Complexity, Self-organization, and Evolution - PowerPoint PPT Presentation

1 / 35
About This Presentation
Title:

Complexity, Self-organization, and Evolution

Description:

'I daresay you haven't had much practice,' said the Queen. ... Yet: behavior of ant colonies can be astounding. ... Slavery of other ant species. Farming of ... – PowerPoint PPT presentation

Number of Views:107
Avg rating:3.0/5.0
Slides: 36
Provided by: drrichard
Category:

less

Transcript and Presenter's Notes

Title: Complexity, Self-organization, and Evolution


1
Complexity, Self-organization,and Evolution
  • Rich Strauss

Alice laughed There's no use trying,' she
said 'one can't believe impossible things.'
'I daresay you haven't had much practice,' said
the Queen. 'When I was younger, I always did it
for half an hour a day. Why, sometimes I've
believed as many as six impossible things before
breakfast.' Lewis Carroll Alice in
Wonderland
2
Outline
  • Evolution and natural selection can it work?
  • Emergent properties and complexity.
  • Genetic networks
  • Boolean N-K networks
  • CAS complex adaptive systems
  • Life at the edge of chaos
  • Autocatalytic systems
  • Artificial life
  • Whats the point?

3
Theory of evolution
  • Mayr theory of evolution can be divided into
    five distinct subtheories.
  • Somewhat independent of one another.
  • Explains why parts of the theory can be so
    divisive among those who dont question other
    parts.
  • Subtheories
  • Evolution, as such, occurs.
  • Common descent every group of organisms is
    descended from common ancestor, including all of
    life.
  • Multiplication of species speciation.
  • Gradualism accumulation of small changes.
  • Natural selection genetic variation
    differential reproduction.

4
Natural selection
  • Model originated with Darwin as a verbal
    argument process to account for pattern.
  • Augmented during the New Synthesis with
    quantitative models from population genetics.
  • Basic model of Darwinian natural selection
  • Natural selection is the inevitable outcome of 4
    features of organisms.
  • Proposed as four postulates by Darwin.
  • Now understood as basic facts about the natural
    world.

5
Natural selection
  • (1) Organisms vary no two living things are
    exactly alike.
  • DNA and mutations.
  • Sexual reproduction
  • Independent assortment.
  • Crossing-over and recombination.
  • Combining two unique haploid genotypes into a
    novel diploid genotype.
  • (2) Variation among organisms is heritable
  • Many differences are due to environmental
    variation, but many (most?) are due to
    differences in genotypes.
  • Genetic differences among adult organisms in any
    generation will produce differences among their
    offspring.

6
Natural selection
  • (3) Excess progeny are produced
  • Organisms face a struggle for existence
    (Malthus).
  • In every generation, far more offspring are born
    than ever survive to reproduce, due to
  • Overproduction more offspring than the
    environment can support.
  • Biotic interactions
  • Competition within and among species for common
    resources.
  • Predation, parasitism, pathogenesis.
  • Selective mating via sexual selection not all
    individuals are suitable as mates.

7
Natural selection
  • (4) Reproductive output varies among individuals
    based on their heritable differences
  • Survival and reproduction are non-random
    processes (at least in part)
  • Some individuals have traits that allow them to
    survive and reproduce better than do others in
    the population.
  • Individuals with the most favorable traits will,
    on average, produce more offspring than do others
    in the population.
  • Variation in competence might be due to
  • Different abilities in competition with other
    genotypes.
  • Differential survival under onslaught of
    parasites, predators, diseases, changes in
    physical environment.
  • Variable reproductive competence.
  • Variable ability to find and penetrate new
    habitats.

