Biological network analysis has become a central component of computational and systems biology. Because such analysis provides a unifying language to describe relations within complex systems, it has played an increasingly important role in understanding physiological function. Significant efforts have focused on analyzing and inferring the topologyandstructureofcellularnetworksandonrelatingthemtocellularfunctionand organization. However, much of this work has taken a static view of cellular

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... tworks, despite the knowledge that biological networks can change with time, context, and conditions.We introduce this session on The Dynamics of Biological Networks to encourage and support the development of computational methods that elucidate the dynamic interactome. Biological networks encompass many types of variation. Temporal variation can be inferred via experimentally determined large-scale (static) cellular networks, along with other highthroughput experimental data sets that provide snapshots of biological systems at different times and conditions. These data are often integrated with static interaction data (e.g. protein-protein, domain-domain, or regulatory interactions), time-or environment-dependent expression data, protein localization data, or other contextual information. Temporal variation also operates at the evolutionary scale and can be inferred via comparison of biological networks across species. An evolutionary perspective on network dynamics can help shed light on molecular function and