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12
Intervention in Gene Regulatory Networks via Greedy
 Control Policies Based on LongRun Behavior,” BMC Systems Biology
"... Abstract—A salient purpose for studying gene regulatory networks is to derive intervention strategies to identify potential drug targets and design genebased therapeutic intervention. Optimal and approximate intervention strategies based on the transition probability matrix of the underlying Markov ..."
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Abstract—A salient purpose for studying gene regulatory networks is to derive intervention strategies to identify potential drug targets and design genebased therapeutic intervention. Optimal and approximate intervention strategies based on the transition probability matrix of the underlying Markov chain have been studied extensively for probabilistic Boolean networks. While the key goal of control is to reduce the steadystate probability mass of undesirable network states, in practice it is important to limit collateral damage and this constraint should be taken into account when designing intervention strategies with network models. In this paper, we propose two new phenotypically constrained stationary control policies by directly investigating the effects on the network longrun behavior. They are derived to reduce the risk of visiting undesirable states in conjunction with constraints on the shift of undesirable steadystate mass so that only limited collateral damage can be introduced. We have studied the performance of the new constrained control policies together with the previous greedy control policies to randomly generated probabilistic Boolean networks. A preliminary example for intervening in a metastatic melanoma network is also given to show their potential application in designing genetic therapeutics to reduce the risk of entering both aberrant phenotypes and other ambiguous states corresponding to complications or collateral damage. Experiments on both random network ensembles and the melanoma network demonstrate that, in general, the new proposed control policies exhibit the desired performance. As shown by intervening in the melanoma network, these control policies can potentially serve as future practical gene therapeutic intervention strategies. Index Terms—Gene regulatory networks, probabilistic Boolean networks, network intervention, Markov chain, stationary control policy, melanoma. Ç
Optimal Intervention Strategies for Cyclic Therapeutic Methods
"... Abstract—External control of a genetic regulatory network is used for the purpose of avoiding undesirable states such as those associated with a disease. Certain types of cancer therapies, such as chemotherapy, are given in cycles with each treatment being followed by a recovery period. During the r ..."
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Abstract—External control of a genetic regulatory network is used for the purpose of avoiding undesirable states such as those associated with a disease. Certain types of cancer therapies, such as chemotherapy, are given in cycles with each treatment being followed by a recovery period. During the recovery period, the side effects tend to gradually subside. In this paper, it is shown how an optimal cyclic intervention strategy can be devised for any Markovian genetic regulatory network. The effectiveness of optimal cyclic therapies is demonstrated through numerical studies for random networks. Furthermore, an optimal cyclic policy is derived to control the behavior of a regulatory model of the mammalian cellcycle network. Index Terms—Cyclic therapy, dynamic programming, genetic regulatory networks, probabilistic Boolean networks (PBNs), stochastic optimal control. I.
Optimal Intervention Strategies for Therapeutic Methods With FixedLength Duration of Drug Effectiveness
"... Abstract—Intervention in gene regulatory networks in the context of Markov decision processes has usually involved finding an optimal onetransition policy, where a decision is made at every transition whether or not to apply treatment. In an effort to model dosing constraint, a cyclic approach to i ..."
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Cited by 5 (3 self)
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Abstract—Intervention in gene regulatory networks in the context of Markov decision processes has usually involved finding an optimal onetransition policy, where a decision is made at every transition whether or not to apply treatment. In an effort to model dosing constraint, a cyclic approach to intervention has previously been proposed in which there is a sequence of treatment windows and treatment is allowed only at the beginning of each window. This protocol ignores two practical aspects of therapy. First, a treatment typically has some duration of action: adrugwillbeeffectiveforsomeperiod, after which there can be a recovery phase. This, too, might involve a cyclic protocol; however, in practice, a physician might monitor a patient at every stage and decide whether to apply treatment, and if treatment is applied, then the patient will be under the influence of the drug for some duration, followed by a recovery period. This results in an acyclic protocol. In this paper we take a unified approach to both cyclic and acyclic control with duration of effectiveness by placing the problem in the general framework of multiperiod decision epochs with infinite horizon discounting cost. The time interval between successive decision epochs can have multiple time units, where given the current state and the action taken, there is a joint probability distribution defined for the next state and the time when the next decision epoch will be called. Optimal control policies are derived, synthetic networks are used to investigate the properties of both cyclic and acyclic interventions with fixedduration of effectiveness, and the methodology is applied to a mutated mammalian cellcycle network. Index Terms—Acyclic intervention, cyclic intervention, drug scheduling, gene regulatory network, genomic signal processing, optimal control. I.
