Difficult questions in evolution

Eörs Szathmáry

Biological Insitute,

Eötvös University, Budapest

Evolutionary Systems Research Group

MTA Ecological Research Centre

Units of evolution: a tacit

‘algorithm’

Some hereditary traits affect

survival and/or fertility

1. multiplication

2. heredity

3. variability

1. ORIGIN OF LIFE

2. EVOLVABILITY

3. EVOLUTION IN LEARNING

ORIGIN OF LIFE

The centrality of autocatalysis

• Replication from a chemical point of view

always rests on autocatalysis

• The basic form is

A + X 2 A + Y

• very important for biology

• Much more general than DNA

The formose ‘reaction’

formaldehyde

glycolaldehyde

autocatalysis

Butlerow, 1861

Chemical evolution

• May have been very different from

contemporary biochemistry!!!

A crucial insight: Eigen’s paradox

(1971)

• Early replication must have been error-prone

• Error threshold sets the limit of maximal genome size to <100 nucleotides

• Not enough for several genes

• Unlinked genes will compete

• Genome collapses

• Resolution???

Molecular hypercycle (Eigen,

1971)

autocatalysis

heterocatalytic

aid

Parasites in the hypercycle (JMS)

parasite

short circuit

The minimal GCCU/GUGGC

ribozyme system

• The smallest ribozyme that

carries out a complex group

transfer is the sequence

GUGGC-3’,

• Acting to aminoacylate GCCU-

3’ (and further reactions) in the

presence of substrate PheAMP.

The trait group model (Wilson, 1980)

Random dispersal

Harvest

Mixed global pool

Mixed global pool

A molecular rendering of the

Wilson model

Stochastic corrector model

(SCM)

• Independently reassorting genes

• Selection for optimal gene composition between compartments

• Competition among genes within the same compartment

• Stochasticity in replication and fission generates variation on which

natural selection acts

• A stationary compartment population emerges

Gánti’s chemoton model (1971, 1974)

ALL THREE SUBSYSTEMS ARE AUTOCATALYTIC

template

copying

metabolism

membrane

growth

Pathways of supersystem evolution

boundary

template

metabolism M B

B T

M T M B T

INFRABIOLOGICAL SYSTEMS

EVOLVABILITY

Empirical modularity

MVG “modularity language”

Modularity Varying Goals: goals change

over time but share the same subgoals

Evolution of genetic triggers

Facilitated variation

EVOLUTION IN LEARNING?

The Hebb synapse

Bayes and Darwin

Bayes and selection (e.g. Harper,

2010)

Collaborative ProjectGA no 308943

Only units of selection: Changeux 1973

• There is selection, in the

sense of Price.

• This fundamentally

important limitation is

clearly stated: "an

organism can not learn

more than is initially

present in its pre-

representations."

• A one-shot game!

1. Known: The selection loop

Basal ganglia SELEX

2. Known: Autoassociative

(“attractor”) networks

• Palimpsest memory

possible!

3. Known: “copying” (accurate

information transmission)

• From short- to long-term memory

• Specialized areas to the global workspace

and back

• Implicit working memory to conscious WM

A “proof of principle” toy model

• For complex thinking

• Language learning

• Insight problems

• Theory building

Population of networks: selection

Evolution

1. Provocation

2. Output of prototype variant

3. Evaluation of population output

4. Selection of best pattern

5. Retraining of networks with

mutated pattern

6. Provocation of population with

mutated pattern

Evolutionary parallel processing

• Offline thinking with internal evaluation

• Parallel evaluations (ratio to population

size)

• Unconscious activity dynamics critically

important

• NOT limited by working memory

• Sleep promotes insight

• Selection for all the above!!!

The interplay of Hebb and Darwin

DNA replication Neuronal copying

local influence non-local influence

THANK YOU FOR YOUR

ATTENTION!