course outline

1 Introduction

2 Theoretical background Biochemistry/molecular biology

3 Theoretical background computer science

4 History of the field

5 Splicing systems

6 P systems

7 Hairpins

8 Detection techniques

9 Micro technology introduction

10 Microchips and fluidics

11 Self assembly

12 Regulatory networks

13 Molecular motors

14 DNA nanowires

15 Protein computers

16 DNA computing - summery

17 Presentation of essay and discussion

Course outline

CMOSComplementary Metal-Oxide Semiconductor

Introduction

Electronic pathway

Seoul subway

Pyrimidine pathway

From DNA to pathways

Two Types of Biological Information

The genome, digital information

Environmental, analog information

Biological information

Two types of digital genome information

Genes, the molecular machines of life

Gene regulatory networks, specify the

behavior of the genes

Genome information

Biological System

RNA

DNA

Proteins

Biomodules

Cells Networks

What is systems biology?

A gene network …

A gene network in a physical network

Cell programming

E. coli

Diffusing signal

proteins

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

Programming cell communities

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

In vivo logic circuits

e. coli

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

x y NAND

0 0 1

0 1 1

1 0 1

1 1 0

NAND and NOT gate

X

Y

XY

X X

x NOT

0 1

1 0

X

Y

R1 Z

R1

R1X

Y

Z

= gene

gene

gene

NAND NOT

Logic circuit based on inverters

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?

Why digital?

SPICE

BioCircuit CAD

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

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

10

1 0

10

Smallest memory: RS-latch flip-flop

~Q = S + Q

R

SQ

~Q

Q = R + ~Q

RS-latch flip-flop truth table

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

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

Proof of Concept in BioSPICE

Work in BioSPICE simulations [Weiss, Homsy, Nagpal, 1998]

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

The IMPLIES gate

operatorpromoter gene

RNAP

activerepressor

operatorpromoter gene

RNAP

inactiverepressor

inducerno transcription transcription

The IMPLIES gate

pIKE = lac/tet

pTAK = lac/cIts

The toggle switch

[Gardner & Collins, 2000]

promoter

protein coding sequence

The toggle switch

[Gardner & Collins, 2000]

The ring oscillator

[Elowitz, Leibler 2000]

The repressilator network. 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 (see

Methods). 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 ring oscillator

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

[Elowitz, Leibler 2000]

Examples of oscillatory behaviour and of negative controls.

a±c, Comparison of the repressilator dynamics exhibited by

sibling cells. In each case, the fluorescence timecourse of

the cell depicted in Fig. 2 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.

Evaluation of the ring oscillator

A = original cI/λP(R)

B = repressor binding 3X weaker

C = 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

Construct a circuit that allows:

Control and observation of input protein levels

Simultaneous observation of resulting output

levels

Also, need to normalize CFP vs YFP

Measuring a transfer curve

“drive” gene output gene

R YFPCFP

inverter

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

Measuring a transfer curve

for lacI/p(lac)

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

Signal processing circuits

Receiver cells

pLuxI-Tet-8 pRCV-3

aTc

luxI VAI

VAI

LuxRGFP

tetR

aTc

00

Sender cells

VAI VAI

Receiver cellsSender cells

tetRP(tet)

luxIP(Ltet-O1)

aTc

GFP(LVA)Lux P(R)luxR Lux P(L)

+

Cell-cell communication circuits

C(4)HSLqsc box

C(6)HSLlux box

Cell Color

0 0 none

0 1 Green (GFP)

1 0 Red(HcRED)

1 1 Cyan(CFP)

2:4 multiplexer

Significance of multiplexer

With a 2:4 mux, the combination of 2 inputs

produces 4 different output states /

expressed proteins

In Eukaryotic cells, these proteins could

potentially differentiate the cell into one

of four cell types

Applications include tissue engineering and

more understanding for stem cell fate and

determination

qsc lux A

0 0 0

0 1 green

1 0 0

1 1 0

qsc lux B

0 0 0

0 1 0

1 0 red

1 1 0

qsc lux C

0 0 0

0 1 0

1 0 0

1 1 cyan

qsc lux D

0 0 0

0 1 green

1 0 red

1 1 cyan

+ +

=

Mux the sum of three circuits

luxbox

qscbox

GFP

luxR

RhlR

C4HSL

C6HSL

qsc lux A

0 0 0

0 1 green

1 0 0

1 1 0

Case A

luxbox

qscbox

HcRED

luxR

RhlR

C4HSL

C6HSL

qsc lux B

0 0 0

0 1 0

1 0 red

1 1 0

Case B

λP(R)CFP

cI

cI

luxbox

qscbox

qsc lux C

0 0 0

0 1 0

1 0 0

1 1 cyan

Case C, AND gate

luxbox

qscbox

HcRED

luxR RhlR

C4HSLC6HSL

qsc lux AxorB

0 0 0

0 1 green

1 0 red

1 1 0

GFP

Case A + B

qsc binding site

plasmid copy number

production of C(x)HSL

Design considerations

triple plasmid, regulatory

double plasmid, antisensing

double plasmid, antisensing + regulatory

chromosome, antisensing + regulatory

Phenotype tests

pRCV-34149 bp

AP r

GFP(LVA)

