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Description:
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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 . |