Sci Am. Aug;(2) Antichaos and adaptation. Kauffman SA(1). Author information: (1)University of Pennsylvania, School of Medicine. Erratum in . English. Etymology. anti- + chaos, coined by Stuart Kauffman in Antichaos and Adaptation (published in Scientific American, August ). Antichaos and Adaptation Biological evolution may have been shaped by more than just natural selection. Computer models suggest that.
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A number of solid state physicists, including Deitrich Stauffer of the University of Koeln and Bernard Derrida and Gerard Weisbuch of the Ecole Normale Superieure in Paris, have studied the effects of biased functions. Increasing the proportion of canalizing functions used in a network can therefore drive the system toward a phase transition zdaptation chaos and order.
It will consequently cycle repeatedly through the same states. Their structure degenerates into isolated feedback loops that do not interact.
In such poised systems, most mutations have small consequences because of the systems’ homeostatic nature. That order, adapttion course, is much the same as I have described for networks with low connectivity.
As predicted, the length of antichaod cycles does seem to be proportional to roughly the square root of the amount of DNA in the cells of bacteria and higher organisms.
In the ordered ajd of networks adapfation two or fewer inputs per element, there is little sensitivity to initial conditions: After receiving an appropriate stimulus, a gene in a eukaryotic cell needs about one to 10 minutes to become active. During the past two decades, there has been an explosion of interest in such systems throughout the natural and social sciences.
In contrast, other simple mathematical models for genomic systems predict that the number of cell types would increase exponentially with the number of genes. Poised systems will therefore typically adapt to a changing environment gradually, but if necessary, they can occasionally change rapidly. The more compressed the code, the less capacity it has to evolve.
Antichaos and Adaptation
But as frozen components melt, more complicated dynamics involving the complex coordination of activities throughout a network become adapyation. Highly ordered networks are too frozen adaptxtion coordinate complex behavior. In a Boolean network, each variable is regulated by others that serve as inputs. Networks on the boundary between order and chaos may have the flexibility to adapt rapidly and successfully through the accumulation of useful variations.
Consequently, a cell should run through all the gene expression patterns of its type in roughly to 3, minutes. If the hypotheses continue to hold up, biologists may have the beginnings of a comprehensive theory of genomic organization, behavior and capacity to evolve.
A genome that containsgenes has the potential for at leastpatterns of gene expression. Christopher Langton, a computer scientist at Los Alamos National Laboratory, has introduced an analogy that helps one think about the change between order and disorder in different ensembles of networks.
Hence, all the cell types in an organism should express most of the same genes. The network behaves chaotically. In fact, by conservative estimates, the number of cell types appears to increase at most as a linear function.
Across many phyla, the number of cell types seems to increase with approximately the square root of the number of genes per cell that is, with the number of genes raised to a fractional power that is roughly one half.
Antichaos and adaptation.
The stability of attractors subjected to minimal perturbations can differ. By the most recent count, humans have about distinct cell types, so that prediction is also in the right range. When K drops to two, however, the properties of random Boolean networks change abruptly: Chaos, fascinating as it is, is only part of the behavior of complex systems. The natural suggestion is that a cell type corresponds to a state-cycle attractor: As far as biologists know, cell differentiation in multicellular organisms has been fundamentally constrained and organized by successive branching pathways since the Cambrian period almost million years ago.
antichaos – Wiktionary
Moreover, the expected sizes of the unfrozen islands in the gene systems come close to predicting the sizes of such avalanches. Thus, bacteria have one or two cell types, sponges have perhaps from 12 to 15 and annelid worms have about Of these, the self-regulating network of a genome the complete set of genes in an organism offers a good example of how antichaos may govern development.
Typically only a few percent of the genes should show different activities. As Darwin taught, mutations and natural selection can improve a biological system through the accumulation of successive minor variants, just as tinkering can improve technology. Recently my colleague Sonke Johnsen of the University of Pennsylvania and I have found further evidence of evolution proceeding to the edge of chaos.
The average length of a state cycle in the network is roughly the square root of that number, about states. Nevertheless, on the basis of mathematical models for biological systems that exhibit self-organization, one can make predictions that are consistent with the observed properties of organisms.
Initial conditions that are very much alike may have markedly different outcomes. At the next clocked moment, the elements turn on or off in accordance with their individual functions.
But a stable cell type persists in expressing restricted sets of genes. All the network populations improved at playing the games faster than chance alone could accomplish.
The hypothesis is bold, perhaps even beautiful, but is it true? In a canalizing ensemble, however, each model cell can differentiate directly into only a few alternative cell types because each attractor is “near” only a few others.
At that phase transition, both small and large unfrozen islands would exist. It has been more than 20 years since I discovered those features of random networks, and they still surprise me. We have begun studying the question by making Boolean networks play a variety of games with one another [see box on opposite page]. To understand how self-organization can be a force in evolution, a brief overview of complex systems is necessary.
Antichaos and Adaptation – Stuart Alan Kauffman – Google Books
Minimal perturbations cause adaptaton small avalanches and a few large avalanches. Highly chaotic networks would be so disordered that control of complex behaviors would be hard to maintain.
The frozen core creates interlinked walls of constancy that “percolate” or grow across the entire system. If such a change does not move a network outside its original basin of attraction, the network will eventually return to its original state cycle. Alternatively, the AND function declares that a variable will wdaptation active only if all its inputs are currently active.