[Paleopsych] SW: On Scale and Complexity

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Theoretical Biology: On Scale and Complexity
http://scienceweek.com/2005/sw050729-3.htm

    The following points are made by Neil D. Theise (Nature 2005
    435:1165):
    1) Complexity theory, which describes emergent self-organization of
    complex adaptive systems, has gained a prominent position in many
    sciences. One powerful aspect of emergent self-organization is that
    scale matters. What appears to be a dynamic, ever changing
    organizational panoply at the scale of the interacting agents that
    comprise it, looks to be a single, functional entity from a higher
    scale. Ant colonies are a good example: from afar, the colony appears
    to be a solid, shifting, dark mass against the earth. But up close,
    one can discern individual ants and describe the colony as the
    emergent self-organization of these scurrying individuals. Moving in
    still closer, the individual ants dissolve into myriad cells.
    2) Cells fulfill all the criteria necessary to be considered agents
    within a complex system: they exist in great numbers; their
    interactions involve homeostatic, negative feedback loops; and they
    respond to local environmental cues with limited stochasticity
    ("quenched disorder"). Like any group of interacting individuals
    fulfilling these criteria, they self-organize without external
    planning. What emerges is the structure and function of our tissues,
    organs and bodies.
    3) This view is in keeping with cell doctrine -- the fundamental
    paradigm of modern biology and medicine whereby cells are the
    fundamental building blocks of all living organisms. Before cell
    doctrine emerged, other possibilities were explored. The ancient
    Greeks debated whether the body's substance was an endlessly divisible
    fluid or a sum of ultimately indivisible subunits. But when the
    microscopes of Theodor Schwann (1810-1882) and Matthias Schleiden
    (1804-1881) revealed cell membranes, the debate was settled. The
    body's substance is not a fluid, but an indivisible box-like cell: the
    magnificently successful cell doctrine was born.
    4) But a complexity analysis presses for consideration of a level of
    observation at a lower scale. At the nanoscale, one might suggest that
    cells are not discreet objects; rather, they are dynamically shifting,
    adaptive systems of uncountable biomolecules. Do biomolecules fulfill
    the necessary criteria for agents forming complex systems? They
    obviously exist in sufficient quantities to generate emergent
    phenomena; they interact only on the local level, without monitoring
    the whole system; and many homeostatic feedback loops govern these
    local interactions. But do their interactions display quenched
    disorder; that is, are they somewhere between being completely random
    and rigidly determined? Analyses of individual interacting molecules
    and the recognition that at the nanoscale, quantum effects may have a
    measurable impact, suggest that the answer is yes.[1-3]
    References:
    1. Theise N. D. & d'Inverno, M. Blood Cells Mol. Dis. 32, 17-20 (2004)
    2. Theise N. D. & Krause D. S. Leukemia 16, 542-548 (2002)
    3. Kurakin A. Dev. Genes Evol. 215, 46-52 (2005)
    Nature http://www.nature.com/nature
    --------------------------------
    Related Material:
    PHYSICS AND COMPLEXITY
    The following points are made by Gregoire Nicolis (citation below):
    1) For the vast majority of scientists physics is a marvelous
    algorithm explaining natural phenomena in terms of the building blocks
    of the universe and their interactions. Planetary motion; the
    structure of genetic material, of molecules, atoms or nuclei; the
    diffraction pattern of a crystalline body; superconductivity; the
    explanation of the compressibility, elasticity, surface tension or
    thermal conductivity of a material, are only a few among the
    innumerable examples illustrating the immense success of this view,
    which presided over the most impressive breakthroughs that have so far
    marked the development of modern science since Newton.
    2) Implicit in the classical view, according to which physical
    phenomena are reducible to a few fundamental interactions, is the idea
    that under well-defined conditions a system governed by a given set of
    laws will follow a unique course, and that a slight change in the
    causes will likewise produce a slight change in the effects. But,
    since the 1960s, an increasing amount of experimental data challenging
    this idea has become available, and this imposes a new attitude
    concerning the description of nature. Such ordinary systems as a layer
    of fluid or a mixture of chemical products can generate, under
    appropriate conditions, a multitude of self-organization phenomena on
    a macroscopic scale -- a scale orders of magnitude larger than the
    range of fundamental interactions -- in the form of spatial patterns
    or temporal rhythms.
    3) States of matter capable of evolving (states for which order,
    complexity, regulation, information and other concepts usually absent
    from the vocabulary of the physicist become the natural mode of
    description) are, all of a sudden, emerging in the laboratory. These
    states suggest that the gap between "simple" and "complex", and
    between "disorder" and "order", is much narrower than previously
    thought. They also provide the natural archetypes for understanding a
    large body of phenomena in branches which traditionally were outside
    the realm of physics, such as turbulence, the circulation of the
    atmosphere and the oceans, plate tectonics, glaciations, and other
    forces that shape our natural environment: or, even, the emergence of
    replicating systems capable of storing and generating information,
    embryonic development, the electrical activity of brain, or the
    behavior of populations in an ecosystem or in an economic environment.
    Adapted from: Gregoire Nicolis: in: Paul Davies (ed.): The New
    Physics. Cambridge University Press 1989, p.316
    --------------------------------
    Related Material:
    ON EVOLUTION AND COMPLEXITY
    The following points are made by N. Barton and W. Zuidema (Current
    Biology 2003 13:R649):
    1) A central goal of evolutionary biology is to explain the origin of
    complex organs -- the ribosomal machinery that translates the genetic
    code, the immune system that accurately distinguishes self from
    non-self, eyes that can resolve precise images, and so on. Although we
    understand in broad outline how such extraordinary systems can evolve
    by natural selection, we know very little about the actual steps
    involved, and can hardly begin to answer general questions about the
    evolution of complexity. For example, how much time is required for
    some particular structure to evolve?
    2) Complex systems -- systems whose function requires many
    interdependent parts -- are vanishingly unlikely to arise purely by
    chance. Darwin's explanation of their origin is that natural selection
    establishes a series of variants, each of which increases fitness.
    This is an efficient way of sifting through an enormous number of
    possibilities, provided there is a sequence of ever-increasing fitness
    that leads to the desired feature. To use Sewall Wright's metaphor,
    there must be a path uphill on the "adaptive landscape".
    3) The crucial issue, then, is to know what variants are available --
    what can be reached from where -- and what is the fitness of these
    variants. Is there a route by which fitness can keep increasing?
    Population genetics is not much help here. Given the geometry defined
    by mutation and recombination, and given the fitnesses, we can work
    out how a population will change simply by following the proportion of
    different types through time. But understanding how complex features
    evolve requires plausible models for the geometry of the adaptive
    landscape, which population genetics by itself does not provide.
    4) "Artificial Life" -- the study of life as it could be --provides a
    variety of such models. For instance, Thomas Ray (1992) developed a
    model called "Tierra", where digital creatures are little computer
    programs that copy themselves and compete with each other for memory
    and processing time. Fitness here --just as in the real world -- is
    defined very indirectly by the rate of self-replication of the
    creatures relative to others. Ray's creatures evolved strategies to
    hinder competitors and even to parasitize other creatures. Karl Sims
    (1994) created a simulated physical world in which "digital creatures"
    successfully evolve both their bodies and brains in order to beat
    other creatures in a variety of tasks such as swimming, walking and
    jumping. Lipson and Pollack (2000), in a recent follow-up study,
    actually made such walking creatures as little robots and showed that
    the evolved locomotion strategies work even in the real world. Fitness
    in these models is defined implicitly by the complex relation between
    brain and body architecture and the resulting way of moving.
    Current Biology http://www.current-biology.com