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Planning for Gene Regulatory Network Intervention

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2 Author(s)
Bryce, D. ; Dept. of Comput. Sci. & Eng., Arizona State Univ., 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:

Life Science Systems and Applications Workshop, 2006. IEEE/NLM

Date of Conference:

July 2006