Gene perturbation and intervention in probabilistic Boolean networks.
BACKGROUND
A major objective of gene regulatory network modeling, in addition to gaining a deeper understanding of genetic regulation and control, is the development of computational tools for the identification and discovery of potential targets for therapeutic intervention in diseases such as cancer. We consider the general question of the potential effect of individual genes on the global dynamical network behavior, both from the view of random gene perturbation as well as intervention in order to elicit desired network behavior.


RESULTS
Using a recently introduced class of models, called Probabilistic Boolean Networks (PBNs), this paper develops a model for random gene perturbations and derives an explicit formula for the transition probabilities in the new PBN model. This result provides a building block for performing simulations and deriving other results concerning network dynamics. An example is provided to show how the gene perturbation model can be used to compute long-term influences of genes on other genes. Following this, the problem of intervention is addressed via the development of several computational tools based on first-passage times in Markov chains. The consequence is a methodology for finding the best gene with which to intervene in order to most likely achieve desirable network behavior. The ideas are illustrated with several examples in which the goal is to induce the network to transition into a desired state, or set of states. The corresponding issue of avoiding undesirable states is also addressed. Finally, the paper turns to the important problem of assessing the effect of gene perturbations on long-run network behavior. A bound on the steady-state probabilities is derived in terms of the perturbation probability. The result demonstrates that states of the network that are more 'easily reachable' from other states are more stable in the presence of gene perturbations. Consequently, these are hypothesized to correspond to cellular functional states.


BACKGROUND
A library of functions written in MATLAB for simulating PBNs, constructing state-transition matrices, computing steady-state distributions, computing influences, modeling random gene perturbations, and finding optimal intervention targets, as described in this paper, is available on request from is@ieee.org.
Shmulevich
Ilya
I
Cancer Genomics Laboratory, University of Texas M. D. Anderson Cancer Center, 1515 Holcombe Blvd, Box 85, Houston, TX 77030, USA. is@ieee.org
Dougherty
Edward R
ER
Zhang
Wei
W
D002874:Chromosome Mapping / Q000379:methods; D003198:Computer Simulation; D005786:Gene Expression Regulation / Q000235:genetics*; D005818:Genetic Engineering / Q000379:methods*; D016045:Human Genome Project; D008957:Models, Genetic; D015233:Models, Statistical; D016296:Mutagenesis / Q000235:genetics; D009154:Mutation / Q000235:genetics; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013269:Stochastic Processes