Evolvability and Cross-Talkin Chemical Networks

Evolvability and Cross-Talkin Chemical Networks

Chrisantha Fernando

Jon Rowe

Systems Biology Centre &

School of Computer Science

Birmingham University, UK

ESIGNET Meeting September 2007

Chrisantha Fernando

Jon Rowe

Systems Biology Centre &

School of Computer Science

Birmingham University, UK

ESIGNET Meeting September 2007

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AimsAims

Model evolution and function of cellular networks

Understand the principles of evolvability in

cellular networks

Model cross-talk

Model evolution and function of cellular networks

Understand the principles of evolvability in

cellular networks

Model cross-talk

Simulated Evolution of Protein Networks

Simulated Evolution of Protein Networks

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Bray and Lay, 1994

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dLdt=E(t)−k1[L][X1]+k'1[LX1]−k9[L][Y1]+k'9[Y1L]+k11[Y2L]−k'11[Y2][L] …1

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dX1dt=−k1[L][X1]+k'1[LX1] …2

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d[LX1]dt =k1[L][X1]−k'1[LX1]−k2[LX1]+k'2[LX2] …3

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d[LX2]dt =k2[LX1]−k'2[LX2]−k3[T1][LX2]+k'3[LX2T1]+k5[LX2T2]−k'5[LX2][T2] …4

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d[LX2T1]dt =k3[LX2][T1]−k'3[LX2T1]−k4[LX2T1]+k'4[LX2T2] …5

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d[LX2T2]dt =k4[LX2T1]−k'4[LX2T2]−k5[LX2T2]+k'5[LX2][T2] …6

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d[Y1]dt=−k6[Y1][T2]−k9[Y1][L]+k'6[Y1T2]+k'9[Y1L]+k12[Y2]+k8[Y1T1]−k'8[T1][Y1] …7

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d[Y1T2]dt =−k7[Y1T2]−k'6[Y1T2]+k6[T2][Y1]+k'7[Y1T1] …8

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d[Y1T1]dt =−k8[Y1T1]+k'8[T1][Y1]−k'7[Y1T1]+k7[Y1T2] …9

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d[Y1L]dt =k9[Y1][L]−k'9[Y1L]−k10[Y1L]+k'10[Y2L]

…10

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d[Y2L]dt =k10[Y1L]−k'10[Y2L]−k11[Y2L]+k'11[Y2][L]

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d[Y2]dt=k11[Y2L]−k'11[Y2][L]−k12[Y2]

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d[T1]dt=k8[Y1T1]−k'8[T1][Y1]−k3[T1][LX2]+k'3[LX2T1]

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d[T2]dt=k5[LX2T2]−k'5[T2][LX2]−k6[T2][Y1]+k'6[Y1T2]

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ConclusionsConclusions

The ‘genetic description’ of proteins used was very unevolvable, i.e. brittle.

Stochastic simulation did not allow ‘futile cycles’ to be modeled efficiently. These are essential for information transmission.

We moved to a more abstract representation of chemical networks, inspired by work in Eindhoven.

The ‘genetic description’ of proteins used was very unevolvable, i.e. brittle.

Stochastic simulation did not allow ‘futile cycles’ to be modeled efficiently. These are essential for information transmission.

We moved to a more abstract representation of chemical networks, inspired by work in Eindhoven.

Turing Complete Enzyme Computers

Turing Complete Enzyme Computers

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To Appear in European Conference in Artificial Life 2007

Lisbon.

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ConclusionConclusion

Although there is now an easy way of programming serial programs in enzyme controlled systems….

Implementation in a physical system is not trivial!!

Parallel implementations are possible. But how could we get evolvable chemical

networks in the real world?

Although there is now an easy way of programming serial programs in enzyme controlled systems….

Implementation in a physical system is not trivial!!

Parallel implementations are possible. But how could we get evolvable chemical

networks in the real world?

Chemical EvolutionChemical Evolution

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How did metabolism evolve?How did metabolism evolve?

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The ChemotonThe Chemoton

Metabolism

Template

Membrane

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•Molecular autocatalysts are necessary for heredity. •Some have 2o

effects that are beneficialto the compartment. •Some energy is required for this ‘memory’.

Catalysis

Autocatalysis

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New autocatalysts arise and integrate into existing intermediary metabolismNot a reflexive autocatalytic set!

Substrate

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Multiplication: YesHeredity: YesVariability: Macro not micro

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Is there a limit to complexity increase? Yes, in this simple model, the probability of stable autocatalyst formation decreases!

