repressor binding 3X weaker C = transcription 2X stronger

device physics in steady state
“Ideal” inverter Transfer curve gain (flat,steep,flat) adequate noise margins “gain” [output] 1 [input] Curve can be achieved with certain dna-binding proteins Inverters with these properties can be used to build complex circuits

measuring a transfer curve
Construct a circuit that allows: Control and observation of input protein levels Simultaneous observation of resulting output levels inverter CFP R YFP “drive” gene output gene Also, need to normalize CFP vs YFP

flow cytometry (FACS)

drive input levels by varying inducer
lacI [high] YFP IPTG (uM) (Off) P(lacIq) P(lac) IPTG 100 P(lacIq) lacI IPTG P(lac) YFP 1000 promoter protein coding sequence

measuring a transfer curve
for lacI/p(lac) aTc YFP lacI CFP tetR [high] (Off) P(LtetO-1) P(R) P(lac) measure TC P(R) tetR P(lac) YFP aTc P(Ltet-O1) lacI CFP

transfer curve data points
01 undefined 10 1 ng/ml aTc 10 ng/ml aTc 100 ng/ml aTc

lacl/p(lac) transfer curve
aTc YFP lacI CFP tetR [high] (Off) P(LtetO-1) P(R) P(lac) gain = 4.72

evaluating the transfer curve
Gain / Signal restoration Noise margins high gain note: graphing vs. aTc (i.e. transfer curve of 2 gates)

applications

some possibilities “Forward Engineering” as a means of learning about natural genetic regulation. Biotechnology Experimental systems Validation of models

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 interacting at different scales... Possible modes of attack: Engineering math: sensitivity analysis, control theory “Complex Systems” analysis

forward engineering Forward Engineering Approach:
“We learned more about how birds fly from trying to build airplanes than from 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.

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.

experimental systems 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. DNA Binding Activation fish bait DNA Binding Activation GFP

validation of modeling techniques
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.

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