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Facilitated Variation: How Evolution Learns from Past Environments To Generalize to New Environments

Facilitated Variation: How Evolution Learns from Past Environments To Generalize to New Environments,10.1371/journal.pcbi.1000206.t001,PLOS Computatio

Facilitated Variation: How Evolution Learns from Past Environments To Generalize to New Environments   (Citations: 16)
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One of the striking features of evolution is the appearance of novel structures in organisms. Recently, Kirschner and Gerhart have integrated discoveries in evolution, genetics, and developmental biology to form a theory of facilitated variation (FV). The key observation is that organisms are designed such that random genetic changes are channeled in phenotypic directions that are potentially useful. An open question is how FV spontaneously emerges during evolution. Here, we address this by means of computer simulations of two well-studied model systems, logic circuits and RNA secondary structure. We find that evolution of FV is enhanced in environments that change from time to time in a systematic way: the varying environments are made of the same set of subgoals but in different combinations. We find that organisms that evolve under such varying goals not only remember their history but also generalize to future environments, exhibiting high adaptability to novel goals. Rapid adaptation is seen to goals composed of the same subgoals in novel combinations, and to goals where one of the subgoals was never seen in the history of the organism. The mechanisms for such enhanced generation of novelty (generalization) are analyzed, as is the way that organisms store information in their genomes about their past environments. Elements of facilitated variation theory, such as weak regulatory linkage, modularity, and reduced pleiotropy of mutations, evolve spontaneously under these conditions. Thus, environments that change in a systematic, modular fashion seem to promote facilitated variation and allow evolution to generalize to novel conditions.
Journal: PLOS Computational Biology - PLOS COMPUT BIOL , vol. 4, no. 11, 2008
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    • ...Tsuda and Kawata, 2010) and logic circuits...

    Hiroyuki Kuwaharaet al. Bistability in feedback circuits as a byproduct of evolution of evolva...

    • ... Indeed, theoretical studies aimed at investigating the evolution of architecturally modular networks have had to use quite specific fitness targets to obtain modular networks ...

    Kirsten H. Ten Tusscheret al. Evolution of Networks for Body Plan Patterning; Interplay of Modularit...

    • ...For example, ecological population dynamics are controlled by a slow-changing network of evolved interspecies relationships [60, 65], selection of individual genetic traits is altered by a slow-changing network of evolved pleiotropic and epistatic interactions [57, 76], and social behaviors are affected by a slow-changing network of social connections [55, 56, 74].,In evolutionary scenarios, changes in environmental conditions [57], for example, by migration to different spatial localities [83], may preclude the need for such perturbations by, in effect, enabling different attractors to be sampled in parallel rather than in series.,Specifically, Parter et al. [57] investigated how the evolvability of a population changes over time when it is subjected to a fluctuating environment (we achieve the same conditions using repeated perturbation in a static environment[82])...

    Richard A. Watsonet al. Global Adaptation in Networks of Selfish Components: Emergent Associat...

    • ...Examples of the capacity for an evolutionary algorithm to evolve its state-space representations to allow facilitated variation exist in several domains, such as evolving logic circuits (Kashtan & Alon, 2005), ribozyme secondary structures (Parter, Kashtan, & Alon, 2008), and rewrite rules (Toussaint, 2003)...

    Chrisantha Fernandoet al. The Neuronal Replicator Hypothesis

    • ...We will observe that in both cases networks can present some striking features in terms of their topologies and focus on the presence of certain characteristics such as the frequencies of motifs [51, 73] and other macroscopic observables...

    Pietro DeLelliset al. Synchronization and Control of Complex Networks via Contraction, Adapt...

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