a model of chemical evolution by artificial selection for energy flux maximization chrisantha...

A Model of Chemical Evolution by Artificial

Selection for Energy Flux Maximization

A Model of Chemical Evolution by Artificial

Selection for Energy Flux Maximization

Chrisantha Fernando & Jon Rowe

School of Computer Science

University of Birmingham

UK

[email protected]

Dublin, 2006

Chrisantha Fernando & Jon Rowe

School of Computer Science

University of Birmingham

UK

[email protected]

Dublin, 2006

mailto:[email protected]

mailto:[email protected]

AimAim

To help chemists create dissipative structures capable of the ‘recursive

generation of functional constraints’ (RGFC). e.g. life.

To help chemists create dissipative structures capable of the ‘recursive

generation of functional constraints’ (RGFC). e.g. life.

Examples of Dissipative Structures not capable of RGFC

Examples of Dissipative Structures not capable of RGFC

Gravity

Convection

Convection

Arguably, clouds have not improved in 4 billion years.

What is the Minimal Dissipative Structure capable of RGFC?

What is the Minimal Dissipative Structure capable of RGFC?

Circulation must produce novel products capable of positive feedback on the circulation.

But lets be realistic.

Circulation must produce novel products capable of positive feedback on the circulation.

But lets be realistic. X+

An imaginary adaptive novelty in Cloudoid

Most random novel products are harmful

Most random novel products are harmful

X is likely to be either neutral or harmful.

The solution is to have

many clouds to prevent

Muller’s Ratchet.

X is likely to be either neutral or harmful.

The solution is to have

many clouds to prevent

Muller’s Ratchet.

X0/-

An imaginaryharmful or neutral novelty

X0/-

X0/-

X0/-

X0/-

X+

X0/-

X0/-

X0/-

X0/-

Low Rate of Novel Product Formation is Preferable.

Low Rate of Novel Product Formation is Preferable.

To prevent harmful products ‘drowning out’

the good ones.

To prevent harmful products ‘drowning out’

the good ones.

X0/-

Y+

But even then,the rare adaptive novelty is lost by dilution.

But even then,the rare adaptive novelty is lost by dilution.

X+

X/2+

X/2+

H2O

To prevent this, X must at least double with the doubling period

of the cloud

To prevent this, X must at least double with the doubling period

of the cloud

X+

X+

X+

H2O

X

The Cloud Selection MachineThe Cloud Selection Machine

Natural selection can apply if The entities self-replicate. Undergo heritable adaptive novelties.

Natural selection can apply if The entities self-replicate. Undergo heritable adaptive novelties.

QuickTime™ and aTIFF (LZW) decompressor

are needed to see this picture.

















Puddle

Puddle = non-dissipative cloud, e=0, f=0

Less dissipative cloudf2<f1

More dissipative cloudf2>f1





E1m1=f1

E2m2=f2



More dissipative cloudf2>f1. But proliferated, notself-replicated.

Generation 1

Generation 2





E2m2=f2

Selfreplicating

Type.



E2m2=f2: Proliferating Type.

Puddle



Puddle

The proliferating type only has one chancefor an adaptive novelty.

The self-replicating typehas two extra chances per generation of an adaptive novelty.

Cloudoids vs. CloudsCloudoids vs. Clouds

Cloudoids exhibit unlimited production of autocatalytic products, X.

Cloudoids self-replicate, clouds generally don’t. They proliferate.

So how do we make a real cloudoid? With a richer chemistry than that of clouds, and a

different energy source. No living system known feeds on gravity like clouds do.

Cloudoids exhibit unlimited production of autocatalytic products, X.

Cloudoids self-replicate, clouds generally don’t. They proliferate.

So how do we make a real cloudoid? With a richer chemistry than that of clouds, and a

different energy source. No living system known feeds on gravity like clouds do.

Multiple Sources for a ‘Rich’ Chemistry

Multiple Sources for a ‘Rich’ Chemistry

A.I. Oparin & J.B.S. Haldane (1924,1929). UV light energy G. Wachtershauser. FeS/H2S reducing power produces

COO-, -S-

, -COS-.

