Inference of a probabilistic Boolean network from a single observed temporal sequence
EURASIP Journal on Bioinformatics and Systems Biology
Algorithms for Inference, Analysis and Control of Boolean Networks
AB '08 Proceedings of the 3rd international conference on Algebraic Biology
Controllability and observability of Boolean control networks
Automatica (Journal of IFAC)
Intervention in context-sensitive probabilistic Boolean networks revisited
EURASIP Journal on Bioinformatics and Systems Biology - Special issue on applications of signal procesing techniques to bioinformatics, genomics, and proteomics
Bayesian robustness in the control of gene regulatory networks
IEEE Transactions on Signal Processing
Scalable approach for effective control of gene regulatory networks
Artificial Intelligence in Medicine
IEEE Transactions on Signal Processing
Generating probabilistic Boolean networks from a prescribed stationary distribution
Information Sciences: an International Journal
Stationary and structural control in gene regulatory networks: basic concepts
International Journal of Systems Science - Dynamics Analysis of Gene Regulatory Networks
Planning interventions in biological networks
ACM Transactions on Intelligent Systems and Technology (TIST)
The Benefit of Decomposing POMDP for Control of Gene Regulatory Networks
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Transient Dynamics of Reduced-Order Models of Genetic Regulatory Networks
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Growing Seed Genes from Time Series Data and Thresholded Boolean Networks with Perturbation
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Hi-index | 3.85 |
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 context-sensitive 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 context-sensitive PBNs. Results: This paper treats intervention via external control variables in context-sensitive PBNs by extending the results for instantaneously random PBNs in several directions. First, and most importantly, whereas an instantaneously random PBN yields a Markov chain whose state space is composed of gene vectors, each state of the Markov chain corresponding to a context-sensitive PBN is composed of a pair, the current gene vector occupied by the network and the current constituent Boolean network. Second, the analysis is applied to PBNs with perturbation, meaning that random gene perturbation is permitted at each instant with some probability. Third, the (mathematical) influence of genes within the network is used to choose the particular gene with which to intervene. Lastly, PBNs are designed from data using a recently proposed inference procedure that takes steady-state considerations into account. The results are applied to a context-sensitive PBN derived from gene-expression data collected in a study of metastatic melanoma, the intent being to devise a control strategy that reduces the WNT5A gene's action in affecting biological regulation, since the available data suggest that disruption of this influence could reduce the chance of a melanoma metastasizing. Contact: edward@ee.tamu.edu