Two models are proposed to describe interactions among genes, transcription factors, and signaling cascades involved in regulating a cellular sub-system. These models fall within the class of Markovian regulatory networks, and can accommodate for different biological time scales. These regulatory networks are used to study pathological cellular dynamics and discover treatments that beneficially alter those dynamics. The salient translational goal is to design effective therapeutic actions that desirably modify a pathological cellular behavior via external treatments that vary the expressions of targeted genes. The objective of therapeutic actions is to reduce the likelihood of the pathological phenotypes related to a disease. The task of finding effective treatments is formulated as sequential decision making processes that discriminate the gene-expression profiles with high pathological competence versus those with low pathological competence. Thereby, the proposed computational frameworks provide tools that facilitate the discovery of effective drug targets and the design of potent therapeutic actions on them. Each of the proposed system-based therapeutic methods in this dissertation is motivated by practical and analytical considerations. First, it is determined how asynchronous regulatory models can be used as a tool to search for effective therapeutic interventions. Then, a constrained intervention method is introduced to incorporate the side-effects of treatments while searching for a sequence of potent therapeutic actions. Lastly, to bypass the impediment of model inference and to mitigate the numerical challenges of exhaustive search algorithms, a heuristic method is proposed for designing system-based therapies. The presentation of the key ideas in method is facilitated with the help of several case studies.
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