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E. coli Automatic Directed Evolution Machine a Universal Framework for Evolutionary Approaches in Synthetic Biology. University of Science and Technology of China. The power of evolution. Our inspiration. Biology. Engineering. Synthetic Biology.

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slide1

E. coli Automatic Directed Evolution Machinea Universal Framework for Evolutionary Approaches in Synthetic Biology

University of Science and Technology of China

slide3

Our inspiration

Biology

Engineering

  • Synthetic Biology
  • E. coli Automatic Directed Evolution Machine

Directed Evolution

Evolutionary Algorithm

the goal
The Goal

Scoring Function

Evolution

Desired Result

Evolution Object

E.ADEM

e adem framework
E.ADEM Framework

Kill

Variation Function

Selection Function

Change

Evolution Object

PoPS

Variation

PoPS

Selection

Self-Adaptive Controller

Reporter

PoPS

Act On

PoPS

Score

Scoring Function

Score

slide8

Scoring

Function

PoPS

tetR

luxI

tetR

Selection Function

PoPS

AHL

pLux-Tet

luxR-AHL

luxR

pCon 0.15

pCon 0.70

luxR

luxI

Self-Adaptive Controller

slide9

Scoring

Function

PoPS

tetR

luxI

tetR

Selection Function

PoPS

AHL

pLux-Tet

luxR-AHL

luxR

pCon 0.15

pCon 0.70

luxR

luxI

Self-Adaptive Controller

slide10

Scoring

Function

PoPS

tetR

luxI

tetR

Selection Function

PoPS

AHL

pLux-Tet

luxR-AHL

luxR

pCon 0.15

pCon 0.70

luxR

luxI

Self-Adaptive Controller

slide11

Scoring

Function

PoPS

tetR

luxI

tetR

Selection Function

PoPS

AHL

pLux-Tet

luxR-AHL

luxR

pCon 0.15

pCon 0.70

luxR

luxI

Self-Adaptive Controller

slide13

Selection function

tetR

pLux-Tet

ccdB

luxR-AHL

slide14

Selection function

High AHL & Low tetR

High AHL & High tetR

tetR

tetR

tetR

tetR

luxR

luxR-AHL

luxR-AHL

AHL

AHL

pLux-Tet

pLux-Tet

pLux-Tet

ccdB

ccdB

ccdB

pLux-Tet

ccdB

Low AHL & Low tetR

Low AHL & High tetR

luxR

AHL

AHL

slide15

High score E.coli

AHL

tetR

VR X

VR X

mRFP

mRFP

pCon X

pCon X

tetR

tetR

luxI

luxI

pLux-Tet

pLux-Tet

ccdB

ccdB

Low score E.coli

AHL

tetR

slide16

High score E.coli

AHL

tetR

VR X

mRFP

pCon X

tetR

luxI

pLux-Tet

ccdB

Low score E.coli

AHL

tetR

VR X

mRFP

pCon X

tetR

luxI

slide18

Measurement and Modeling

  • Basic measurement strategy

GFP

Reporter

B0015

PoPS

B0034

Use spectrfluorophotometer to measure fluorescence intensity

slide19

Measurement

  • Standard measurement

Standard unit: ustc_st1

Strains : TOP10

Plasmid: pSB1A3

Medium: M9 supplemented medium

Reporter: GFP BBa_I13504

More details about the measurement are available on our team wiki:

