EXTINCTIONS IN HETEROGENEOUS ENVIRONMENTS AND THE EVOLUTION OF MODULARITY
EXTINCTIONS IN HETEROGENEOUS ENVIRONMENTS AND THE EVOLUTION OF MODULARITY
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DOI：
10.1111/j.1558-5646.2009.00684.x
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发表时间：
2009-08-01
期刊：
影响因子：
3.3
通讯作者：
Alon, Uri
中科院分区：
文献类型：
--
作者：
Kashtan, Nadav;Parter, Merav;Alon, Uri
局部亚种群的灭绝是自然界中常见的事件。在这里，我们提出这样的灭绝是否会影响生物进化时间尺度内生物网络的设计。我们研究了灭绝事件对生物系统模块化的影响，这是生物学中多个尺度上常见的建筑原则。作为一个模型系统，我们使用向指定的目标进化的网络作为期望的输入-输出关系。我们使用灭绝-再殖民模型，其中元种群在不同地点之间占据和迁移。每个位置显示不同的环境条件（目标），但与其他位置共享相同的子目标集。我们发现，在没有灭绝事件的情况下，进化的计算网络通常对具有非模块化结构的位置具有高度最优性。相反，当当地种群不时灭绝时，我们发现进化的网络在结构上是模块化的。选择模块化电路是因为它能够在灭绝事件后迅速适应自由生态位的条件。这种快速适应主要是通过来自邻近当地人口的移民之间的基因重组模块来实现的。因此，本研究表明，异质环境中的灭绝促进了模块化生物网络结构的进化，使当地种群能够有效地重组其模块以重新定位生态位。
Extinctions of local subpopulations are common events in nature. Here, we ask whether such extinctions can affect the design of biological networks within organisms over evolutionary timescales. We study the impact of extinction events on modularity of biological systems, a common architectural principle found on multiple scales in biology. As a model system, we use networks that evolve toward goals specified as desired input-output relationships. We use an extinction-recolonization model, in which metapopulations occupy and migrate between different localities. Each locality displays a different environmental condition (goal), but shares the same set of subgoals with other localities. We find that in the absence of extinction events, the evolved computational networks are typically highly optimal for their localities with a nonmodular structure. In contrast, when local populations go extinct from time to time, we find that the evolved networks are modular in structure. Modular circuitry is selected because of its ability to adapt rapidly to the conditions of the free niche following an extinction event. This rapid adaptation is mainly achieved through genetic recombination of modules between immigrants from neighboring local populations. This study suggests, therefore, that extinctions in heterogeneous environments promote the evolution of modular biological network structure, allowing local populations to effectively recombine their modules to recolonize niches.