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How Natural Selection could Evolve Metabolism. Chrisantha Fernando School of Computer Science University of Birmingham, UK San Sebastian, September 2006

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Page 1: How Natural Selection could Evolve Metabolism. Chrisantha Fernando School of Computer Science University of Birmingham, UK San Sebastian, September 2006

How Natural Selection could Evolve Metabolism.

How Natural Selection could Evolve Metabolism.

Chrisantha Fernando

School of Computer Science

University of Birmingham, UK

San Sebastian, September 2006

Chrisantha Fernando

School of Computer Science

University of Birmingham, UK

San Sebastian, September 2006

Page 2: How Natural Selection could Evolve Metabolism. Chrisantha Fernando School of Computer Science University of Birmingham, UK San Sebastian, September 2006

The Problem Defined:The Problem Defined:

The multiple source hypothesis proposes that a great variety of organic synthesis routes could have produced organic molecular variety.

Eg…..

The multiple source hypothesis proposes that a great variety of organic synthesis routes could have produced organic molecular variety.

Eg…..

Page 3: How Natural Selection could Evolve Metabolism. Chrisantha Fernando School of Computer Science University of Birmingham, UK San Sebastian, September 2006

A.I. Oparin & J.B.S. Haldane (1924,1929). UV light energy, Fox and Dose, Folsome, etc.. Amino acid polym.

G. Wachtershauser. FeS/H2S reducing power produces COO-, -S-

, -COS-, neg

charged metabolism on surface. S. Miller. Electrical discharges make Aas, aldehydes, etc.. in reducing

conditions C. de Duve. Thioester metabolism on surfaces. H. Morowitz. Reverse Citric acid cycle on mineral surfaces, (autocatalytic

step so far a problem). Decker, Ganti. Formose cycle feeding on Formaldehyde and other aldehydes

formed from perhaps a Miller like reaction or CO and H2O + light at pressure (Hazen et al).

Chyba and Astrobiology. Organics from space, meteoric origin of organics. Tommy Gold. Abiotic synthesis of hydrocarbons! (Controversial).

A.I. Oparin & J.B.S. Haldane (1924,1929). UV light energy, Fox and Dose, Folsome, etc.. Amino acid polym.

G. Wachtershauser. FeS/H2S reducing power produces COO-, -S-

, -COS-, neg

charged metabolism on surface. S. Miller. Electrical discharges make Aas, aldehydes, etc.. in reducing

conditions C. de Duve. Thioester metabolism on surfaces. H. Morowitz. Reverse Citric acid cycle on mineral surfaces, (autocatalytic

step so far a problem). Decker, Ganti. Formose cycle feeding on Formaldehyde and other aldehydes

formed from perhaps a Miller like reaction or CO and H2O + light at pressure (Hazen et al).

Chyba and Astrobiology. Organics from space, meteoric origin of organics. Tommy Gold. Abiotic synthesis of hydrocarbons! (Controversial).

Page 4: How Natural Selection could Evolve Metabolism. Chrisantha Fernando School of Computer Science University of Birmingham, UK San Sebastian, September 2006

Ingredients but no Recipe? Ingredients but no Recipe?

Given a wide range of ingredients to choose from, what process resulted in the origin of metabolism?

We seek I. The minimal autopoetic chemical organization

capable of the “recursive generation of functional constraints”, initially in the absence of template replication.

II. The properties of the ingredients, and the recipe, required to produce a metabolism, ultimately capable of evolving template replication.

Given a wide range of ingredients to choose from, what process resulted in the origin of metabolism?

We seek I. The minimal autopoetic chemical organization

capable of the “recursive generation of functional constraints”, initially in the absence of template replication.

II. The properties of the ingredients, and the recipe, required to produce a metabolism, ultimately capable of evolving template replication.

Page 5: How Natural Selection could Evolve Metabolism. Chrisantha Fernando School of Computer Science University of Birmingham, UK San Sebastian, September 2006

Selection and Drift in Phase Separated Spots

Selection and Drift in Phase Separated Spots

Operin suggested natural selection between coacervates and Dyson modeled the effect of drift in such systems and the probability of transition from simpler to more ‘complex’ metabolisms.

Thought experiments and models of natural selection between coacervate-like entities.

Replication rate is correlated with steady-state light absorption due to: direct growth enhancement and an energy constraint on information transmission.