S. Miller. Electrical discharges. C. de Duve. Thioester metabolism on surfaces. H. Morowitz. Reverse Citric acid cycle on mineral surfaces. Decker, Ganti. Formose cycle feeding on Formaldehyde

from CO and H2O + light. Chyba and Astrobiology. Organics from space.

A.I. Oparin & J.B.S. Haldane (1924,1929). UV light energy G. Wachtershauser. FeS/H2S reducing power produces

COO-, -S-

, -COS-.

S. Miller. Electrical discharges. C. de Duve. Thioester metabolism on surfaces. H. Morowitz. Reverse Citric acid cycle on mineral surfaces. Decker, Ganti. Formose cycle feeding on Formaldehyde

from CO and H2O + light. Chyba and Astrobiology. Organics from space.

Given this chemical detail, how can a model help a chemist?

Given this chemical detail, how can a model help a chemist?

Identify capacity for RGFC in generative chemistries that are at least subject to conservation of mass and energy.

The chemist can look at what the imaginary chemist does with such imaginary chemistries to get RGFC, and wonder whether they can do the same with their pet chemistry.

Identify capacity for RGFC in generative chemistries that are at least subject to conservation of mass and energy.

The chemist can look at what the imaginary chemist does with such imaginary chemistries to get RGFC, and wonder whether they can do the same with their pet chemistry.

Our Imaginary ChemistryOur Imaginary Chemistry

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

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

Reversible exogonic reactions (heat producing) Irreversible endogonic reaction (‘light absorbing)

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

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

Reversible exogonic reactions (heat producing) Irreversible endogonic reaction (‘light absorbing)

kf = e-dG/RT

kb = 0.01, dG = (Gproducts - Greactants)

R = gas constant, T = 300K

Artificial SelectionArtificial Selection

abbb

ba, ababbb

How does novelty arise?How does novelty arise?

Control (Ideal) Experiment Allow the random production and deletion of novel

high flux reactions. Full Experiment

Allow N novel low flux reactions per generation. Then determine the high flux reactions in which novel

products take part, by assuming they react at random with proportion P of any existing species.

This results in a reaction avalanche.

Control (Ideal) Experiment Allow the random production and deletion of novel

high flux reactions. Full Experiment

Allow N novel low flux reactions per generation. Then determine the high flux reactions in which novel

products take part, by assuming they react at random with proportion P of any existing species.

This results in a reaction avalanche.

babb + abbb

babb

babb + ba ---> abb + abbbabb + ab ---> bbb + aa bbb + babb ---> perhaps more novel products. Etc… aa + ba ---> perhaps more novel products.

The Reaction Avalanche

Bolus ofabbb used.

Bolus of abbb not used

What does the group II network do?

What does the group II network do?

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.

Autocatalytic Behaviour ofGroup II networks

Autocatalytic Behaviour ofGroup II networks





The characteristic delay phase of autocatalysis.

Conclusion from Control Experiment

Conclusion from Control Experiment

Where there is no explicit engram that preserves novel reactions, with selection for high energy flux, and dilution pressure, the light absorbing species, the system evolves to autocatalytically produce abb. Food abbb utilizes high energy inherited matter to produce abb rapidly.

We have shown this selection regime is sufficient to account for the origin of growth autocatalysis.

Where there is no explicit engram that preserves novel reactions, with selection for high energy flux, and dilution pressure, the light absorbing species, the system evolves to autocatalytically produce abb. Food abbb utilizes high energy inherited matter to produce abb rapidly.

We have shown this selection regime is sufficient to account for the origin of growth autocatalysis.

The Full ExperimentThe Full Experiment

Now we run the same experiment, but where novel low flux reactions produce products that may or may not be autocatalytic and cross-catalytic.

Now we run the same experiment, but where novel low flux reactions produce products that may or may not be autocatalytic and cross-catalytic.

A Problem with the generative algorithm.

A Problem with the generative algorithm.

bbbbba + ab ‡ ab + abbb,

All novel high flux reactions must be irreversible to prevent

this unrealistic solution.

All novel high flux reactions must be irreversible to prevent

this unrealistic solution.

More cheating!More cheating!

X + ba X + ab. Selfish parental adaptations. Rare reactions directly provided a slight advantage

to the parent.

