Comparing Integer Linear Programming to SAT-Solving for Hard Problems in Computational and Systems Biology

It is useful to have general-purpose solution methods that can be applied to a wide range of problems, rather than relying on the development of clever, intricate algorithms for each specific problem. Integer Linear Programming is the most widely-used such general-purpose solution method. It is successful in a wide range of problems. However, there are some problems in computational biology where integer linear programming has had only limited success. In this paper, we explore an alternate, general-purpose solution method: SAT-solving, i.e., constructing Boolean formulas in conjunctive normal form (CNF) that encode a problem instance, and using a SAT-solver to determine if the CNF formula is satisfiable or not. In three hard problems examined, we were very surprised to find the SAT-solving approach was dramatically better than the ILP approach in two problems; and a little slower, but more robust, in the third problem. We also re-examined and confirmed an earlier result on a fourth problem, using current ILP and SAT-solvers. These results should encourage further efforts to exploit SAT-solving in computational biology.