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The metabolic equivalent of Szathmary’s SCM

ConclusionsConclusions

A limit to complexity is imposed if chemical variability properties cannot be shaped by second order selection

Self-isolation of ‘faulty’ components (Tan, Revilla, Zauner, 2005)

What is second-order selection?

A limit to complexity is imposed if chemical variability properties cannot be shaped by second order selection

Self-isolation of ‘faulty’ components (Tan, Revilla, Zauner, 2005)

What is second-order selection?

Real chemicals embody variability rules as (modular) structures. Make a chemical description language capable of representing chemical equivalence classes abstractly, that allows adaptive variability.Evolve the system at the compartment level to maximize information transmission.

A

Second order selection is selection on the basis of

offspring fitness

B

It can act on variability properties

Evolvability shaped by second order selection?

Evolvability shaped by second order selection?

Produce a CE-calculus, capable of representing the crucial functional properties of small molecules that allow them to be structured by second order selection to promote evolvability, information transmission, and effective search.

Use Keppa (Vincent Danos, Harvard)

Produce a CE-calculus, capable of representing the crucial functional properties of small molecules that allow them to be structured by second order selection to promote evolvability, information transmission, and effective search.

Use Keppa (Vincent Danos, Harvard)

European Collaborations ArisingEuropean Collaborations Arising

• Eors Szathmary, ThalesNano (Budapest) & Guenter Von Kiedrowski (Bochum), FP7 Large scale application.

•Evolution of Formose cycle combinatorial libraries

Find lipid precursor that reacts with formose cycle sugars via phase-transfer autocatalysis yielding sugar-lipid conjugates.

Study the formose cycle using such a precursor

Study these subsystems under high pressure

A New Kind of Cell Signaling using RNAi

A New Kind of Cell Signaling using RNAi

Protein structure to function map is very complex.

A simpler and possibly more evolvable CSN could be made from RNA.

John Mattick’s work shows the large amount of non-translated RNA in cells.

We published a simulator capable of modeling complex populations of interacting RNA molecules with simple 2o structures.

Protein structure to function map is very complex.

A simpler and possibly more evolvable CSN could be made from RNA.

John Mattick’s work shows the large amount of non-translated RNA in cells.

We published a simulator capable of modeling complex populations of interacting RNA molecules with simple 2o structures.

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Bad Cross-Talk = Side-Reactions

a

b

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d

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Minimal Replicase was a Restriction Ribozyme

David Bartel and Jack Szostakbarking up wrong tree?

ConclusionsConclusions

The simulator used a simplified model of nucleic acid interactions to test hypotheses about how autocatalytic RNA could function in the absence of protein enzymes.

Further work will increase the range of secondary structures, e.g. hairpins.

The simulator used a simplified model of nucleic acid interactions to test hypotheses about how autocatalytic RNA could function in the absence of protein enzymes.

Further work will increase the range of secondary structures, e.g. hairpins.

Bacteria that can learnBacteria that can learn

Replicate this experiment

Is learning epigenetically heritable?

Are there any associated macro-nuclear

gene changes? (L. Landweber)

Cross-talk does associationCross-talk does association

In collaboration with molecular biologists, (Prof. Pete Lund, Dr. Lewis Bingle) and Anthony Liekens we have designed Hebbian learning circuits in plasmids carried by E. coli.

In collaboration with molecular biologists, (Prof. Pete Lund, Dr. Lewis Bingle) and Anthony Liekens we have designed Hebbian learning circuits in plasmids carried by E. coli.

v = w.u

dwi/dt = uiv

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Peter Dittrich, Thorsten Lenser & Christian Beck

‘Evolver’ uses “Biobrick” primitives. It is a

Synthetic Biology Toolbox

What to expect? What to expect?

Later….

Cell Signaling Network Implementation

Cell Signaling Network Implementation

ConclusionsConclusions

Nature paper in prep. Grant applications for synthesis in prep. Future medical applications. Introduces learning concepts to systems

biology.

Nature paper in prep. Grant applications for synthesis in prep. Future medical applications. Introduces learning concepts to systems

biology.

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Liquid State Machines in Bacteria?

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Why so Little Lamarckian Inheritance?

Why so Little Lamarckian Inheritance?

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ECAL 2007

Publications so far…Publications so far…

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http://www.cs.bham.ac.uk/~ctf/

Expected PublicationsExpected Publications

Nature. Hebbian Learning (in collaboration with Eindhoven and Jena).

Evolution. Second-order selection for evolvability.

Nature. Hebbian Learning (in collaboration with Eindhoven and Jena).

Evolution. Second-order selection for evolvability.