Novelties arise through chemical ‘avalanches’.

Operin suggested natural selection between coacervates and Dyson modeled the effect of drift in such systems and the probability of transition from simpler to more ‘complex’ metabolisms.

Thought experiments and models of natural selection between coacervate-like entities.

Replication rate is correlated with steady-state light absorption due to: direct growth enhancement and an energy constraint on information transmission.

Novelties arise through chemical ‘avalanches’.

Page 6: How Natural Selection could Evolve Metabolism. Chrisantha Fernando School of Computer Science University of Birmingham, UK San Sebastian, September 2006

AssumptionsAssumptions

Assumption 1: Phase separated spots form on a liquid surface. They proliferate due to supply of oily ‘food’ material that perhaps falls in rain. They divide by agitation and are subject to random loss, with geological recycling of their ‘food’ back to the atmosphere.

Assumption 1: Phase separated spots form on a liquid surface. They proliferate due to supply of oily ‘food’ material that perhaps falls in rain. They divide by agitation and are subject to random loss, with geological recycling of their ‘food’ back to the atmosphere.

Page 7: How Natural Selection could Evolve Metabolism. Chrisantha Fernando School of Computer Science University of Birmingham, UK San Sebastian, September 2006

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Page 8: How Natural Selection could Evolve Metabolism. Chrisantha Fernando School of Computer Science University of Birmingham, UK San Sebastian, September 2006

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The First Autotrophic Unit is at the Level of a Geophysical Cycle

Chemical recycling

Physical recycling

Page 9: How Natural Selection could Evolve Metabolism. Chrisantha Fernando School of Computer Science University of Birmingham, UK San Sebastian, September 2006

Assumption 2: Within the oil phase, there exists a potential light absorbing reaction that can re-form abbb and a high energy molecule ba. But to start with there is no abb so this reaction cannot actually take place.

Assumption 2.1. In addition, light absorbing products may be much longer, and contribute more to spot growth than non-driven reactions. Long molecules stay in spots better than short ones.

Assumption 2: Within the oil phase, there exists a potential light absorbing reaction that can re-form abbb and a high energy molecule ba. But to start with there is no abb so this reaction cannot actually take place.

Assumption 2.1. In addition, light absorbing products may be much longer, and contribute more to spot growth than non-driven reactions. Long molecules stay in spots better than short ones.

abb + abb + light ----> ba + abbbabb + abb + light ----> ba + abbb

Page 10: How Natural Selection could Evolve Metabolism. Chrisantha Fernando School of Computer Science University of Birmingham, UK San Sebastian, September 2006

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1. A light absorbing reaction may produce molecules that make the spot grow faster directly.

or longer molecule

Page 11: How Natural Selection could Evolve Metabolism. Chrisantha Fernando School of Computer Science University of Birmingham, UK San Sebastian, September 2006

Assumption 3: Spots able to absorb abbb more quickly obviously proliferate faster.

If a spot contains high energy species that react with abbb molecules in chemical reactions that produce spot localized contents, then abbb absorption rate can be increased greatly (irreversibility of this reaction is ++ favoured).

Assumption 3: Spots able to absorb abbb more quickly obviously proliferate faster.

If a spot contains high energy species that react with abbb molecules in chemical reactions that produce spot localized contents, then abbb absorption rate can be increased greatly (irreversibility of this reaction is ++ favoured).

Page 12: How Natural Selection could Evolve Metabolism. Chrisantha Fernando School of Computer Science University of Birmingham, UK San Sebastian, September 2006

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2. High energy reactants can shift the equilibrium of ‘food’promoting growth.

Page 13: How Natural Selection could Evolve Metabolism. Chrisantha Fernando School of Computer Science University of Birmingham, UK San Sebastian, September 2006

Assumption 4: Novel species are produced by rare bimolecular rearrangement reactions between existing species, e.g. abbb and other food species, e.g.

abbb + abbb -(v. low flux ~ 0)-> ba + bbbbba

Both ba and bbbbba are novel species, present at initially very low concentration e.g. 0.0000001M, in actual fact, much less than this, i.e. 1 molecule!

Assumption 4: Novel species are produced by rare bimolecular rearrangement reactions between existing species, e.g. abbb and other food species, e.g.

abbb + abbb -(v. low flux ~ 0)-> ba + bbbbba

Both ba and bbbbba are novel species, present at initially very low concentration e.g. 0.0000001M, in actual fact, much less than this, i.e. 1 molecule!