Selection policyinduced reduction mappings for boolean networks
 University of Southern California, Los Angles, in
, 1991
"... Abstract—Developing computational models paves the way to understanding, predicting, and influencing the longterm behavior of genomic regulatory systems. However, several major challenges have to be addressed before such models are successfully applied in practice. Their inherent high complexity re ..."
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Abstract—Developing computational models paves the way to understanding, predicting, and influencing the longterm behavior of genomic regulatory systems. However, several major challenges have to be addressed before such models are successfully applied in practice. Their inherent high complexity requires strategies for complexity reduction. Reducing the complexity of the model by removing genes and interpreting them as latent variables leads to the problem of selecting which states and their corresponding transitions best account for the presence of such latent variables. We use the Boolean network (BN) model to develop the general framework for selection and reduction of the model’s complexity via designating some of the model’s variables as latent ones. We also study the effects of the selection policies on the steadystate distribution and the controllability of the model. Index Terms—Compression, control, gene regulatory networks, selection policy. I.
Recent advances in intervention in markovian regulatory networks
 Curr Genomics
, 2009
"... Abstract: Markovian regulatory networks constitute a class of discrete statespace models used to study gene regulatory dynamics and discover methods that beneficially alter those dynamics. Thereby, this class of models provides a framework to discover effective drug targets and design potent therap ..."
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Abstract: Markovian regulatory networks constitute a class of discrete statespace models used to study gene regulatory dynamics and discover methods that beneficially alter those dynamics. Thereby, this class of models provides a framework to discover effective drug targets and design potent therapeutic strategies. The salient translational goal is to design therapeutic strategies that desirably modify network dynamics via external signals that vary the expressions of a control gene. The objective of an intervention strategy is to reduce the likelihood of the pathological cellular function related to a disease. The task of finding an effective intervention strategy can be formulated as a sequential decision making problem for a predefined cost of intervention and a costperstage function that discriminates the geneactivity profiles. An effective intervention strategy prescribes the actions associated with an external signal that result in the minimum expected cost. This strategy in turn can be used as a treatment that reduces the longrun likelihood of gene expressions favorable to the disease. In this tutorial, we briefly summarize the first method proposed to design such therapeutic interventions, and then move on to some of the recent refinements that have been proposed. Each of these recent intervention methods is motivated by practical or analytical considerations. The presentation of the key ideas is facilitated with the help of two case studies.
Translational Science: Epistemology and the Investigative Process
"... Abstract: The term “translational science ” has recently become very popular with its usage appearing to be almost exclusively related to medicine, in particular, the “translation ” of biological knowledge into medical practice. Taking the perspective that translational science is somehow different ..."
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Abstract: The term “translational science ” has recently become very popular with its usage appearing to be almost exclusively related to medicine, in particular, the “translation ” of biological knowledge into medical practice. Taking the perspective that translational science is somehow different than science and that sound science is grounded in an epistemology developed over millennia, it seems imperative that the meaning of translational science be carefully examined, especially how the scientific epistemology manifests itself in translational science. This paper examines epistemological issues relating mainly to modeling in translational science, with a focus on optimal operator synthesis. It goes on to discuss the implications of epistemology on the nature of collaborations conducive to the translational investigative process. The philosophical concepts are illustrated by considering intervention in gene regulatory networks.
State reduction for network intervention in probabilistic Boolean networks
 Bioinformatics
, 2010
"... Motivation: A key goal of studying biological systems is to design therapeutic intervention strategies. Probabilistic Boolean networks (PBNs) constitute a mathematical model which enables modeling, predicting and intervening in their longrun behavior using Markov chain theory. The longrun dynamics ..."
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Motivation: A key goal of studying biological systems is to design therapeutic intervention strategies. Probabilistic Boolean networks (PBNs) constitute a mathematical model which enables modeling, predicting and intervening in their longrun behavior using Markov chain theory. The longrun dynamics of a PBN, as represented by its steadystate distribution (SSD), can guide the design of effective intervention strategies for the modeled systems. A major obstacle for its application is the large state space of the underlying Markov chain, which poses a serious computational challenge. Hence, it is critical to reduce the model complexity of PBNs for practical applications. Results: We propose a strategy to reduce the state space of the underlying Markov chain of a PBN based on a criterion that the reduction least distorts the proportional change of stationary masses for critical states, for instance, the network attractors. In comparison
Cancer Informatics
"... This is an open access article. Unrestricted noncommercial use is permitted provided the original work is properly cited. Open Access Full open access to this and thousands of other papers at ..."
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This is an open access article. Unrestricted noncommercial use is permitted provided the original work is properly cited. Open Access Full open access to this and thousands of other papers at