Graph-Based Algorithms for Boolean Function Manipulation
IEEE Transactions on Computers
Principles of artificial intelligence
Principles of artificial intelligence
External Control in Markovian Genetic Regulatory Networks
Machine Learning
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Heuristic search value iteration for POMDPs
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Conditioning-Based Modeling of Contextual Genomic Regulation
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Issues in reasoning about interaction networks in cells: necessity of event ordering knowledge
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
The first probabilistic track of the international planning competition
Journal of Artificial Intelligence Research
Planning graph heuristics for belief space search
Journal of Artificial Intelligence Research
Anytime point-based approximations for large POMDPs
Journal of Artificial Intelligence Research
Planning for gene regulatory network intervention
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Planning and acting in partially observable stochastic domains
Artificial Intelligence
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Modeling the dynamics of biological processes has recently become an important research topic in computational biology and systems engineering. One of the most important reasons to model a biological 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 rapid and inexpensive replication and alteration. While some techniques exist for reasoning with biological processes, few take advantage of the flexible and scalable algorithms popular in AI research. In reasoning about interventions in biological processes, where scalability is crucial for feasible application, we apply AI planning-based search techniques and demonstrate their advantage over existing enumerative methods. We also present a novel formulation of intervention planning that relies on models that characterize and attempt to change the phenotype of a system. We study three biological systems: the yeast cell cycle, a model of the human aging process, and the Wnt5a network governing the metastasis of melanoma in humans. The contribution of our investigation is in demonstrating that: (i) prior approaches, based on dynamic programming, cannot scale as well as heuristic search, and (ii) the newly found scalability enables us to plan previously unknown sequences of interventions that reveal novel and biologically significant responses in the systems which are consistent with biological knowledge in the literature.