Page 14: How Natural Selection could Evolve Metabolism. Chrisantha Fernando School of Computer Science University of Birmingham, UK San Sebastian, September 2006

Assumption 5: Once a novel species is produced, it may react in spontaneous high flux reactions with a proportion of other species,e.g. 0.01%, 1%, 5% etc…

However, by definition such novel reactions must have insignificant reverse rates if their product is any existing species, otherwise, reverse flux through this reaction would have been observed before the novel rare species arose, and the novel species would therefore not have been novel!

Assumption 5: Once a novel species is produced, it may react in spontaneous high flux reactions with a proportion of other species,e.g. 0.01%, 1%, 5% etc…

However, by definition such novel reactions must have insignificant reverse rates if their product is any existing species, otherwise, reverse flux through this reaction would have been observed before the novel rare species arose, and the novel species would therefore not have been novel!

Page 15: How Natural Selection could Evolve Metabolism. Chrisantha Fernando School of Computer Science University of Birmingham, UK San Sebastian, September 2006

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Page 16: How Natural Selection could Evolve Metabolism. Chrisantha Fernando School of Computer Science University of Birmingham, UK San Sebastian, September 2006

Assumption 6: For each novel species produced in high flux reactions, the high flux reactions it takes part in are again calculated, resulting in a potential ‘avalanche’ of novel reactions. Avalanche size distribution will depend on the proportion P each species reacts with.

Assumption 6: For each novel species produced in high flux reactions, the high flux reactions it takes part in are again calculated, resulting in a potential ‘avalanche’ of novel reactions. Avalanche size distribution will depend on the proportion P each species reacts with.

Page 17: How Natural Selection could Evolve Metabolism. Chrisantha Fernando School of Computer Science University of Birmingham, UK San Sebastian, September 2006

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aaa + b

bbbbbaa + a

1.2.

3.

Determining the Avalanche.

Page 18: How Natural Selection could Evolve Metabolism. Chrisantha Fernando School of Computer Science University of Birmingham, UK San Sebastian, September 2006

Energy for InformationEnergy for Information

Page 19: How Natural Selection could Evolve Metabolism. Chrisantha Fernando School of Computer Science University of Birmingham, UK San Sebastian, September 2006

3. Energy required to replicate babb, (high k2). 4. Energy required for babb to have influence (high k3).

Page 20: How Natural Selection could Evolve Metabolism. Chrisantha Fernando School of Computer Science University of Birmingham, UK San Sebastian, September 2006

Assumption 7 : Division occurs due to absorption of abbb at regular intervals.

Replication rate is increased if abbb undergoes chemical reactions with the material in the spot to produce spot phase material, (longer polymers being more likely to be spot-phase). Division occurs when the volume reaches some noisy threshold.

One definition of fitness is time to reach this threshold. In the simulation, fitness could be defined as the total mass of material absorbed into the spot in fixed time T.

Assumption 7 : Division occurs due to absorption of abbb at regular intervals.

Replication rate is increased if abbb undergoes chemical reactions with the material in the spot to produce spot phase material, (longer polymers being more likely to be spot-phase). Division occurs when the volume reaches some noisy threshold.

One definition of fitness is time to reach this threshold. In the simulation, fitness could be defined as the total mass of material absorbed into the spot in fixed time T.

Page 21: How Natural Selection could Evolve Metabolism. Chrisantha Fernando School of Computer Science University of Birmingham, UK San Sebastian, September 2006

Low energy vs. high energy spots.

Low energy vs. high energy spots.

Spots growing by incorporation of low energy material are disadvantaged because, Absorption of abbb by chemical reactions is

thermodynamically unfavorable. Unable to create the thermodynamically unfavorable

longer polymers that greatly benefit spot growth. Unable to sustain autocatalysis required for the

‘channeling’ of the reaction network. High energy autocatalytic particles can more

effectively channel the reaction network than low energy autocatalytic particles.

Spots growing by incorporation of low energy material are disadvantaged because, Absorption of abbb by chemical reactions is

thermodynamically unfavorable. Unable to create the thermodynamically unfavorable

longer polymers that greatly benefit spot growth. Unable to sustain autocatalysis required for the

‘channeling’ of the reaction network. High energy autocatalytic particles can more

effectively channel the reaction network than low energy autocatalytic particles.