http://2009.igem.org/Team:USTC/Standard_%26_Protocol

slide20

VR X

pCon X

luxI

mRFP

tetR

tetR

pLux

-

Tet

ccdB

AHL

luxR

-

AHL

luxR

pCon

0.15

pCon

0.70

luxR

luxI

Scoring

Reporter

Self

-

Adaptive Controller

Selection Function

Function

slide21

Delayed

Waiting

Working

Done

Sequenced

1

5

2

6

7

Measurement

  • pLux-Tet
  • pLux-Tet + GFP
  • pCon×4+ luxR+ pLux-Tet
  • ccdB×8
  • pCon×8
  • pCon×8 + GFP
  • pCon+ luxR
  • pCon×7 + luxI (AHL detection by 9)
  • pCon×4 + luxR + pLux-Tet + GFP (AHL)
  • pCon×4 + luxR+ pLux-Tet + ccdB×8 (AHL)
  • tetR×2
  • tetR×2 + pCon×2 + luxR + pLux-Tet [+ GFP]
  • pCon×4+ tetR×2 + pCon×2 + luxR + pLux-Tet[+ GFP] (AHL/aTc)
  • [pCon×7 +] luxI+ pCon×2 + luxR + pLux-Tet[+ ccdB×8 | + GFP] (AHL)
  • VR×10
  • (VR + pCon)×7
  • tetR + pCon + luxI + pCon+ luxR + pLux-Tet [+ ccdB | + GFP]
  • [mRFP +] luxI + tetR + pCon + luxI + pCon + luxR + pLux-Tet [+ ccdB | + GFP]
  • (VR + pCon)×5 + tetR+ pCon + luxI + pCon + luxR + pLux-Tet [+ ccdB | + GFP]
  • (VR + pCon)×5 + mRFP+ luxI + tetR + pCon + luxI+ pCon + luxR + pLux-Tet [+ ccdB | + GFP]

9

3

4

10

8

11

14

15

12

17

16

19

13

18

20

slide22

Constitutive Promoter

tetR

VR X

mRFP

pCon X

luxI

tetR

FLU/OD(ustc_st1)

AHL

pLux-Tet

ccdB

luxR

pCon 0.15

pCon 0.70

luxR

luxI

slide23

Hybrid Promoter

tetR

VR X

mRFP

pCon X

luxI

tetR

pLux-Tet

AHL

ccdB

luxR

pCon 0.15

pCon 0.70

luxR

luxI

Hybrid promoter as a logic gate

slide24

Other measurement

Hybrid promoter response to LuxI

Hybrid promoter response to aTc

Efficiency of cI/pcI inverter

More details about the measurement are available on our team wiki:

http://2009.igem.org/Team:USTC/Standard_%26_Protocol

slide25

ccdB parts

  • Previous work: lacZα-ccdB
  • Did not work very well. Still working…
  • Different versions of ccdB we have tried

B0015

B0015

B0031

lacZα-ccdB-LVA

B0031

ccdB

B0015

B0015

B0034

ccdB

B0034

lacZα-ccdB-LVA

B0015

B0031

B0015

ccdB-LVA

lacZα-ccdB

B0034

B0015

B0034

ccdB-LVA

B0015

B0031

lacZα-ccdB

You L, Cox RS 3rd, Weiss R, Arnold FH. Programmed population control by cell-cell communication and regulated killing. Nature. 2004 Apr 22;428(6985):868-71.

slide26

Modeling

pLux-Tet

We estimate the values of k3, rAHLin, C1, C2, C3 and C4, using the data from our measurements. The simulation results as follows.

k3=0.8min-1; rAHL=0.001min-1; C1=1.5*10-10; C2=1*1010; C3=8*10-7; C4=5.8*1011

slide27

Simulation results

The dots in the graph represent our experiment data and the curve and surface

represent our simulation results.

slide28

Summary

★ Submit over 160 BioBrick parts to the registry

★ Characterize most of the parts

★ Comprehensive measurement data for whole system modeling

★ Introduce evolutionary algorithm into synthetic biology

★ A modular framework for automatic in vivo directed evolution

future plan
Future Plan
  • Short-term plan:
    • Use GFP to check the output of the self-adaptive controller
    • Try alternative proposal of the selection function, such as antisense RNA
slide31

PoPS

GFP

future plan1
Future Plan
  • Short-term plan:
    • Use GFP to check the output of the self-adaptive controller
    • Try alternative proposal of the selection function, such as antisense RNA
future plan2
Future Plan
  • Long-term plan:
    • E.ADEM as collaborative project in OpenWetWare
    • Realize the scoring functions for transcription repressor, sensor, logic gate, enzyme and binding partner
    • Develop variation functions for site-specific mutagenesis and recombination
    • Fine-tune the parameters in the self-adaptive controller
slide34
Sponsors

iGEM community

Teaching Affairs Office, USTC

Graduate School, USTC

Foreign Affairs Office, USTC

School of Life Sciences, USTC

Acknowledgments

in memory of our great masters
In Memory of Our Great Masters

TsienHsue-shen(钱学森)

