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Formal Analysis of Network Motifs

机译:网络主题的形式分析

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A recurring set of small sub-networks have been identified as the building blocks of biological networks across diverse organisms. These network motifs have been associated with certain dynamical behaviors and define key modules that are important for understanding complex biological programs. Besides studying the properties of motifs in isolation, existing algorithms often evaluate the occurrence frequency of a specific motif in a given biological network compared to that in random networks of similar structure. However, it remains challenging to relate the structure of motifs to the observed and expected behavior of the larger network. Indeed, even the precise structure of these biological networks remains largely unknown. Previously, we developed a formal reasoning approach enabling the synthesis of biological networks capable of reproducing some experimentally observed behavior. Here, we extend this approach to allow reasoning about the requirement for specific network motifs as a way of explaining how these behaviors arise. We illustrate the approach by analyzing the motifs involved in sign-sensitive delay and pulse generation. We demonstrate the scalability and biological relevance of the approach by revealing the requirement for certain motifs in the network governing stem cell pluripotency.
机译:一组反复出现的小型子网络已被确定为跨各种生物的生物网络的组成部分。这些网络主题已与某些动力学行为相关联,并定义了关键模块,这些模块对于理解复杂的生物程序非常重要。除了单独研究基序的特性外,现有算法通常会评估特定基序在给定生物网络中的出现频率,而不是相似结构的随机网络中的发生频率。然而,将图案的结构与更大的网络的观察到的和预期的行为联系起来仍然具有挑战性。确实,这些生物网络的精确结构仍然很大程度上未知。以前,我们开发了一种正式的推理方法,能够合成能够重现某些实验观察到的行为的生物网络。在这里,我们扩展了这种方法,以允许对特定网络图案的需求进行推理,以此来解释这些行为是如何产生的。我们通过分析涉及符号敏感的延迟和脉冲生成的图案来说明该方法。我们通过揭示控制干细胞多能性的网络中某些基序的要求,证明了该方法的可扩展性和生物学意义。

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