Page 22: How Natural Selection could Evolve Metabolism. Chrisantha Fernando School of Computer Science University of Birmingham, UK San Sebastian, September 2006

Selection at the Level of the Geophysical Unit?

Selection at the Level of the Geophysical Unit?

Low energy spot material requires more energy for recycling back to abbb by the autotrophic geophysical metabolism.

If [abbb] in rain is limited by recycling rate, then geophysical units with greater abbb production mediate greater energy flux, more replication, and hence more natural selection.

A spot that produces abbb not only benefits itself, but benefits all other spots in the geophysical unit.

Low energy spot material requires more energy for recycling back to abbb by the autotrophic geophysical metabolism.

If [abbb] in rain is limited by recycling rate, then geophysical units with greater abbb production mediate greater energy flux, more replication, and hence more natural selection.

A spot that produces abbb not only benefits itself, but benefits all other spots in the geophysical unit.

Page 23: How Natural Selection could Evolve Metabolism. Chrisantha Fernando School of Computer Science University of Birmingham, UK San Sebastian, September 2006

Preliminary Models. Preliminary Models.

In the models so far I did not have this elaborate geophysical unit conception, but attempted to directly select for increased steady-state light absorption in spots.

The artificial selection experiment is intended to capture some of the dynamics just presented.

In the models so far I did not have this elaborate geophysical unit conception, but attempted to directly select for increased steady-state light absorption in spots.

The artificial selection experiment is intended to capture some of the dynamics just presented.

Page 24: How Natural Selection could Evolve Metabolism. Chrisantha Fernando School of Computer Science University of Birmingham, UK San Sebastian, September 2006

Aim of the Models Aim of the Models

I use a 1:1 GA to artificially seelct ‘spots’ satisfying a fitness function.

What chemical organizations are produced, e.g. autocatalytic cycles etc..?

What is the ratio of harmful reactions to beneficial reactions as a function of the properties of chemical variation, the food set and the fitness function?

I use a 1:1 GA to artificially seelct ‘spots’ satisfying a fitness function.

What chemical organizations are produced, e.g. autocatalytic cycles etc..?

What is the ratio of harmful reactions to beneficial reactions as a function of the properties of chemical variation, the food set and the fitness function?

Page 25: How Natural Selection could Evolve Metabolism. Chrisantha Fernando School of Computer Science University of Birmingham, UK San Sebastian, September 2006

Main FindingsMain Findings Experiments selecting for high steady-state light absorption

show adaptive avalanches v. rare because. Probability of growth of novel low concentration product is small. Probability that novel product has beneficial spot-level effect is

small. Bimolecular rearrangement reactions tend to produce divergent

networks (not closed), not such a problem if autocatalytic growth of novel product is occurring.

Increasing food set size increases probability of adaptations. Early experiments selecting for abbb uptake rate: Light

absorption reaction not always utilized. Adaptive frequency decreases over evolution?

Experiments selecting for high steady-state light absorption show adaptive avalanches v. rare because. Probability of growth of novel low concentration product is small. Probability that novel product has beneficial spot-level effect is

small. Bimolecular rearrangement reactions tend to produce divergent

networks (not closed), not such a problem if autocatalytic growth of novel product is occurring.

Increasing food set size increases probability of adaptations. Early experiments selecting for abbb uptake rate: Light

absorption reaction not always utilized. Adaptive frequency decreases over evolution?

Page 26: How Natural Selection could Evolve Metabolism. Chrisantha Fernando School of Computer Science University of Birmingham, UK San Sebastian, September 2006

The ChemistryThe Chemistry

Bimolecular rearrangement reactions of binary atoms, e.g. abbb + ba <----> abb + abb

Each molecule has free energy of formation, G. Two types of reaction.

Irreversible (and reversible) exogonic reactions (heat producing) Irreversible endogonic reaction (light absorbing)

When generating novel reactions ensure they are always spontaneous.

Bimolecular rearrangement reactions of binary atoms, e.g. abbb + ba <----> abb + abb

Each molecule has free energy of formation, G. Two types of reaction.

Irreversible (and reversible) exogonic reactions (heat producing) Irreversible endogonic reaction (light absorbing)

When generating novel reactions ensure they are always spontaneous.

