Soft Comput (2015) 19:1595–1610 DOI 10.1007/s00500-014-1467-6 FOCUS

Abstract

© The Author(s) 2014. This article is published with open access at Springerlink.com Abstract Computational modelling of biochemical sys-tems based on top-down and bottom-up approaches has been well studied over the last decade. In this research, after illus-trating how to generate atomic components by a set of given reactants and two user pre-defined component patterns, we propose an integrative top-down and bottom-up modelling approach for stepwise qualitative exploration of interactions among reactants in biochemical systems. Evolution strat-egy is applied to the top-down modelling approach to com-pose models, and simulated annealing is employed in the bottom-up modelling approach to explore potential interac-tions based on models constructed from the top-down mod-elling process. Both the top-down and bottom-up approaches support stepwise modular addition or subtraction for the model evolution. Experimental results indicate that our mod-elling approach is feasible to learn the relationships among biochemical reactants qualitatively. In addition, hidden reac-tants of the target biochemical system can be obtained by generating complex reactants in corresponding composed models. Moreover, qualitatively learned models with inferre