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The effect of scale-free topology on the robustness and evolvability of genetic regulatory networks.

机译:无标度拓扑对遗传调控网络的鲁棒性和可进化性的影响。

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摘要

We investigate how scale-free (SF) and Erdos-Renyi (ER) topologies affect the interplay between evolvability and robustness of model gene regulatory networks with Boolean threshold dynamics. In agreement with Oikonomou and Cluzel (2006) we find that networks with SF(in) topologies, that is SF topology for incoming nodes and ER topology for outgoing nodes, are significantly more evolvable towards specific oscillatory targets than networks with ER topology for both incoming and outgoing nodes. Similar results are found for networks with SF(both) and SF(out) topologies. The functionality of the SF(out) topology, which most closely resembles the structure of biological gene networks (Babu et al. 2004), is compared to the ER topology in further detail through an extension to multiple target outputs, with either an oscillatory or a non-oscillatory nature. For multiple oscillatory targets of the same length, the differences between SF(out) and ER networks are enhanced, but for non-oscillatory targets both types of networks show fairly similar evolvability. We find that SF networks generate oscillations much more easily than ER networks do, and this may explain why SF networks are more evolvable than ER networks are for oscillatory phenotypes. In spite of their greater evolvability, we find that networks with SF(out) topologies are also more robust to mutations (mutational robustness) than ER networks. Furthermore, the SF(out) topologies are more robust to changes in initial conditions (environmental robustness). For both topologies, we find that once a population of networks has reached the target state, further neutral evolution can lead to an increase in both the mutational robustness and the environmental robustness to changes in initial conditions.
机译:我们调查无标度(SF)和鄂尔多斯-仁义(ER)拓扑如何影响具有布尔阈值动力学的模型基因调控网络的可进化性和鲁棒性之间的相互作用。与Oikonomou和Cluzel(2006)一致,我们发现具有SF(in)拓扑结构的网络(即传入节点的SF拓扑和传出节点的ER拓扑)比特定于振荡目标的网络要显着地发展为特定的振荡目标和传出节点。对于具有SF(两者)和SF(out)拓扑的网络,发现了类似的结果。 SF(out)拓扑的功能与生物基因网络的结构最相似(Babu等,2004),通过扩展到多个目标输出(具有振荡或振荡)来与ER拓扑进行更详细的比较。非振荡性质。对于相同长度的多个振荡目标,SF(out)和ER网络之间的差异得到了增强,但是对于非振荡目标,两种类型的网络都显示出相当相似的可演化性。我们发现,SF网络比ER网络更容易产生振荡,这可以解释为什么SF网络比ER网络在振荡表型方面更具发展性。尽管它们具有更大的可进化性,但我们发现具有SF(out)拓扑的网络也比ER网络对突变(突变鲁棒性)更强健。此外,SF(out)拓扑对于初始条件的更改更加健壮(环境健壮性)。对于这两种拓扑,我们发现一旦网络数量达到目标状态,进一步的中性演化可能会导致突变健壮性和环境健壮性均增加,以适应初始条件的变化。

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