Intervention in context-sensitive probabilistic Boolean networks.
BACKGROUND
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.
Pal
Ranadip
R
Department of Electrical Engineering, Texas A&M University, College Station, TX 77843, USA.
Datta
Aniruddha
A
Bittner
Michael L
ML
Dougherty
Edward R
ER
D000465:Algorithms; D000818:Animals; D045744:Cell Line, Tumor; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000502:physiology*; D016015:Logistic Models; D008545:Melanoma / Q000235:genetics / Q000378:metabolism*; D051379:Mice; D008954:Models, Biological; D015233:Models, Statistical; D009363:Neoplasm Proteins / Q000235:genetics / Q000378:metabolism; D011518:Proto-Oncogene Proteins / Q000235:genetics / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*; D014157:Transcription Factors / Q000378:metabolism; D051153:Wnt Proteins; D000071818:Wnt-5a Protein
D009363:Neoplasm Proteins; D011518:Proto-Oncogene Proteins; D014157:Transcription Factors; D051153:Wnt Proteins; D000071818:Wnt-5a Protein; C438615:Wnt5a protein, mouse