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The design of effective therapeutic interventions can be formulated as Markov decision processes in the framework of context-sensitive probabilistic Boolean networks, assuming that the states are measurable. The full state of a context-sensitive probabilistic Boolean network is specified by an ordered pair composed of a network context and a gene-activity profile. In practice, however, the network...
External control of a gene regulatory network is used for the purpose of avoiding undesirable states, such as those associated with disease. Certain types of cancer therapies are given in cycles: each treatment is followed by a recovery phase. In a recovery phase, the side effects tend to gradually degrade. Here, an intervention strategy that simulates cyclic therapies is proposed. It is shown how...
Cancer therapy options are directed at eradicating cancerous cells. Unfortunately, cancer treatments may also damage healthy cells. The result of this damage is side effects of the treatment. It is desirable to maintain the side effects below an acceptable threshold. This goal can be facilitated by enforcing an upper bound on the expected number of treatments a patient may receive during her/his therapy...
Probabilistic Boolean networks are a class of rule-based models for gene regulatory networks. This class of models is used to design optimal therapeutic intervention strategies. While synchronous probabilistic Boolean networks have been investigated in detail in the literature, no similar endeavor has been completed for asynchronous networks. This paper addresses this issue by introducing an asynchronous...
Probabilistic Boolean networks are rule-based models for gene regulatory networks. They are used to design intervention strategies in translational genomics such as cancer treatment. Previously, methods for finding control policies with the highest effect on steady-state distributions of probabilistic Boolean networks have been proposed. These methods were derived using the theory of infinite-horizon...
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