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ScienceWeek
THEORETICAL BIOLOGY: ON SCALE AND COMPLEXITY
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
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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
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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
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