LuxR

CAP bs

CAP/cAMP Binding Site

P(BLA)

P(LAC)

lux P(L)

lux P(R)

lux box

RBSII

LuxR RBS

ColE1 ORI

Inverted Repeat

rrnB T1

rrnB T1

-10 region

-10 region

LuxR -10

LuxICDABEG -10 region

-35 region

LuxR -35

pASK-102: Single “Parent” Offspring

QSC box

Case A

pASK-102-qsc1174159 bp

AP r

GFP(LVA)

LuxR

CAP bs


P(BLA)

P(LAC)

lux P(L)

lux P(R)

lux box

qsc117 lux box for C4HSL

LuxR RBS

RBSII

ColE1 ORI

Inverted Repeat

rrnB T1

rrnB T1

-10 region

-10 region

LuxR -10


-35 region

LuxR -35

Plasmid 1

Case A

pASK-103-RhlR-qsc1174848 bp

AP r

GFP(LVA)

LuxR

RhlR Ver 2 (8 Mismatch)

CAP bs


P(LAC)

lux P(L)

lux P(R)

lux box

qsc117 lux box for C4HSL

LuxR RBS

RBSII

ColE1 ORI

Inverted Repeat

rrnB T1

-10 region

-10 region

LuxR -10


-35 region LuxR -35

Parents:pASK-102-qsc117 (vector)pECP61.5 (insert)

Case APlasmid 2

OO OONH

O

OO OONH

OO OONH

OO OONH

OO OONH

OO OONH

Analyte source detection

analytesource

reporter rings

OOONH

OOONH

OOONH

OOONH

OO OONH

OOONH

signal

Detecting chemical gradients

Components

1. Acyl-HSL detect

2. Low threshold

3. High threshold

4. Negating combiner

LuxRO O

O

ONHLuxR

O OO

ONH O O

O

ONHO O

O

ONH

O OO

ONH

P(lux) X Y

ZP(W)

GFPP(Z)

ZP(X)

WP(Y)

O OO

ONH

O OO

ONH

O OO

ONH

luxRP(R)

Circuit components

LuxRO O

O

ONHLuxR

O OO

ONH O O

O

ONHO O

O

ONH

O OO

ONH

P(lux) X Y

ZP(W)

GFPP(Z)

ZP(X)

WP(Y)

O OO

ONH

O OO

ONH

O OO

ONH

luxRP(R)

Y high threshold

X low threshold

Detecting chemical gradientsacyl-hSL detection

LuxRO O

O

ONHLuxR

O OO

ONH O O

O

ONHO O

O

ONH

O OO

ONH

P(lux) X Y

Z2P(W)

GFPP(Z)

Z1P(X)

WP(Y)

O OO

ONH

O OO

ONH

O OO

ONH

luxRP(R)

Detecting chemical gradientslow threshold detection

LuxRO O

O

ONHLuxR

O OO

ONH O O

O

ONHO O

O

ONH

O OO

ONH

P(lux) X Y

Z2P(W)

GFPP(Z)

Z1P(X)

WP(Y)

O OO

ONH

O OO

ONH

O OO

ONH

luxRP(R)


high threshold detection

LuxRO O

O

ONHLuxR

O OO

ONH O O

O

ONHO O

O

ONH

O OO

ONH

P(lux) X Y

Z2P(W)

GFPP(Z)

Z1P(X)

WP(Y)

O OO

ONH

O OO

ONH

O OO

ONH

luxRP(R)


protein Z determines range

LuxRO O

O

ONHLuxR

O OO

ONH O O

O

ONHO O

O

ONH

O OO

ONH

P(lux) X Y

Z2P(W)

GFPP(Z)

Z1P(X)

WP(Y)

O OO

ONH

O OO

ONH

O OO

ONH

luxRP(R)


negating combiner

HSL-mid: the midpoint where GFP has the

highest concentration

HSL-width: the range where GFP is above 0.3uM

HSL-width

HSL-mid0.3

Engineering circuit characteristics

course outline

Documents