1911.12.11-2009.10.31

Father of Space Tech in China

Bei Shi-zhang (贝时璋)

1903.10.10-2009.10.29

Father of Biophysics in China

directed evolution vs evolutionary algorithm
Directed Evolution vs.Evolutionary Algorithm

Poelwijk et al. 2007. Empirical fitness landscapes reveal accessible evolutionary paths. Nature 445, 383-386.

transcription repressor1
Transcription Repressor

Evolution Object

pCon

x

pX

Scoring Function

transcription repressor2
Transcription Repressor

Evolution Object

pCon

x

pX

Scoring Function

transcription repressor3
Transcription Repressor

Evolution Object

pCon

x

pX

!Score

Scoring Function

transcription repressor4
Transcription Repressor

Evolution Object

X' = pCon - dX X

pCon

x

!Score = pX KdX / (X + KdX)

pX

!Score

Scoring Function

transcription repressor5
Transcription Repressor

Evolution Object

X' = pCon - dX X

pCon

x

!Score = pX KdX / (X + KdX)

Xs = pCon / dX

pX

!Scores = pX dX KdX / (pCon + dX KdX)

!Score

Scoring Function

device e g sensor logic gate supervised learning
Device (e.g. Sensor, Logic Gate):Supervised Learning

Scoring Function

Evolution

Object

Comparator

Input

Output

!Score

Supervision (Reference Output)

slide47
More
  • For Enzyme:
    • If there is a sensor for the product or substrate, use it as the scoring function.
    • Else, perform evolution for a sensor first.
  • For Binding Partner:
    • E. coli two-hybrid systems.
    • Logic gates based on binding.
how to design the scoring function1

How to Design the Scoring Function?

Conclusion:

GenotypePhenotypeSignal TransductionTranscription Rate (PoPS)Universal Interface of E.ADEM

control strategies in evolutionary algorithm
Control Strategiesin Evolutionary Algorithm
  • Deterministic Control
    • No Feedback
  • Adaptive Control
    • Feedback Control
  • Self-Adaptive Control
    • The Evolution of Evolution Parameters
variation function
Variation Function
  • Targeted Mutagenesis
    • Activation Induced (Cytidine) Deaminase (AID)
      • iGEM 2008 Peking_University
      • iGEM 2008 Warsaw
    • Multiplex Automated Genome Engineering (MAGE)
    • Error-prone DNA polymerase I
    • Bacteriophage
  • Recombination
    • Site-specific recombination
      • Including Inversion, Excision/Integration and Translocation
    • Homologous recombination
    • Transposition
avoiding non specific mutation is a must
Avoiding Non-Specific Mutationis a Must
  • One More Immature Idea:

Variation Cell

Selection Cell

Conjugation ?

Evolution

Object

Scoring

Function

Reporter

Variation

Function

Controller

Selection

Function

selection function killer or saver
Selection Function: Killer or Saver

Selection Function

Death

Death

ccdB

OR

Selection Function

Death

Death

asRNA

pCon

RBS

kanR

reporter
Reporter
  • Real-time Monitoring Evolution Dynamics by FACS
  • Further Screening

Reporter

mRFP

Reporter

Reporter

 iGEM 2008 Tokyo_Tech

slide55

Quorum sensing I

VR X

tetR

pCon X

luxI

mRFP

pCI

tetR

AID

cI

AHL

pLux-Tet

ccdB

luxR

Quorum sensing II

pCon 0.15

luxI-LVA

pCon 0.70

luxR

human practice
Human Practice
  • Ethic

The chance to play God?

  • Safety

Experimental Safety

E.ADEM Safety

  • Security

Dilemma

slide57
OpenWetWare
  • Restore the System

Supervise the Mutation

Suicide Switch