Confirmation versus Search in Gene Regulation : The Complexity of Gene Perturbation Experimentation as a Search Method
Various proposals have been made for understanding gene regulation through measurements of differential expression in wild type versus strains in which expression of specific genes has been suppressed or enhanced, as well as determining the most informative next experiment in a sequence. While the behavior of these algorithms has been investigated for toy examples, the full computational complexity of the problem has not received sufficient attention. We show that finding the true regulatory network requires (in the worst-case) exponentially many experiments (in the number of genes). Perhaps more importantly, we provide an algorithm for determining the set of regulatory networks consistent with the observed data. We then show that this algorithm is infeasible for realistic data (specifically, nine genes and ten experiments). This infeasibility is not due to some flaw in the algorithm, but rather to the fact that there are far too many networks consistent with the data (at least 10, to be precise). We conclude that gene perturbation experiments are useful in confirming regulatory network models discovered by other techniques, but not a feasible search strategy.