生物网络建模的综合方法
An integrative approach to modeling biological networks
摘要：
由于蛋白质通过与其他蛋白质相互作用来进行生物过程，因此分析蛋白质相互作用（PPI）网络的结构可以解释复杂的生物机制、进化和疾病。同样，研究蛋白质结构网络，残基相互作用图（RIG），可能会提供对蛋白质折叠，稳定性和功能的见解。理解这些网络的第一步是找到一个适当的网络模型，可以紧密复制它们的结构。评估模型与数据的拟合需要将模型与真实世界的网络进行比较。由于网络比较在计算上是不可行的，它们依赖于网络特性。“我们表明，很难评估模型与任何单个网络属性的拟合的可靠性。因此，我们的方法集成了各种网络属性，并进一步将这些与一系列概率方法相结合，以预测生物网络的适当网络模型。我们发现几何随机图，模型对象之间的空间关系，是最适合的模型RIG。这验证了我们的方法的正确性，因为RIG以前已被证明是几何的。我们将我们的方法应用于有噪声的PPI网络，并证明其结构也与几何随机图一致。
英文摘要：
Since proteins carry out biological processes by interacting with other proteins, analyzing the structure of protein-protein interaction (PPI) networks could explain complex biological mechanisms, evolution, and disease. Similarly, studying protein structure networks, residue interaction graphs (RIGs), might provide insights into protein folding, stability, and function. The first step towards understanding these networks is finding an adequate network model that closely replicates their structure. Evaluating the fit of a model to the data requires comparing the model with real-world networks. Since network comparisons are computationally infeasible, they rely on heuristics, or "network properties." We show that it is difficult to assess the reliability of the fit of a model with any individual network property. Thus, our approach integrates a variety of network properties and further combines these with a series of probabilistic methods to predict an appropriate network model for biological networks. We find geometric random graphs, that model spatial relationships between objects, to be the best-fitting model for RIGs. This validates the correctness of our method, since RIGs have previously been shown to be geometric. We apply our approach to noisy PPI networks and demonstrate that their structure is also consistent with geometric random graphs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
