Improving evolutionary models of protein interaction networks
Improving evolutionary models of protein interaction networks
复制标题
DOI：
10.1093/bioinformatics/btq623
复制
发表时间：
2011-02-01
期刊：
影响因子：
5.8
通讯作者：
Goldberg, Debra S.
中科院分区：
文献类型：
--
作者：
Gibson, Todd A.;Goldberg, Debra S.
动机：生物网络的理论模型是进化推理的宝贵工具。基于基因复制和分化的理论模型提供了生物学上合理的进化机制。经验网络和理论上生成的网络之间的相似性被认为是模型力学在生物进化中所起作用的证据。然而，这些模型参数化的方法可能会导致有关推断有效性的问题。为了产生特定拓扑值而选择参数值会混淆模型可能针对大范围的参数值产生类似拓扑的可能性。或者，一个模型可以产生一个大范围的拓扑，允许（不正确的）参数值从一个有缺陷的模型产生一个有效的拓扑。为了使模拟的进化力学具有生物学的可信度，参数值应该从经验数据中得出。此外，最近的工作表明，基因复制的时间和命运是至关重要的，以正确的推导这些parameters.Results：我们提出了一种方法，用于从经验数据，用于parameterifying复制和发散模型的蛋白质相互作用网络进化的进化速率。我们的方法避免了以前的方法，没有考虑后续重复的影响的缺点。从我们的参数值，我们发现，并发和现有的复制和发散模型是不够的蛋白质相互作用网络的进化建模。我们引入了一个基于蛋白质表面上的遗传相互作用位点的模型增强，并发现它更紧密地反映了经验网络中的高聚类。
Motivation: Theoretical models of biological networks are valuable tools in evolutionary inference. Theoretical models based on gene duplication and divergence provide biologically plausible evolutionary mechanics. Similarities found between empirical networks and their theoretically generated counterpart are considered evidence of the role modeled mechanics play in biological evolution. However, the method by which these models are parameterized can lead to questions about the validity of the inferences. Selecting parameter values in order to produce a particular topological value obfuscates the possibility that the model may produce a similar topology for a large range of parameter values. Alternately, a model may produce a large range of topologies, allowing ( incorrect) parameter values to produce a valid topology from an otherwise flawed model. In order to lend biological credence to the modeled evolutionary mechanics, parameter values should be derived from the empirical data. Furthermore, recent work indicates that the timing and fate of gene duplications are critical to proper derivation of these parameters.Results: We present a methodology for deriving evolutionary rates from empirical data that is used to parameterize duplication and divergence models of protein interaction network evolution. Our method avoids shortcomings of previous methods, which failed to consider the effect of subsequent duplications. From our parameter values, we find that concurrent and existing existing duplication and divergence models are insufficient for modeling protein interaction network evolution. We introduce a model enhancement based on heritable interaction sites on the surface of a protein and find that it more closely reflects the high clustering found in the empirical network.