Multimethod Optimization for Reverse Engineering of Complex Biological Networks

Optimization problems appears in different areas of science and engineering. This paper considers the general problem of reverse engineering in computational biology by means of mixed-integer nonlinear dynamic optimization (MIDO). Although this kind of problems are typically hard, solutions can be achieved for rather complex networks by applying global optimization metaheuristics. The main objective of this work is to handle them by means of multimethod optimization, in which different metaheuristics cooperate to outperform the results obtained by any of them isolated. For its preliminary evaluation we consider a synthetic signaling pathway case study and we assess the performance of the proposal on a public cloud. These results open up new possibilities for other MIDO-based large-scale applications in computational systems biology.