Page 27: How Natural Selection could Evolve Metabolism. Chrisantha Fernando School of Computer Science University of Birmingham, UK San Sebastian, September 2006

kf = 0.01*e-dG/RT

kb = 0.01, dG = (Gproducts - Greactants)

R = gas constant, T = 300K

The Initial Network

Page 28: How Natural Selection could Evolve Metabolism. Chrisantha Fernando School of Computer Science University of Birmingham, UK San Sebastian, September 2006

Artificial SelectionArtificial Selection

Page 29: How Natural Selection could Evolve Metabolism. Chrisantha Fernando School of Computer Science University of Birmingham, UK San Sebastian, September 2006

Control ExperimentControl Experiment

Information (i.e. modification of reaction structure) implicit. Now reactions can just be formed and removed randomly! No need for the evolution of “engram autocatalysis”.

Autocatalytic reaction network evolved. High energy hub molecules drive reactions,

e.g. babb.

Information (i.e. modification of reaction structure) implicit. Now reactions can just be formed and removed randomly! No need for the evolution of “engram autocatalysis”.

Autocatalytic reaction network evolved. High energy hub molecules drive reactions,

e.g. babb.

Page 30: How Natural Selection could Evolve Metabolism. Chrisantha Fernando School of Computer Science University of Birmingham, UK San Sebastian, September 2006

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Page 31: How Natural Selection could Evolve Metabolism. Chrisantha Fernando School of Computer Science University of Birmingham, UK San Sebastian, September 2006
Page 32: How Natural Selection could Evolve Metabolism. Chrisantha Fernando School of Computer Science University of Birmingham, UK San Sebastian, September 2006

Fitness is largely unaffected if the network is initialized with 100mM abbb plus any one of the following species at 0.1mM; ba, ab, abb, babb, babbb, bbbbbba.

However, if the network is initialized with 100mM abbb alone, or with 100mM abbb + 0.1mM bbab, bbabab, bbbba, bab, bbabb, or bbbaabab, etc… then fitness = 0.

I.e. the network reacts abbb (food) with inherited high energy molecules (self) to produce abb.

Fitness is largely unaffected if the network is initialized with 100mM abbb plus any one of the following species at 0.1mM; ba, ab, abb, babb, babbb, bbbbbba.

However, if the network is initialized with 100mM abbb alone, or with 100mM abbb + 0.1mM bbab, bbabab, bbbba, bab, bbabb, or bbbaabab, etc… then fitness = 0.

I.e. the network reacts abbb (food) with inherited high energy molecules (self) to produce abb.

Page 33: How Natural Selection could Evolve Metabolism. Chrisantha Fernando School of Computer Science University of Birmingham, UK San Sebastian, September 2006

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Page 34: How Natural Selection could Evolve Metabolism. Chrisantha Fernando School of Computer Science University of Birmingham, UK San Sebastian, September 2006

Full ExperimentFull Experiment

Avalanche type variation introduced. No adaptations evolved with just abbb as food,

within 80,000 generations, and after several runs! To solve this problem….

More food molecule types introduced. ab,aab, aaab, aabb, abbb, aaaab, aaabb, aabbb, abbbb

Only one initial reaction defined at outset. abb + abb ---> ba + abbb Therefore initial fitness = 0. More possible small molecule reactions undefined.

Avalanche type variation introduced. No adaptations evolved with just abbb as food,

within 80,000 generations, and after several runs! To solve this problem….

More food molecule types introduced. ab,aab, aaab, aabb, abbb, aaaab, aaabb, aabbb, abbbb

Only one initial reaction defined at outset. abb + abb ---> ba + abbb Therefore initial fitness = 0. More possible small molecule reactions undefined.

Page 35: How Natural Selection could Evolve Metabolism. Chrisantha Fernando School of Computer Science University of Birmingham, UK San Sebastian, September 2006

Fitness Fitness

3Sept_10_0_05

Generation

Fitness

Page 36: How Natural Selection could Evolve Metabolism. Chrisantha Fernando School of Computer Science University of Birmingham, UK San Sebastian, September 2006

3Sept_10_0_05

Time

Conc

abbb,aaab,aabb, (aaaab)* Not used.

ba

abbbaab

abb

Page 37: How Natural Selection could Evolve Metabolism. Chrisantha Fernando School of Computer Science University of Birmingham, UK San Sebastian, September 2006

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3Sept_10_0_05

Rare novel molecule

0.68kJ

1kJ

1kJ

1kJ

1kJ

1kJ5kJ1kJ

0.15kJ

0.27kJ

0.4kJ

- High energy food used to drive new reactions. -ba from light not used?

