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33
Intervention in contextsensitive probabilistic Boolean networks
, 2005
"... Motivation: Intervention in a gene regulatory network is used to help it 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 essentially a finite collection of Boolean n ..."
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Cited by 26 (9 self)
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Motivation: Intervention in a gene regulatory network is used to help it 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 essentially a finite collection of Boolean networks in which at any discrete time point the gene state vector transitions according to the rules of one of the constituent networks. For an instantaneously random PBN, the governing Boolean network is randomly chosen at each time point. For a contextsensitive PBN, the governing Boolean network remains fixed for an interval of time until a binary random variable determines a switch. The theory of automatic control has been previously applied to find optimal strategies for manipulating external (control) variables that affect the transition probabilities of an instantaneously random PBN to desirably affect its dynamic evolution over a finite time horizon. This paper extends the methods of external control to contextsensitive PBNs.
Steadystate analysis of genetic regulatory networks modelled by probabilistic Boolean networks
, 2003
"... Probabilistic Boolean networks (PBNs) have recently been introduced as a promising class of models of genetic regulatory networks. The dynamic behaviour of PBNs can be analysed in the context of Markov chains. A key goal is the determination of the steadystate (longrun) behaviour of a PBN by analy ..."
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Cited by 19 (1 self)
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Probabilistic Boolean networks (PBNs) have recently been introduced as a promising class of models of genetic regulatory networks. The dynamic behaviour of PBNs can be analysed in the context of Markov chains. A key goal is the determination of the steadystate (longrun) behaviour of a PBN by analysing the corresponding Markov chain. This allows one to compute the longterm influence of a gene on another gene or determine the longterm joint probabilistic behaviour of a few selected genes. Because matrixbased methods quickly become prohibitive for large sizes of networks, we propose the use of Monte Carlo methods. However, the rate of convergence to the stationary distribution becomes a central issue. We discuss several approaches for determining the number of iterations necessary to achieve convergence of the Markov chain corresponding to a PBN. Using a recently introduced method based on the theory of twostate Markov chains, we illustrate the approach on a subnetwork designed from human glioma gene expression data and determine the joint steadystate probabilities for several groups of genes. Copyright # 2003 John Wiley & Sons, Ltd.
Optimal infinitehorizon control for probabilistic Boolean networks
 IEEE Transactions on Signal Processing
"... Abstract—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 finitehorizon control, i.e., control over a small number of stages. This paper considers the design of opt ..."
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Cited by 19 (9 self)
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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 disease. Heretofore, intervention has focused on finitehorizon control, i.e., control over a small number of stages. This paper considers the design of optimal infinitehorizon control for contextsensitive probabilistic Boolean networks (PBNs). It can also be applied to instantaneously random PBNs. The stationary policy obtained is independent of time and dependent on the current state. This paper concentrates on discounted problems with bounded cost per stage and on averagecostperstage problems. These formulations are used to generate stationary policies for a PBN constructed from melanoma geneexpression data. The results show that the stationary policies obtained by the two different formulations are capable of shifting the probability mass of the stationary distribution from undesirable states to desirable ones. Index Terms—Altering steady state, genetic network intervention, infinitehorizon control, optimal control of probabilistic Boolean networks. I.
Mappings between Probabilistic Boolean Networks
, 2003
"... Probabilistic Boolean Networks (PBNs) comprise a graphical model based on uncertain rulebased dependencies between nodes and have been proposed as a model for genetic regulatory networks. As with any algebraic strucicf theckxxzkfjx#[xk of important mappings between PBNs isckT#G for both theory ..."
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Cited by 16 (8 self)
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Probabilistic Boolean Networks (PBNs) comprise a graphical model based on uncertain rulebased dependencies between nodes and have been proposed as a model for genetic regulatory networks. As with any algebraic strucicf theckxxzkfjx#[xk of important mappings between PBNs isckT#G for both theory andapplickfjj This paper treats the ckxjH[[kfjj of mappings to alter PBNstruc#V while at the same time maintaining cintaining with the original probability strucilit It ctkx[[jH projecHkfj onto subnetworks, adjuncwork of new nodes, resolution reducuti mappings formed by merging nodes, and morphological mappings on the graph structure of the PBN. It places PBNs in the framework of manysorted algebras and in that context defines homomorphisms between PBNs.
Genomic Signal Processing: Diagnosis and Therapy
, 2005
"... this article, we give an overview of GSP and describe how pattern recognition and network analysis are central to diagnosis and therapy for genetic diseases ..."
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Cited by 11 (4 self)
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this article, we give an overview of GSP and describe how pattern recognition and network analysis are central to diagnosis and therapy for genetic diseases
Symbolic approaches to finding control strategies in boolean networks
 Proceedings of The Sixth AsiaPacific Bioinformatics Conference, (APBC
, 2008
"... We present an exact algorithm, based on techniques from the field of Model Checking, for finding control policies for Boolean networks (BN) with control nodes. Given a BN, a set of starting states, I, a set of goal states, F, and a target time, t, our algorithm automatically finds a sequence of cont ..."
