Planning for Gene Regulatory Network Intervention
Daniel Bryce, 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. In this domain, where scalability is crucial for feasible application, we apply AI planning based search techniques and demonstrate their advantage over existing enumerative methods.