Instituto de Biotecnología Universidad Nacional Autónoma de México, Cuernavaca, Morelos, México.
PLoS Comput Biol. 2012;8(9):e1002669. doi: 10.1371/journal.pcbi.1002669. Epub 2012 Sep 6.
Accumulating experimental evidence suggests that the gene regulatory networks of living organisms operate in the critical phase, namely, at the transition between ordered and chaotic dynamics. Such critical dynamics of the network permits the coexistence of robustness and flexibility which are necessary to ensure homeostatic stability (of a given phenotype) while allowing for switching between multiple phenotypes (network states) as occurs in development and in response to environmental change. However, the mechanisms through which genetic networks evolve such critical behavior have remained elusive. Here we present an evolutionary model in which criticality naturally emerges from the need to balance between the two essential components of evolvability: phenotype conservation and phenotype innovation under mutations. We simulated the Darwinian evolution of random Boolean networks that mutate gene regulatory interactions and grow by gene duplication. The mutating networks were subjected to selection for networks that both (i) preserve all the already acquired phenotypes (dynamical attractor states) and (ii) generate new ones. Our results show that this interplay between extending the phenotypic landscape (innovation) while conserving the existing phenotypes (conservation) suffices to cause the evolution of all the networks in a population towards criticality. Furthermore, the networks produced by this evolutionary process exhibit structures with hubs (global regulators) similar to the observed topology of real gene regulatory networks. Thus, dynamical criticality and certain elementary topological properties of gene regulatory networks can emerge as a byproduct of the evolvability of the phenotypic landscape.
越来越多的实验证据表明,生物的基因调控网络在临界状态下运作,即在有序和混沌动力学之间的转变。这种网络的临界动力学允许稳健性和灵活性共存,这对于确保内稳态稳定(给定表型)是必要的,同时允许在多个表型(网络状态)之间切换,这在发育和应对环境变化中都会发生。然而,遗传网络进化到这种临界行为的机制仍然难以捉摸。在这里,我们提出了一个进化模型,其中临界性自然地从平衡进化的两个必要组成部分中出现:突变下的表型保守性和表型创新性。我们模拟了随机布尔网络的达尔文进化,这些网络的基因调控相互作用发生突变,并通过基因复制而增长。突变网络受到选择,以选择既能保留所有已经获得的表型(动态吸引子状态)又能产生新表型的网络。我们的结果表明,这种扩展表型景观(创新)与保留现有表型(保守)之间的相互作用足以导致群体中所有网络向临界状态进化。此外,这种进化过程产生的网络具有与真实基因调控网络观察到的拓扑结构相似的枢纽(全局调节剂)。因此,动态临界性和基因调控网络的某些基本拓扑性质可以作为表型景观的可进化性的副产品而出现。