Motivation: Recent advances in experimental techniques have generated large amounts of protein interaction data, producing networks containing large numbers of cellular proteins. Mathematically sound and robust foundations are needed for extensive, context-specific exploration of networks, integrating knowledge from different specializations and facilitating biological discovery. Results: Extending our earlier work, we present a theoretical construct, based on random walks, for modelling of information channels between selected points in interaction networks. The software implementation, called ITM Probe, can be used as network exploration and hypothesis forming tool. Through examples involving the yeast pheromone response pathway, we illustrate the versatility and stability of ITM Probe.Availability: www.ncbi.nlm.nih.gov/CBBresearch/qmbp/itm_probe
In our previous publication, a framework for information flow in interaction networks based on random walks with damping was formulated with two fundamental modes: emitting and absorbing. While many other network analysis methods based on random walks or equivalent notions have been developed before and after our earlier work, one can show that they can all be mapped to one of the two modes. In addition to these two fundamental modes, a major strength of our earlier formalism was its accommodation of context-specific directed information flow that yielded plausible and meaningful biological interpretation of protein functions and pathways. However, the directed flow from origins to destinations was induced via a potential function that was heuristic. Here, with a theoretically sound approach called the channel mode, we extend our earlier work for directed information flow. This is achieved by our newly constructed nonheuristic potential function that facilitates a purely probabilistic interpretation of the channel mode. For each network node, the channel mode combines the solutions of emitting and absorbing modes in the same context, producing what we call a channel tensor. The entries of the channel tensor at each node can be interpreted as the amount of flow passing through that node from an origin to a destination. Similarly to our earlier model, the channel mode encompasses damping as a free parameter that controls the locality of information flow. Through examples involving the yeast pheromone response pathway, we illustrate the versatility and stability of our new framework.
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Motivation: Recent advances in experimental techniques have generated large amounts of protein interaction data, producing networks containing large numbers of cellular proteins. Mathematically sound and robust foundations are needed for extensive,
In our previous publication, a framework for information flow in interaction networks based on random walks with damping was formulated with two fundamental modes: emitting and absorbing. While many other network analysis methods based on random walks or equivalent notions have been developed before and after our earlier work, one can show that they can all be mapped to one of the two modes. In addition to these two fundamental modes, a major strength of our earlier formalism was its accommodation of
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exploration of networks, integrating knowledge from different specializations and facilitating biological discovery. Results: Extending our earlier work, we present a theoretical construct, based on random walks, for modelling of information channels between selected points in interaction networks. The software implementation, called ITM Probe, can be used as network exploration and hypothesis forming tool.
directed information flow that yielded plausible and meaningful biological interpretation of protein functions and pathways. However, the directed flow from origins to destinations was induced via a potential function that was heuristic. Here, with a theoretically sound approach called the channel mode, we extend our earlier work for directed information flow. This is achieved by our newly constructed nonheuristic potential function that facilitates a purely probabilistic interpretation of the channel mode. For each network node, the channel mode combines the solutions of emitting and absorbing modes in the same context, producing what we call a channel tensor. The entries of the channel tensor at each node can be interpreted as the amount of flow passing through that node from an origin to a destination. Similarly to our earlier model, the channel mode encompasses damping as a free parameter that controls the locality of information flow.
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ITM Probe.Availability: www.ncbi.nlm.nih.gov/CBBresearch/qmbp/itm_probe
our new framework.