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Planning for Gene Regulatory Network Intervention

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2 Author(s)
Daniel Bryce ; Department of Comp. Sci. and Eng., Arizona State University, 699 S. Mill Ave. Ste. 501, Tempe, AZ. ; Seungchan Kim

Modeling the dynamics of cellular processes has recently become a important research area of many disciplines. One of the most important reasons to model a cellular process is to enable high-throughput in-silico experiments that attempt to predict or intervene in the process. These experiments can help accelerate the design of therapies through their cheap replication and alteration. While some techniques exist for reasoning with cellular processes, few take advantage of the flexible and scalable algorithms popularized in AI research. We apply AI planning based search techniques to a well-studied gene regulatory network model and demonstrate its clear advantage over existing methods based on enumeration

Published in:

2006 IEEE/NLM Life Science Systems and Applications Workshop

Date of Conference:

July 2006