Modeling and Control in Cancer Genomics

Prof. Aniruddha Datta, Department of Electrical Engineering, Texas A & M University

Genomics concerns the study of large sets of genes with the goal of understanding collective function, rather than that of individual genes. Such a study is important since cellular control and its failure in disease result from multivariate activity among cohorts of genes. Very recent research indicates that engineering approaches for prediction, signal processing and control are quite well suited for studying this kind of multivariate interaction. In this talk, we will present an overview of the research that has been accomplished thus far in this interdisciplinary field and point out some of the open research challenges that remain.

Among the recent paradigms that have been proposed for modeling genetic regulatory networks are the so called Probabilistic Boolean Networks (PBN’s). Such rule-based networks provide a convenient tool for studying interactions between different genes while allowing for uncertainty in the knowledge of these relationships. This talk will first introduce PBN’s as a modeling tool and then consider the issue of control in probabilistic Boolean networks. First, we will consider the following control problem: given a probabilistic Boolean network whose state transition probabilities depend on an external (control) variable, choose the sequence of control actions to minimize a given performance index over a finite number of steps. This is a standard finite horizon optimal control problem for Markov Chains and can be solved using the classical technique of Dynamic Programming. The choice of the finite horizon performance index is motivated by cancer treatment applications where one would ideally like to intervene only over a finite time horizon, then suspend treatment and observe the effects over some additional time before deciding if further intervention is necessary. Having established the connection between optimal control theory and a problem in cancer therapy, we will highlight several challenges that will have to be overcome before such methods can be used in actual clinical practice. We will also report on ongoing work and progress made in overcoming some of these challenges.