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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Tp
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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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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.