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Cited by 7 (3 self)
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We present an exact algorithm, based on techniques from the field of Model Checking, for finding control policies for Boolean networks (BN) with control nodes. Given a BN, a set of starting states, I, a set of goal states, F, and a target time, t, our algorithm automatically finds a sequence of control signals that deterministically drives the BN from I to F at, or before time t, or else guarantees that no such policy exists. Despite recent hardnessresults for finding control policies for BNs, we show that, in practice, our algorithm runs in seconds to minutes on over 13,400 BNs of varying sizes and topologies, including a BN model of embryogenesis in D. melanogaster with 15,360 Boolean variables. We then extend our method to automatically identify a set of Boolean transfer functions that reproduce the qualitative behavior of gene regulatory networks. Specifically, we automatically (re)learn a BN model of D. melanogaster embryogenesis in 5.3 seconds, from a Computational cellular and systems modeling is playing an increasingly important role in biology, bioengineering, and medicine. The promise of computer modeling is that it becomes a conduit through which reductionist data can be translated into scientific discoveries, clinical practice, and the design of new technologies. The reality of modeling is that there are still a number of unmet
Optimal FiniteHorizon Control for Probabilistic Boolean Networks with Hard Constraints
 The International Symposium on Optimization and Systems Biology (OSB 2007), Lecture Notes in Operations Research
, 2007
"... Abstract In this paper, we study optimal control policies for Probabilistic Boolean Networks (PBNs) with hard constraints. Boolean Networks (BNs) and PBNs are useful and effective tools for modelling genetic regulatory networks. A PBN is essentially a collection of BNs driven by a Markov chain proce ..."
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Cited by 7 (5 self)
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Abstract In this paper, we study optimal control policies for Probabilistic Boolean Networks (PBNs) with hard constraints. Boolean Networks (BNs) and PBNs are useful and effective tools for modelling genetic regulatory networks. A PBN is essentially a collection of BNs driven by a Markov chain process. It is wellknown that the control/intervention of a genetic regulatory network is useful for avoiding undesirable states associated with diseases like cancer. Therefore both optimal finitehorizon control and infinitehorizon control policies have been proposed to achieve the purpose. Actually the optimal control problem can be formulated as a probabilistic dynamic programming problem. In many studies, the optimal control problems did not consider the case of hard constraints, i.e., to include a maximum upper bound for the number of controls that can be applied to the PBN. The main objective of this paper is to introduce a new formulation for the optimal finitehorizon control problem with hard constraints. Experimental results are given to demonstrate the efficiency of our proposed formulation.
Bayesian robustness in the control of gene regulatory networks
 Signal Processing, IEEE Transactions on 2009
"... Abstract—The errors originating in the data extraction process, gene selection and network inference prevent the transition probabilities of a gene regulatory network from being accurately estimated. Thus, it is important to study the effect of modeling errors on the final outcome of an intervention ..."
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Cited by 7 (6 self)
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Abstract—The errors originating in the data extraction process, gene selection and network inference prevent the transition probabilities of a gene regulatory network from being accurately estimated. Thus, it is important to study the effect of modeling errors on the final outcome of an intervention strategy and to design robust intervention strategies. Two major approaches applied to the design of robust policies in general are the minimax (worst case) approach and the Bayesian approach. The minimax control approach is typically conservative because it gives too much importance to the scenarios which hardly occur in practice. Consequently, in this paper, we formulate the Bayesian approach for the control of gene regulatory networks. We characterize the errors emanating from the data extraction and inference processes and compare the performance of the minimax and Bayesian designs based on these uncertainties. Index Terms—Bayesian robustness, gene regulatory networks, intervention, parameter estimation, robust control. I.
Inference of noisy nonlinear differential equation models for gene regulatory networks using genetic programming and kalman filtering
 IEEE Transactions on Signal Processing
, 2008
"... Abstract—A key issue in genomic signal processing is the inference of gene regulatory networks. These are used both to understand the role of biological regulation in phenotypic determination and to derive therapeutic strategies for geneticbased diseases. In this paper, gene regulatory networks are ..."
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Cited by 4 (0 self)
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Abstract—A key issue in genomic signal processing is the inference of gene regulatory networks. These are used both to understand the role of biological regulation in phenotypic determination and to derive therapeutic strategies for geneticbased diseases. In this paper, gene regulatory networks are inferred via evolutionary modeling based on timeseries microarray measurements. A nonlinear differential equation model is adopted. It includes random noise parameters for intrinsic noise arising from stochasticity in transcription and translation and for external noise arising from factors such as the amount of RNA polymerase, levels of regulatory proteins, and the effects of mRNA and protein degradation. An iterative algorithm is proposed for model identification. Genetic programming is applied to identify the structure of the model and Kalman filtering is used to estimate the parameters in each iteration. Both standard and robust Kalman filtering are considered. The effectiveness of the proposed scheme is demonstrated by using synthetic data and by using microarray measurements pertaining to yeast protein synthesis. Index Terms—Gene regulatory network, genetic programming, Kalman filter. I.
Optimal Constrained Stationary Intervention in Gene Regulatory Networks
, 2008
"... A key objective of gene network modeling is to develop intervention strategies to alter regulatory dynamics in such a way as to reduce the likelihood of undesirable phenotypes. Optimal stationary intervention policies have been developed for gene regulation in the framework of probabilistic Boolean ..."
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Cited by 4 (3 self)
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A key objective of gene network modeling is to develop intervention strategies to alter regulatory dynamics in such a way as to reduce the likelihood of undesirable phenotypes. Optimal stationary intervention policies have been developed for gene regulation in the framework of probabilistic Boolean networks in a number of settings. To mitigate the possibility of detrimental side effects, for instance, in the treatment of cancer, it may be desirable to limit the expected number of treatments beneath some bound. This paper formulates a general constraint approach for optimal therapeutic intervention by suitably adapting the reward function and then applies this formulation to bound the expected number of treatments. A mutated mammalian cell cycle is considered as a case study.