8
Natural selection
  • If the four factors hold, the inevitable result
    is natural selection favorable traits will
    increase in frequency in the population over
    time.
  • Accounts for much of what we observe in nature.
  • Basis of agriculture and animal husbandry.
  • Basis of highly successful optimization
    procedures genetic algorithms and evolutionary
    programming.
  • "How extremely stupid not to have thought of
    that!
  • Thomas Henry Huxley, 1865

9
Natural selection
  • But selection cant be perfect
  • Cant result in perfect adaptation because of
    several types of constraints or limitations
  • Time lags
  • Every generation of organisms is adapted to the
    conditions that existed in previous generations.
  • Selection usually acts slowly relative to the
    rate of environmental change.
  • Result organisms may not be perfectly adapted
    to existing conditions.
  • Mechanical constraints
  • Organisms are constructed of materials that
    have limits to their physical properties.
  • Many phenotypes are physically impossible e.g.,
  • Limits to insect body size because of external
    skeleton.
  • Limits to the size of terrestrial vertebrates
    because of properties of bone.

10
Natural selection
  • Genetic/epigenetic linkages between traits
  • Complex genetic/biochemical/developmental/
    functional relationships among traits.
  • Particular genetic variation may produce an
    adaptive change in one trait, but a deleterious
    change in another.
  • Result often is a tradeoff (compromise) among
    traits.
  • All heritable traits are filtered through the
    phenotype.
  • Thousands of possible heritable traits.
  • Mediated and buffered by development and other
    linkages.
  • Number of successful offspring usually very
    small.
  • Crude filter.

11
Natural selection
  • So can natural selection, by itself, really
    work?
  • Can it produce the extremely complex organisms
    currently living?
  • Can it account for the amazingly detailed
    convergences of form and function in different
    groups of organisms?
  • Many evolutionary biologists have thought not.
  • Other processes proposed
  • E.g., heterochrony, developmental canalization.
  • Until recently, none satisfactorily accounted for
    biological complexity.

12
Emergent properties
  • Consider ants simple nervous systems.
  • Individual ants regarded as unconscious
    automatons.
  • Interactions not very complex
  • Signal in only a few (5-8) different ways.
  • Yet behavior of ant colonies can be astounding.
  • Colonies may contain 5,000-2,000,000 individuals.
  • Behavioral repertoires include
  • Elaborate nest construction and defense. E.g.
  • Columnar or arch-shaped structures.
  • Wedge-shaped nests oriented N-S or E-W direction.
  • Efficient foraging behavior.
  • Slavery of other ant species.
  • Farming of fungi and aphids.
  • Emergent properties collective properties not
    predictable from examining individual organisms.

13
Emergent properties
  • Long known from inanimate objects e.g.,
  • Vortex spontaneously forms above drain in tub.
  • Small perturbations of water and air determine
    direction of rotation (hallmark of chaotic
    behavior).
  • Properties described by laws of fluid dynamics.
  • Cant be predicted from knowledge of properties
    of water molecules and their interactions.
  • Reductionist approach doesnt work.
  • Emergent properties seen almost everywhere
  • Avalanches, stream flow, air turbulence, weather
    patterns, formation of spiral galaxies.
  • Living cells, ecological systems, human
    societies.

14
Emergent properties
  • Studies of emergent properties lead to basic
    principles
  • Emergent properties are characteristic of complex
    systems.
  • System of sufficient complexity will typically
    have properties that cant be explained by
    breaking the system down into its elements.
  • Complex systems are self-organizing.
  • When system becomes sufficiently complex, order
    will spontaneously appear.
  • Often have threshold effects.
  • Till 20 yrs ago, little hope of understanding
    appearance of emergent properties in complex
    systems.
  • Could be described analytically.
  • But if couldnt be analyzed in reductionist
    manner, couldnt really be understood.

15
Emergent properties
  • Advancement that led to change development of
    modern high-speed supercomputers.
  • Possible to create models (simulations) of
    complex systems.
  • Development of a mathematical field unknown
    20 yrs ago complexity theory.
  • Applied to numerous disciplines
  • Physics, astrophysics e.g. galaxy formation.
  • Behavior of stock markets, traffic congestion,
    etc.
  • Behavior of social insects ecological systems
    genomic organization.
  • Relates to questions concerning the evolution of
    life.

