Regulation. introduction molecular biology biotechnology bioMEMS bioinformatics bio-modeling ...

regulation

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

introduction

electronic pathway

seoul subway

tokyo subway

pyrimidine pathway

protein pathway

from DNA to pathways

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

biological information

genome information

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

Biological System

RNA

DNA

Proteins

Biomodules

Cells Networks

what is systems biology?

a gene network

a gene network in a physical network

Gene A

PromoterOperator

RNAP

Repressor

Inducer

Jacob & Monod Model of the prokaryotic operon (1961)

what is a genetic circuit?

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”

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

A genetic NAND Gate

A genetic flip-flop

Gene A

out2

out1

in2

in1

examples

basic genetic engineering

accessexcellence.com/AB/GG/plasmid.html

How do you clone a gene?

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

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

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


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.


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


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.


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:


lacl promoterlacl Promoter Operator lacZ lacY

Lac operon

lacA

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.


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.


Lactose

lacl + lacZ lacY

TRANSCRIPTION BEGINS

-galactosidase

Permeaserepressor mRNA

Repressor plus lactose (an inducer) present. Transcription proceeds.


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.


cell programming

E. coli

Diffusing signal

proteins

programming cell communities

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

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

pattern formation


analyte source

analyte source detection

reporter rings


biological cell programming

cellular logic

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

Z Promoter Z Gene

RNA Polymerase

DNA



Z Promoter Z Gene

Transcription

RNA Polymerase

DNA

Messenger RNA


RNA polymerase (RNAP) binds to promoter RNAP transcribes gene into messenger RNA Ribosome translates messenger RNA into protein

Z

Z Promoter Z Gene

ProteinTranscription

RNA Polymerase

DNA

Translation

Messenger RNA



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

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

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


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

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)



Chemical Equations

Total Concentration EquationsTotal Operator (OT) = (O) + (RO)

Total Repressor (RT) = (R) + (RO) (R) if (RT) >> (O)


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

cooperative inverter model

Coop Binding R + R + O R2O KR2O = (O)(R)2/(R2O)



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


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


Coop Binding R + R + O R2O KR2O = (O)(R)2/(R2O)



Chemical Equations

Total Concentration EquationsTotal 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 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

in vivo logic circuits

E. coli

logic gates

a genetic circuit building block

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

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

=R1

R1X

Y

Zgene

gene

gene

X

Y

R1 Z

NAND NOT

logic circuit based on inverters

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

why digital?

SPICE

http://bwrc.eecs.berkeley.edu/classes/icbook/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

BioCircuit CAD

inputmRNA ribosome

promoter

outputmRNA ribosome

operator

translation

transcription

RNAp

RBS RBS

genetic circuit elements

input

output

repressor

promoter

modeling a biochemical inverter

input

output

repressor

promoter

a BioSPICE inverter simulation

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

~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

RS-latch timing diagram

R

S

~Q1

0

1

0

Q

1

0

1

0

RS-latch dangerous transition

R

S

Q

~Q

1 0

1 0 0

0

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

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

operatorpromoter gene

RNAP

activerepressor

operatorpromoter gene

RNAP

inactiverepressor

inducerno transcription transcription

the IMPLIES gate

pIKE = lac/tetpTAK = lac/cIts

[Gardner & Collins, 2000]

the toggle switch

promoter

protein coding sequence

[Gardner & Collins, 2000]

the toggle switch

[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 ring oscillator

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.


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


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.

A = original cI/λP(R)

B = repressor binding 3X weakerC = transcription 2X stronger

ring oscillator with mismatched inverters

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

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

flow cytometry (FACS)

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

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

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

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

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

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 interactin

g at different scales... Possible modes of attack:

Engineering math: sensitivity analysis, control theory “Complex Systems” analysis

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

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.

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

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

Regulation. introduction molecular biology biotechnology bioMEMS bioinformatics bio-modeling ...

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