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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
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!
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