16
Genetic networks
  • Few traits caused by action of single gene.
  • Most caused by many genes acting in concert.
  • Many genes dont code for traits.
  • Genes interact
  • Turn one another on and off.
  • Activation and inhibition control cellular
    development and activity.
  • Generally not possible to find a gene for a
    certain trait.
  • Most traits produced by networks of genes.
  • Single gene may be part of gt1 network.
  • May cause traits to be linked, functionally or
    fortuitously.
  • E.g. white cats usually deaf.

17
Genetic networks
  • Numbers of genes tend to be large
  • 75,000 genes (?) in human genome.
  • Bacteria have hundreds of genes.
  • Number of possible interactions much greater.
  • Genome of any organisms can be regarded as a
    complex system.
  • Difficult to determine how 5 interconnected
    objects may behave.
  • Hopeless to use reductionist methods to
    understand workings of genome.

18
Genetic networks
  • Thus work in complex systems might shed light on
    problems of molecular biology, in the context of
    evolution.
  • Stuart Kauffman
  • Theoretical biologist, Santa Fe Institute.
  • Seminal and influential work.
  • Constructed computer models relating to
  • Origin of life.
  • Interconnections between genes.
  • Evolvability of genomes that are interconnected
    in various ways.

19
Genetic networks
  • Kauffman began work while a medical student in
    1960s.
  • Based on Prigogine, hypothesized that
  • Genetic networks would be self-organizing.
  • Certain types of order should arise
    spontaneously.
  • Should be possible to determine emergent
    properties.
  • Properties would have nothing to do with natural
    selection.
  • Once order appeared, might be subject to
    selection (exaptation).
  • Worked on slow mainframe computers.
  • Fortuitously began with very simple networks.
  • Results exceeded his expectations.

20
Genetic networks
  • Introduction to boolean N-K networks
  • Simplifying assumptions
  • N genes can turn each other on or off.
  • Vary the mean number of inputs (K) to each gene.
    Extremes
  • No gene has influence on any other (K0).
  • Every gene influences every other gene (KN).

21
Genetic networks
  • Basic results
  • K1 network quickly freezes.
  • All genes remain either on or off.
  • Would result in cell death.
  • K3 or more network becomes chaotic.
  • System passes randomly through enormous number
    of states, without repeating.
  • Behavior extremely sensitive to initial states of
    genes.
  • Not good model for cell no coordination between
    states.
  • K2 genes cycle through limited number of
    different states.
  • Orderly activity, could serve as model for gene
    activity.

22
(No Transcript)
23
(No Transcript)
24
Genetic networks
  • Responses to perturbations
  • K1 stable.
  • System quickly returns to original state.
  • K3 or more chaotic.
  • System usually moves to a completely different
    set of states.
  • K2 stable adaptive.
  • System often returns to original state, but
    occasionally jumps to a new stable state.
  • Expected behavior under Wrights adaptive
    landscape model.

25
Genetic networks
  • First characterization of emergent property of a
    network
  • Out of many different kinds of configurations
    that a system might have, one has special
    properties.
  • If K is variable, configuration can arise
    naturally.
  • I.e., is a CAS complex adaptive system.
  • N-K network models have been major source of work
    in complex systems for gt20 yrs.
  • Generalization optimal number of inputs varies,
    depending on nature of network.
  • For any network, there is always some optimum
    that defines borderline between frozen genetic
    activity and chaos.

26
Edge of chaos
  • Kaufmann Life exists at the edge of chaos.
  • Living systems seek the edge of chaos via
    natural selection.
  • Effectiveness of natural selection depends on
    interconnectedness of genes.
  • Nature of interconnectedness affects
    evolvability.
  • Makes life and evolution possible.

27
Network theory for origin of life
  • Life may be emergent property of certain kinds of
    complex systems autocatalytic.
  • Observation certain biological molecules (e.g.,
    proteins) can catalyze the formation of other
    molecules.
  • If reaction proceeds more rapidly when C is
    present, and C remains unchanged and available,
    then C is a catalyst.
  • Can work in various ways, e.g. as template.
  • Details arent important.
  • In living cells, reaction rates can be 108-1011
    times faster in presence of catalyst.
  • Action of catalysts is foundation for life.

