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This paper presents a general theoretical framework for generating Boolean networks whose state transitions realize a set of given biological pathways or minor variations thereof. This ill-posed inverse problem, which is of crucial importance across practically all areas of biology, is solved by using Karnaugh maps which are classical tools for digital system design. It is shown that the incorporation...
Probabilistic Boolean Networks (PBNs) have been recently introduced as a paradigm for modeling genetic regulatory networks. One of the objectives of PBN modeling is to use the network for the design and analysis of intervention strategies aimed at moving the network out of undesirable states, such as those associated with disease, and into desirable ones. To date, a number of intervention strategies...
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...
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...
One of the objectives of genetic regulatory network modeling is to design intervention approaches for affecting the time evolution of the gene activity profile of the network. The intervention strategies proposed in the context of Probabilistic Boolean Networks(PBNs) assume perfect knowledge of the transition probability matrix of the PBN. This assumption cannot be satisfied in practice due to estimation...
Probabilistic Boolean networks (PBNs) have been recently introduced as a paradigm for modeling genetic regulatory networks. One of the objectives of PBN modeling is to use the network for the design and analysis of intervention strategies aimed at moving the network out of undesirable states, such as those associated with disease, and into desirable ones. To date, a number of intervention strategies...
Intervention in a gene regulatory network is used to avoid undesirable states, such as those associated with a disease. Several types of intervention have been studied in the framework of a probabilistic Boolean network (PBN), which is obtained by joining several Boolean networks (BN) using a probabilistic structure. In this work we treat a case in which we lack this governing probability structure...
External control of a genetic regulatory network is used for the purpose of avoiding undesirable states, such as those associated with disease. Heretofore, intervention has focused on finite-horizon control, i.e., control over a small number of stages. This paper considers the design of optimal infinite-horizon control for context-sensitive probabilistic Boolean networks (PBNs). It can also be applied...
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