A network characteristic that correlates environmental and genetic robustness.
A network characteristic that correlates environmental and genetic robustness.
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DOI：
10.1371/journal.pcbi.1003474
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发表时间：
2014-02
影响因子：
4.3
通讯作者：
Periwal V
中科院分区：
文献类型：
--
作者：
Shreif Z;Periwal V
由于扰乱生物系统的科学进步和数据采集技术的进步使得对生物功能的大规模定量分析成为可能，生物体对瞬时环境压力和代际遗传变化的稳健性是这些功能的数学模型可辨识性的根本障碍。克服这一障碍的一种方法是减少可能的模型的空间，以考虑这两种类型的稳健性。然而，两人之间的关系仍然存在争议。这项工作揭示了特定功能的网络特征--瞬时响应性，该功能将环境的稳健性和遗传稳健性联系在一起。我们对30个节点之间相互作用的常微分方程组的动态网络进行了广泛的测试，发现随着节点数量的增加，环境的稳健性和遗传稳健性之间存在着一个幂定律，它趋向于线性。使用我们的方法，我们根据环境和遗传稳健性对已知的3节点基序进行了分类。我们通过将其应用于趋化信号网络来演示我们的方法。特别是，我们研究了CHIV蛋白通过磷酸化途径在生化适应中扮演的角色的可信模型，测试了可以提高系统对环境和/或遗传扰动的稳健性的修饰。在研究生命系统如何运作时，扰乱生命系统的方式取得了进展，计算机资源成本的急剧下降，使得收集大量数据成为可能。生物系统建模的目的是分析这些数据，以确定作为观察到的功能基础的分子之间的精确相互作用。由于生物系统的两个特征，这一点变得困难：(1)生物在大范围的环境因素中不会表现出明显的功能丧失。(2)尽管基因序列有随机突变，但它们的功能是从父母传给孩子的，或多或少没有变化。我们发现这两个特征在特定的网络子集中更相关，并展示了如何使用这一观察来找到这两个特征同时出现的网络。在这个较小的网络空间内工作，可能会更容易从数据中找到合适的底层模型。
As scientific advances in perturbing biological systems and technological advances in data acquisition allow the large-scale quantitative analysis of biological function, the robustness of organisms to both transient environmental stresses and inter-generational genetic changes is a fundamental impediment to the identifiability of mathematical models of these functions. An approach to overcoming this impediment is to reduce the space of possible models to take into account both types of robustness. However, the relationship between the two is still controversial. This work uncovers a network characteristic, transient responsiveness, for a specific function that correlates environmental imperturbability and genetic robustness. We test this characteristic extensively for dynamic networks of ordinary differential equations ranging up to 30 interacting nodes and find that there is a power-law relating environmental imperturbability and genetic robustness that tends to linearity as the number of nodes increases. Using our methods, we refine the classification of known 3-node motifs in terms of their environmental and genetic robustness. We demonstrate our approach by applying it to the chemotaxis signaling network. In particular, we investigate plausible models for the role of CheV protein in biochemical adaptation via a phosphorylation pathway, testing modifications that could improve the robustness of the system to environmental and/or genetic perturbation. Advances in the ways that living systems can be perturbed in order to study how they function and sharp reductions in the cost of computer resources have allowed the collection of large amounts of data. The aim of biological system modeling is to analyze this data in order to pin down the precise interactions of molecules that underlie the observed functions. This is made difficult due to two features of biological systems: (1) Living things do not show an appreciable loss of function across large ranges of environmental factors. (2) Their function is inherited from parent to child more or less unchanged in spite of random mutations in genetic sequences. We find that these two features are more correlated in a specific subset of networks and show how to use this observation to find networks in which these two features appear together. Working within this smaller space of networks may make it easier to find suitable underlying models from data.
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