A probabilistic methodology for integrating knowledge and experiments on biological networks

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
Irit Gat-ViksRon Shamir

Abstract

Biological systems are traditionally studied by focusing on a specific subsystem, building an intuitive model for it, and refining the model using results from carefully designed experiments. Modern experimental techniques provide massive data on the global behavior of biological systems, and systematically using these large datasets for refining existing knowledge is a major challenge. Here we introduce an extended computational framework that combines formalization of existing qualitative models, probabilistic modeling, and integration of high-throughput experimental data. Using our methods, it is possible to interpret genomewide measurements in the context of prior knowledge on the system, to assign statistical meaning to the accuracy of such knowledge, and to learn refined models with improved fit to the experiments. Our model is represented as a probabilistic factor graph, and the framework accommodates partial measurements of diverse biological elements. We study the performance of several probabilistic inference algorithms and show that hidden model variables can be reliably inferred even in the presence of feedback loops and complex logic. We show how to refine prior knowledge on combinatorial regulatory relations using...Continue Reading

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Citations

Jan 11, 2011·Journal of Computational Biology : a Journal of Computational Molecular Cell Biology·Alex GraudenziStuart A Kauffman
Jan 5, 2013·Journal of Computational Biology : a Journal of Computational Molecular Cell Biology·Roded Sharan, Richard M Karp
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