Biological context networks: a mosaic view of the interactome

Abstract

Network models are a fundamental tool for the visualization and analysis of molecular interactions occurring in biological systems. While broadly illuminating the molecular machinery of the cell, graphical representations of protein interaction networks mask complex patterns of interaction that depend on temporal, spatial, or condition‐specific contexts. In this paper, we introduce a novel graph construct called a biological context network that explicitly captures these changing patterns of interaction from one biological context to another. We consider known gene ontology biological process and cellular component annotations as a proxy for context, and show that aggregating small process‐specific protein interaction sub‐networks leads to the emergence of observed scale‐free properties. The biological context model also provides the basis for characterizing proteins in terms of several context‐specific measures, including ‘interactive promiscuity,’ which identifies proteins whose interacting partners vary from one context to another. We show that such context‐sensitive measures are significantly better predictors of knockout lethality than node degree, reaching better than 70% accuracy among the top scoring proteins. ### Synopsis The machinery of the cell involves a complex network of biochemical interactions. The full complement of known interactions (the so‐called interactome ) is not a static construct. Rather, specific interactions are activated or deactivated as part of cell‐specific processes involving metabolism, cell cycle regulation, signal transduction, and hundreds of other characteristic processes. In this paper, we present a parsimonious network model (a ‘biological context network’) in which the nodes of the network, representing proteins, are deployed in one of several biological contexts, affecting whether or not connecting edges are active. The specification or the biological program provided by the model only articulates which biological contexts are associated with each protein and whether two proteins, each active in a particular context, interact. We study two types of context specification, GO biological processes or GO protein localization, but the formalism naturally extends to modeling contexts in the form of environmental stimuli, pathological conditions, tissues, or even organisms. The model elucidates interactions that are either conserved or variable from one context to another. Graphs and their variants are the foundation for modeling complex biological systems. Graph topology reveals the basic properties of connectivity, robustness, modularity, hierarchical structure, and other properties, enabling identification of protein complexes or functional modules ([Segal et al , 2003][1]; [Spirin and Mirny, 2003][2]), and serves to aid whole‐genome functional annotation efforts ([Marcotte et al , 1999][3]; [Letovsky and Kasif, 2003][4]). Biological networks are also of commercial interest as an aid to drug target discovery ([Gardner et al , 2003][5]) or for predicting toxic side effects, and are at the heart of pharmaceutical initiatives focused on integrating and mining pathway data sets ([Hood et al , 2004][6]). Protein interaction networks are often obtained by high‐throughput detection assays ([Rual et al , 2005][7]) or inferred from literature surveys ([Mishra et al , 2006][8]). As a result, they represent a high‐level integrated summary of a large number of interactions inferred from many biological contexts. However, representing the interactome as a static biological network is akin to a long‐exposure photograph that can mask context‐specific patterns of activation across multiple processes, cellular locations, and time. Conclusions drawn from the full network's topology may be compromised by these inherent limitations. A central goal of systems biology research is to elucidate the underlying patterns of interaction in an effort to obtain more realistic and predictive models of the cell ([Ideker et al , 2001][9]). This has prompted the development of a broad range of graphical representations coupled with mathematical equations intended to model cellular dynamics. By contrast, protein–protein interaction networks are typically represented as a standard undirected graph where vertices correspond to individual proteins and edges connect pairs of interacting proteins. Biological context networks provide an intermediate‐level formalism, in which we label proteins with contextual information about the protein and activate protein interactions as specified by the succinct biological program associated with the network. In its simplest form, the program activates an edge whenever two interacting proteins are in a shared contextual state, and otherwise assumes that the interaction has been inactivated. The biological context network model enables one to view the interactome as a mosaic of overlapping sub‐networks each associated with specific contexts or conditions and to further characterize changes in topology from one context to another. For example, in [Figure 1][10], we show the context‐specific sub‐networks in the local neighborhood of the protein Sec13, highlighting its association with both the nuclear pore complex and the endoplasmic reticulum ([Enninga et al , 2003][11]). It has been widely observed that a broad range of social, technological, and biological networks are scale‐free, characterized by a power‐law degree distribution where a few ‘hub’ proteins have many interacting partners, whereas most proteins have very few ([Barabasi and Oltvai, 2004][12]). Furthermore, high‐degree ‘hubs’ in protein–protein interaction networks are more likely to be essential for the viability of the organism. In this paper, we provide some evidence that a power‐law distribution,...