Shin Jeewoen, MacCarthy Thomas
Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, United States of America.
Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, United States of America; Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, United States of America.
PLoS Comput Biol. 2015 Oct 9;11(10):e1004432. doi: 10.1371/journal.pcbi.1004432. eCollection 2015 Oct.
Robustness, defined as tolerance to perturbations such as mutations and environmental fluctuations, is pervasive in biological systems. However, robustness often coexists with its counterpart, evolvability--the ability of perturbations to generate new phenotypes. Previous models of gene regulatory network evolution have shown that robustness evolves under stabilizing selection, but it is unclear how robustness and evolvability will emerge in common coevolutionary scenarios. We consider a two-species model of coevolution involving one host and one parasite population. By using two interacting species, key model parameters that determine the fitness landscapes become emergent properties of the model, avoiding the need to impose these parameters externally. In our study, parasites are modeled on species such as cuckoos where mimicry of the host phenotype confers high fitness to the parasite but lower fitness to the host. Here, frequent phenotype changes are favored as each population continually adapts to the other population. Sensitivity evolves at the network level such that point mutations can induce large phenotype changes. Crucially, the sensitive points of the network are broadly distributed throughout the network and continually relocate. Each time sensitive points in the network are mutated, new ones appear to take their place. We have therefore named this phenomenon "whack-a-mole" sensitivity, after a popular fun park game. We predict that this type of sensitivity will evolve under conditions of strong directional selection, an observation that helps interpret existing experimental evidence, for example, during the emergence of bacterial antibiotic resistance.
稳健性,定义为对诸如突变和环境波动等扰动的耐受性,在生物系统中普遍存在。然而,稳健性通常与其对立面——可进化性(即扰动产生新表型的能力)共存。先前的基因调控网络进化模型表明,稳健性在稳定选择下进化,但尚不清楚在常见的共同进化情景中稳健性和可进化性将如何出现。我们考虑一个涉及一个宿主种群和一个寄生物种种群的共同进化的双物种模型。通过使用两个相互作用的物种,决定适应度景观的关键模型参数成为模型的涌现属性,避免了从外部强加这些参数的需要。在我们的研究中,寄生物种以杜鹃等物种为模型,在这些物种中,对宿主表型的模仿赋予寄生物高适应度,但赋予宿主低适应度。在这里,由于每个种群不断适应另一个种群,频繁的表型变化受到青睐。敏感性在网络层面进化,使得点突变能够诱导大的表型变化。至关重要的是,网络的敏感点广泛分布在整个网络中并不断重新定位。每次网络中的敏感点发生突变时,新的敏感点就会出现取而代之。因此,我们根据一款流行的游乐场游戏将这种现象命名为“打地鼠”敏感性。我们预测,这种类型的敏感性将在强定向选择条件下进化,这一观察结果有助于解释现有的实验证据,例如在细菌抗生素抗性出现期间。