BIOT 182 |
| In this paper, we describe an analysis framework for characterizing the directed interactions between the enzymes of a metabolic network following a physiological perturbation. This framework combines modularity and Bayesian analysis to infer causality relationships within systematically detected metabolic sub-networks. This framework supports the use of prior biochemical knowledge and efficient heuristic search algorithms for structure learning; moreover, it avoids the limitations of models that only consider pair-wise correlations. Applied to metabolic flux data describing the time course of inflammation-mediated liver hypermetabolism, our analysis discriminated between flexible, i.e. physiological state dependent, and conserved pathway structures. A similar result was obtained for adipocytes undergoing differentiation and subsequent lipid loading, where highly conserved and directed interactions were found for enzymes of both lipogenesis and lipolysis. Maximum likelihood estimates of the node parameters predicted dependencies between the enzymes consistent with the known metabolic biochemistry of liver and adipocyte metabolism. |
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Emerging Technologies: Systems Biology
8:00 AM-11:00 AM, Wednesday, August 22, 2007 BCEC -- 108, Oral
Division of Biochemical Technology |