1.6813

91

1

Page 38: How Natural Selection could Evolve Metabolism. Chrisantha Fernando School of Computer Science University of Birmingham, UK San Sebastian, September 2006

5Sept1 5Sept2

5Sept35Sept4

No preference for particlesof the same length to react.

No b related catalysis probability effect.

1/L not 1/L2

1. Preference for same length species to react.

2. Probability of a particle being catalytic is related to the proportion of p.

3. Prob reaction proportional to 1/L2

.

4. Initial [rare species] = 0.00001

Generation

Fitness

Page 39: How Natural Selection could Evolve Metabolism. Chrisantha Fernando School of Computer Science University of Birmingham, UK San Sebastian, September 2006

5Sept5. No length dependence 5Sept6Initial [rare] = 0.0000001Not 0.00001

Page 40: How Natural Selection could Evolve Metabolism. Chrisantha Fernando School of Computer Science University of Birmingham, UK San Sebastian, September 2006

5Sept1

The Evolutionary History of Experiment 5Sept1

661

662

1221

2200,2201,2205

23292427

1.07852

91.9089

220075.852

20.6873

2201

2205

23.8513

2329

2427

Page 41: How Natural Selection could Evolve Metabolism. Chrisantha Fernando School of Computer Science University of Birmingham, UK San Sebastian, September 2006

The Evolutionary History of Experiment 5Sept2

[abb]296.704

[abb]5.27797

[abb]

221.193

Page 42: How Natural Selection could Evolve Metabolism. Chrisantha Fernando School of Computer Science University of Birmingham, UK San Sebastian, September 2006

Properties of Evolved NetworksProperties of Evolved Networks

Many different food molecules are typically utilized, e.g. aabb, abbb, aaab, abbb.

Novel products (X) often catalyse a reaction with a food molecule, e.g.

X + abbb ---> X + babb

The novel product typically then reacts with another food molecule to re-form the X, e.g. babb + aaab ----> X + Waste

See next diagram…

Many different food molecules are typically utilized, e.g. aabb, abbb, aaab, abbb.

Novel products (X) often catalyse a reaction with a food molecule, e.g.

X + abbb ---> X + babb

The novel product typically then reacts with another food molecule to re-form the X, e.g. babb + aaab ----> X + Waste

See next diagram…

Page 43: How Natural Selection could Evolve Metabolism. Chrisantha Fernando School of Computer Science University of Birmingham, UK San Sebastian, September 2006

X

F1a

F2W

X

This is two step autocatalysis, i. Step one, catalysis. ii. Step two, formation of another catalyst.

The complex networks exhibit many such two step autocatalysis reactions.

Engram Autocatalysis was evolved.

Page 44: How Natural Selection could Evolve Metabolism. Chrisantha Fernando School of Computer Science University of Birmingham, UK San Sebastian, September 2006

ConclusionsConclusions Often the child networks (even without mutation) are less

fit that the parent network, due to a non-heritable adaptation in the parent.

Sometimes there are children of greater fitness than parents produced in quick succession, as [abb] increases over several generations due to the same adaptation in the parent. This can mask harmful reactions in the offspring.

The use of the 1:1 GA is restrictive, not allowing selection for robustness to variation, as would be expected with a larger population size, and a high probability of variation.

Often the child networks (even without mutation) are less fit that the parent network, due to a non-heritable adaptation in the parent.

Sometimes there are children of greater fitness than parents produced in quick succession, as [abb] increases over several generations due to the same adaptation in the parent. This can mask harmful reactions in the offspring.

The use of the 1:1 GA is restrictive, not allowing selection for robustness to variation, as would be expected with a larger population size, and a high probability of variation.

Page 45: How Natural Selection could Evolve Metabolism. Chrisantha Fernando School of Computer Science University of Birmingham, UK San Sebastian, September 2006

Ecological dynamics between spots having different metabolic roles are not considered.

Novel reactions are sustained by the evolution of autocatalytic species, “engram autocatalysts”.

The light absorbing molecule itself shows autocatalytic growth, since this maximizes fitness of the spot, “growth autocatalysis”.

