Application of logic synthesis to the understanding and cure of genetic diseases

In the quest to understand and cure genetic diseases such as cancer, the fundamental approach being taken is undergoing a gradual change. It is becoming more acceptable to view these diseases as an engineering problem, and systems engineering approaches are becoming more accepted as a means to tackle genetic diseases. In this light, we believe that logic synthesis techniques can play a very important role. Several techniques from the field of logic synthesis can be adapted to assist in the arguably huge effort of modeling and controlling such diseases. The set of genes that control a particular genetic disease can be modeled as a Finite State Machine (FSM) called the Gene Regulatory Network (GRN). Important problems include (i) inferring the GRN from observed gene expression data from patients and (ii) assuming that such a GRN exists, determining the ”best” set of drugs so that the disease is ”maximally” cured. In this paper, we report initial results on the application of logic synthesis techniques that we have developed to address both these problems. In the first technique, we present Boolean Satisfiability (SAT) based approaches to infer the logical support of each gene that regulates melanoma, using gene expression data from patients of the disease. From the output of such a tool, biologists can construct targeted experiments to understand the logic functions that regulate a particular gene. The second technique assumes that the GRN is known, and uses a weighted partial Max-SAT formulation to find the set of drugs with the least side-effects, that steer the GRN state towards one that is closest to that of a healthy individual, in the context of colon cancer. Our group is currently exploring the application of several other logic techniques to a variety of related problems in this domain.