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
