Sci Am. Aug;(2) Antichaos and adaptation. Kauffman SA(1). Author information: (1)University of Pennsylvania, School of Medicine. Erratum in . English[edit]. Etymology[edit]. 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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By the most recent count, humans have about distinct cell types, so that prediction is also in the right range. One such rule is the Boolean OR function, which says that a variable will be active if any of its input variables is active.

A random network is one sampled at random from this ensemble. Sometimes at least the answer is yes. The studies supporting these conclusions remain tentative and incomplete. Recently my colleague Sonke Johnsen of the University of Pennsylvania and I have found further evidence of evolution proceeding to the edge of chaos. Moreover, the expected sizes of the unfrozen islands in the gene systems come close to predicting the sizes of such avalanches.

This figure approximates the correct range for real biological systems. Every network must have at least one state cycle; it may have more.

Some can recover from any single perturbation, others from only a amd, whereas still others are destabilized by any perturbation. The stability of an attractor is proportional to its basin size, which is the number of states on trajectories that drain into the attractor. The more compressed the code, the less capacity znd has to evolve. The coordinated behavior of this system underlies cellular differentiation. Their structure degenerates into isolated feedback loops that do not interact.

Therefore, there are 2 to the 2K power possible Boolean switching rules for that element.

Antichaos and Adaptation – Stuart Alan Kauffman – Google Books

How much order and chaos do the genomic systems of viruses, bacteria, plants and animals exhibit? Initial conditions that wdaptation very much alike may have markedly different outcomes. Every complex system has what can be called local features: The activity of any one gene is directly regulated by fairly few other genes or gene products, and certain rules govern adaptatkon interactions.


Big attractors are stable to many perturbations, and small ones are generally unstable.

Antichaos and adaptation. – Abstract – Europe PMC

Yet certain properties of complex systems are becoming clear. These properties are observed in organisms. Because all the elements act simultaneously, the system is also said to be synchronous. The stability of attractors subjected to minimal perturbations can differ. One interpretation of the meaning of antichaos in complex systems has particular relevance to biology: Highly ordered networks are too frozen to coordinate complex behavior. In contrast, if the level anrichaos bias is well below the critical value-as it is in chaotically active systems-then a web of oscillating elements spreads across the system, leaving only small islands of frozen elements.

Highly chaotic networks would be so disordered that control of complex behaviors would be hard to maintain. Alternatively, the AND function declares that a variable will become active only if all its inputs are currently active.

Networks on the boundary between order and chaos may have the flexibility to adapt rapidly and successfully through the accumulation of useful variations. Networks with only a single input per element constitute a adsptation ordered class.

The ad should not be interpreted too literally, of course: The dynamic behavior of the network becomes a web of frozen elements and functionally isolated islands of changeable elements. In the ordered regime of networks with two or fewer inputs per element, there is little sensitivity to initial conditions: The genomic regulatory network orchestrates those possibilities into changing patterns of gene activity over time.

One phenomenon found in some cases has already caught the popular imagination: Indeed, the capacity to evolve and adapt may itself be an achievement of evolution.

Antichaos and adaptation.

Another prediction refers to the stability of cell types. Addaptation order would emerge spontaneously: To understand how self-organization can be a force in evolution, a brief overview of complex systems is necessary. A system with elements and states, for example, would have only about 74 different patterns of behavior. In the chaotic regime, networks diverge after beginning in very similar states, but in the ordered regime, similar states tend to converge on the same successor states fairly soon.


Because its behavior is determined precisely, the system proceeds to the same successor state as it did before.

If the hypotheses continue to hold up, biologists may have the beginnings of a comprehensive theory of genomic organization, behavior and capacity to evolve. Even if each state transition took only one microsecond, it would take aand of times longer than the age of the universe for the network to traverse its attractor completely.

Antichaos and Adaptation

Antichaos and Adaptation Biological evolution may have been shaped by more than just natural selection. A system passes from one unique state to another. Cell types differ because they have dissimilar patterns of genetic activity, not because they have different genes.

That order, of course, is much the same as I have described for networks with low connectivity. Thus, bacteria have one or two cell types, sponges have perhaps from 12 to 15 and annelid worms have about Taken as models of genomic systems, systems poised between order and chaos come close to fitting many features of cellular differentiation during ontogeny-features common to organisms that have been diverging evolutionarily for more than million years.

These systems are named after George Boole, the English inventor of an algebraic approach to mathematical logic.

Usually antixhaos gene is directly regulated by few other genes of molecules-perhaps no more than The system as a whole becomes orderly because changes in its behavior must remain small and local. After receiving an appropriate stimulus, a gene in a eukaryotic cell needs about one to 10 minutes to become active.

For each combination, either an active or inactive result must be specified.