Detalle de: Enhanced parallel differential evolution algorithm for problems in computational systems biology

Graphical abstractDisplay Omitted HighlightsWe consider global optimization problems in computational systems biology.We propose an asynchronous parallel implementation of Differential Evolution (DE).The new algorithm also incorporates improved heuristics to exploit the specific structure of the systems biology problems.Tested with benchmarks problems, including the calibration of non-linear dynamic models of biological systems.Excellent results both in terms of quality of the solution and regarding speedup and scalability. Many key problems in computational systems biology and bioinformatics can be formulated and solved using a global optimization framework. The complexity of the underlying mathematical models require the use of efficient solvers in order to obtain satisfactory results in reasonable computation times. Metaheuristics are gaining recognition in this context, with Differential Evolution (DE) as one of the most popular methods. However, for most realistic applications, like those considering parameter estimation in dynamic models, DE still requires excessive computation times.Here we consider this latter class of problems and present several enhancements to DE based on the introduction of additional algorithmic steps and the exploitation of parallelism. In particular, we propose an asynchronous parallel implementation of DE which has been extended with improved heuristics to exploit the specific structure of parameter estimation problems in computational systems biology. The proposed method is evaluated with different types of benchmarks problems: (i) black-box global optimization problems and (ii) calibration of non-linear dynamic models of biological systems, obtaining excellent results both in terms of quality of the solution and regarding speedup and scalability.