No micro-mutation is necessary if sufficient variety of random catalytic avalanches exists, and if harmful avalanches can be prevented spreading.

Ecological dynamics between spots having different metabolic roles are not considered.

Novel reactions are sustained by the evolution of autocatalytic species, “engram autocatalysts”.

The light absorbing molecule itself shows autocatalytic growth, since this maximizes fitness of the spot, “growth autocatalysis”.

No micro-mutation is necessary if sufficient variety of random catalytic avalanches exists, and if harmful avalanches can be prevented spreading.

Page 46: How Natural Selection could Evolve Metabolism. Chrisantha Fernando School of Computer Science University of Birmingham, UK San Sebastian, September 2006

Fitness = Maximization of polymer mass ~ Spot Growth

Rate

Fitness = Maximization of polymer mass ~ Spot Growth

Rate Fitness = Sum over all i species of

Speciesi Length x [Speciesi] at end of trial. Excluding all food molecules.

Initial conditions as before,I.e. all food [molecule] = 100mM, except [abbb] = 0mM

Will the light absorbing reaction of abb be utilized to produce high energy species, as a side-effect of selection for growth rate?

Fitness = Sum over all i species of Speciesi Length x [Speciesi] at end of trial. Excluding all food molecules.

Initial conditions as before,I.e. all food [molecule] = 100mM, except [abbb] = 0mM

Will the light absorbing reaction of abb be utilized to produce high energy species, as a side-effect of selection for growth rate?

Page 47: How Natural Selection could Evolve Metabolism. Chrisantha Fernando School of Computer Science University of Birmingham, UK San Sebastian, September 2006

5Mass1

Gen

Fit

5Mass2

5Mass35Mass4

Gen

Gen Gen

No further adaptation in the whole trial!?!?!?

No further adaptation in the whole trial!?!?!?

No further adaptation in the whole trial!?!?!?

Why are avalanchesSo unlikely to be adaptive?

Page 48: How Natural Selection could Evolve Metabolism. Chrisantha Fernando School of Computer Science University of Birmingham, UK San Sebastian, September 2006

5Mass5 5Mass6

Page 49: How Natural Selection could Evolve Metabolism. Chrisantha Fernando School of Computer Science University of Birmingham, UK San Sebastian, September 2006

831.259

593

The Evolutionary History of Experiment 5Mass1

528

Light absorbing reaction lost.

Page 50: How Natural Selection could Evolve Metabolism. Chrisantha Fernando School of Computer Science University of Birmingham, UK San Sebastian, September 2006

The Evolutionary History of Experiment 5Mass2

139

abb not produced.

Page 51: How Natural Selection could Evolve Metabolism. Chrisantha Fernando School of Computer Science University of Birmingham, UK San Sebastian, September 2006

The Evolutionary History of Experiment 5Mass3

323

abb produced.

Page 52: How Natural Selection could Evolve Metabolism. Chrisantha Fernando School of Computer Science University of Birmingham, UK San Sebastian, September 2006

The Evolutionary History of Experiment 5Mass4

abb produced but lightabsorbing reaction lost

Page 53: How Natural Selection could Evolve Metabolism. Chrisantha Fernando School of Computer Science University of Birmingham, UK San Sebastian, September 2006

The Evolutionary History of Experiment 5Mass6

abb produced.

Page 54: How Natural Selection could Evolve Metabolism. Chrisantha Fernando School of Computer Science University of Birmingham, UK San Sebastian, September 2006

ConclusionConclusion

In some runs abb is produced, but some reactions loose the light absorbing reaction.

Further analysis is required to know how much heat dissipation is based on energy obtained from food molecules vs. from light.

Why are there such long periods without an adaptive avalanche, when networks are larger?

In some runs abb is produced, but some reactions loose the light absorbing reaction.

Further analysis is required to know how much heat dissipation is based on energy obtained from food molecules vs. from light.

Why are there such long periods without an adaptive avalanche, when networks are larger?

Page 55: How Natural Selection could Evolve Metabolism. Chrisantha Fernando School of Computer Science University of Birmingham, UK San Sebastian, September 2006

Thanks to…Thanks to…

Kepa Ruiz-Mirazo Jon Rowe Eors Szathmary Graham Cairns-Smith Guenter Wachtershauser

Kepa Ruiz-Mirazo Jon Rowe Eors Szathmary Graham Cairns-Smith Guenter Wachtershauser