Bayesian network inference algorithms for recovering networks on multiple biological scales: molecular, neural, and ecological

V Anne Smith
School of Biology, University of St Andrews

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     Last modified: March 1, 2006

Abstract
Functional network inference, and in particular Bayesian network algorithms for this task, is being applied with growing regularity in computational molecular biology to recover gene regulatory networks from gene expression data. However, the basic task at hand—to predict causal relationships based on repeated concurrent measurements of multiple variables—is not necessarily limited to the molecular realm. Here, I present my research on using Bayesian network inference algorithms to recover networks on several different levels of biological organization: using gene expression data to reveal gene regulatory networks; using neural activity data to reveal neural information flow networks; and using species abundance data to reveal ecological interaction networks. Each biological system presents a unique set of conditions to the network inference task; however, the applicability of the Bayesian network algorithms across all three systems reveals how methodology developed in one complex biological system can be transferred to another, as well as the potential of Bayesian network algorithms for recovering networks in many complex systems—biological or otherwise.