Exploring biological network structure with clustered random networks.
Exploring biological network structure with clustered random networks.
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DOI：
10.1186/1471-2105-10-405
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发表时间：
2009-12-09
影响因子：
3
通讯作者：
Meyers LA
中科院分区：
文献类型：
--
作者：
Bansal S;Khandelwal S;Meyers LA
复杂的生物系统通常被建模为相互作用的单元网络。蛋白质之间的生物化学相互作用网络，宿主之间的流行病学联系，生态系统中的营养相互作用，仅举几例，为塑造和穿越这些系统的动力学过程提供了有用的见解。节点的度（相互作用的数量）和聚类的程度（一组三个节点相互连接的趋势）是许多研究得很好的网络属性中的两个，它们可以从根本上塑造一个系统。然而，要解开各种网络属性的相互依赖的影响可能是困难的。简单的网络模型可以帮助我们量化经验网络系统的结构，并了解各种拓扑性质对动力学的影响。在这里，我们开发并实现了一个新的马尔可夫链模拟算法来生成简单的，连接的随机图，有一个指定的度序列和聚类水平，但在所有其他方面是随机的。该算法（ClustRNet：随机网络）的实现提供了根据局部或全局以及相对或绝对聚类度量优化的随机图的生成。我们比较我们的算法与其他类似的方法，并表明我们更成功地产生所需的网络特性。寻找合适的空模型在生物信息学研究中至关重要，但通常很困难，特别是对于生物网络。正如我们所证明的，当研究复杂网络特征的影响时，ClustRNet生成的网络可以作为随机控制，而不仅仅是经验网络中的度和聚类的副产品。ClustRNet生成指定边结构和聚类的图的集合。这些图允许系统地研究连通性和冗余对网络功能和动态的影响。这一过程是揭示经验生物系统结构特性的功能后果和揭示驱动这些系统的机制的关键一步。
Complex biological systems are often modeled as networks of interacting units. Networks of biochemical interactions among proteins, epidemiological contacts among hosts, and trophic interactions in ecosystems, to name a few, have provided useful insights into the dynamical processes that shape and traverse these systems. The degrees of nodes (numbers of interactions) and the extent of clustering (the tendency for a set of three nodes to be interconnected) are two of many well-studied network properties that can fundamentally shape a system. Disentangling the interdependent effects of the various network properties, however, can be difficult. Simple network models can help us quantify the structure of empirical networked systems and understand the impact of various topological properties on dynamics. Here we develop and implement a new Markov chain simulation algorithm to generate simple, connected random graphs that have a specified degree sequence and level of clustering, but are random in all other respects. The implementation of the algorithm (ClustRNet: Clustered Random Networks) provides the generation of random graphs optimized according to a local or global, and relative or absolute measure of clustering. We compare our algorithm to other similar methods and show that ours more successfully produces desired network characteristics. Finding appropriate null models is crucial in bioinformatics research, and is often difficult, particularly for biological networks. As we demonstrate, the networks generated by ClustRNet can serve as random controls when investigating the impacts of complex network features beyond the byproduct of degree and clustering in empirical networks. ClustRNet generates ensembles of graphs of specified edge structure and clustering. These graphs allow for systematic study of the impacts of connectivity and redundancies on network function and dynamics. This process is a key step in unraveling the functional consequences of the structural properties of empirical biological systems and uncovering the mechanisms that drive these systems.
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