Therefore, rare reaction rate set to zero, and initial concentration of product set to 0.000001 instead of 0.01.

When this was done, none of the ‘cloud type’ adaptations were produced!!! WHY????

X + ba X + ab. Selfish parental adaptations. Rare reactions directly provided a slight advantage

to the parent.

Therefore, rare reaction rate set to zero, and initial concentration of product set to 0.000001 instead of 0.01.

When this was done, none of the ‘cloud type’ adaptations were produced!!! WHY????

The difficulty with evolving a ‘cloud type’ adaptation.

The difficulty with evolving a ‘cloud type’ adaptation.

1. All three reactions must occur at once.2. NOT ONLY THIS!!!! The kinetics must be such that k4 is high

in comparison to k5. Infact [abbb] > k4/k5, k4/k5 is the ‘food threshold’ of the autocatalytic particle [babb] above which [abbb] must be for babb to not decay to zero.• To allow any chance of this happening, we have to increase

the rates of novel high flux reactions.

abbb + ba --> babb + ba

k4 = 1, k5 = 1 k4 = 2, k5 = 1

k4 = 2.5, k5 = 1 k4 = 3, k5 = 1

Networks Evolved with Stringent Parameters

Networks Evolved with Stringent Parameters

Zero rare flux. Very low initial product concentration

(0.000001 M, still unrealistically high) High flux (1000x to 5000x greater than

before) and (almost) irreversible novel reactions, (backrate = 10-6 sec-1 M-1)

Zero rare flux. Very low initial product concentration

(0.000001 M, still unrealistically high) High flux (1000x to 5000x greater than

before) and (almost) irreversible novel reactions, (backrate = 10-6 sec-1 M-1)

[abbb]

Fitness = 1500

[abb]

Network 12

abbb only

Cheating again!

• With these constraints, ‘cloud type’ adaptations not yet evolved.

• Is this because three different serendipitous types of reaction must simultaneously arise?

• ‘Cheat’ solutions were much more likely.

• As network size grows, P = 0.01 produces too much interference, i.e. the mean avalanche sizes suitable for a small network are not suitable for larger ones.

•Perhaps if MOST interactions were weak, this would be solved. We have drawn the kinetics from a uniform distribution, not a log-normal distribution for example.

Lessons for the Chemist?

For RGFC, the generative chemistry must be capable of unlimited production of engram autocatalysts with achievable food threshold, and capable of suitable activity at the higher level.

Dilution and selection for high energy flux should result in the entire system exhibiting growth autocatalysis because this results in efficient abb production.

Very large population sizes will be required due to the requirement for 3 novel reactions to occur at once, thus microfluidics will be required.

Alternatively, one should allow selection for pre-adaptations, e.g. generic autocatalysts that might later be able to obtain the right flask level properties, e.g. RNA. This requires a different fitness function.

For RGFC, the generative chemistry must be capable of unlimited production of engram autocatalysts with achievable food threshold, and capable of suitable activity at the higher level.

Dilution and selection for high energy flux should result in the entire system exhibiting growth autocatalysis because this results in efficient abb production.

Very large population sizes will be required due to the requirement for 3 novel reactions to occur at once, thus microfluidics will be required.

Alternatively, one should allow selection for pre-adaptations, e.g. generic autocatalysts that might later be able to obtain the right flask level properties, e.g. RNA. This requires a different fitness function.

Thanks to…..Thanks to…..

Jon Rowe Kepa and Xabier (San Sabastian, Autonomy

Workshop, Alife X) Eors Szathmary Hywel Williams Alex Penn Tibor Ganti, Guenter Wachtershauser,

Robert Hazen.

Jon Rowe Kepa and Xabier (San Sabastian, Autonomy

Workshop, Alife X) Eors Szathmary Hywel Williams Alex Penn Tibor Ganti, Guenter Wachtershauser,

Robert Hazen.

a model of chemical evolution by artificial selection for energy flux maximization chrisantha...

Documents

harmful x

x x2 h2oh2o slide

cloudoid slide

clouds cloudoids

y slide

neutral novelty slide

dissipative cloud f

harmful products