28
Network theory for origin of life
  • Model goes something like this
  • Suppose chance that one molecule chosen at random
    will act as catalyst on second molecule chosen at
    random is 10-6.
  • If few kinds of molecules are present, probably
    no catalytic action combination will be rare.
  • As number of different kinds of molecules
    increases, number of interactions (possible
    catalytic reactions) increases exponentially.
  • When certain degree of complexity is attained
    (e.g., 106 interactions), probability of
    catalysis increases suddenly threshold effect.
  • Have inert system added a few kinds of elements
    system springs into life becomes autocatalytic.

29
Network theory for origin of life
  • Autocatalytic systems are complex adaptive
    systems capable of mutating and evolving.
  • Thus life and evolvability are emergent
    properties that arise when systems of biological
    chemicals attain certain degree of complexity.
  • Combinatorial explosion.
  • E.g., Cambrian explosion of basic body plans.
  • Brain function in humans?

30
Artificial life
  • Number of attempts to simulate evolution via
    computer.
  • One of most successful
  • Tierra Thomas Ray (1989)
  • Organism is computer program, with three genes
    (function), 80 computer instructions.
  • Replicate
  • Mutate
  • Feed on energy provided by CPU.
  • Organisms reproduce, compete for common resources.

31
Tierra
  • Within a few thousand generations, successful
    mutants appeared
  • 79 computer instructions, reproduced more
    rapidly.
  • Then smaller, more efficient organisms evolved.
  • Parasites used reproductive genes of larger
    organisms.
  • Hosts evolved defenses against parasites new
    forms of parasites evolved (arms race).
  • Eventually was able to observe 29,000 different
    types of electronic organisms.
  • About 300 different sizes.

32
Tierra
  • Moved to different kinds of computers, changing
    the programming language.
  • Much diversity among computers
  • Digital evolution slow and gradual on some, rapid
    and punctuated on others, static on others.
  • Difficult to account for differences.
  • Ongoing work Network Tierra, using
    interconnected computers (different habitats).
  • Ancestor introduced with 640 computer
    instructions 2 cell types, 10 cells (8 sensory,
    2 reproductive).
  • Offspring produced from reproductive cells can
    migrate or not reaper at each computer node.
  • Amazing results
  • New forms, some of which couldnt interbreed
    (species).
  • New cell types e.g., for more efficient
    reproduction and energy use.
  • Tremendous diversity in evolvability.

33
So whats the point?
  • Complexity networks represent simplified models
    of evolution.
  • Similarities between artificial and real
    evolutionary patterns may reveal general
    principles about evolution and evolvability.
  • Many complexity scientists view biological
    structure and evolution as complex adaptive
    systems that have predictable emergent
    properties.
  • Account for, e.g., rampant convergence of
    strikingly similar form in very different groups
    of organisms.
  • If so, life is not surprising.

34
So, whats the point?
  • Principles of self-organization relieve us from
    having to view natural selection as operating
    separately on every small detail of an organism.
  • Self-organization is present in almost every
    level of natural evolution
  • Gene regulation networks
  • Protein interaction networks
  • Metabolic pathways
  • Cellular organization, etc.
  • Nature evolves instructions that produce
    organisms by a process of self-organization, and
    its the instructions that evolve.
  • Issues of complexity and self-organization have
    not been integrated into standard evolutionary
    biology.

35
Darwinism vs complexity
  • Neo-Darwinism
  • Evolution chance mutations natural selection.
  • Life may be a lucky accident.
  • Complexity takes eons to develop.
  • Structure is conditional upon history.
  • Complexity
  • Natural principles of self-organization create
    ordered patterns in complex systems.
  • Evolution self-organization drive systems to
    the edge of chaos, where maximal adaptability
    is possible.
  • Life is expected, and convergent properties are
    expected.
  • We are At Home in the Universe (Kauffman, 1995).
Write a Comment
User Comments (0)
About PowerShow.com