Computational modelling of biochemical systems\nbased on top-down and bottom-up approaches has been\nwell studied over the last decade. In this research, after illustrating\nhow to generate atomic components by a set of given\nreactants and two user pre-defined component patterns, we\npropose an integrative top-down and bottom-up modelling\napproach for stepwise qualitative exploration of interactions\namong reactants in biochemical systems. Evolution strategy\nis applied to the top-down modelling approach to compose\nmodels, and simulated annealing is employed in the\nbottom-up modelling approach to explore potential interactions\nbased on models constructed from the top-down modelling\nprocess. Both the top-down and bottom-up approaches\nsupport stepwise modular addition or subtraction for the\nmodel evolution. Experimental results indicate that our modelling\napproach is feasible to learn the relationships among\nbiochemical reactants qualitatively. In addition, hidden reactants\nof the target biochemical system can be obtained by\ngenerating complex reactants in corresponding composed\nmodels. Moreover, qualitatively learned models with inferred reactants and alternative topologies can be used for further\nweb-lab experimental investigations by biologists of interest,\nwhich may result in a better understanding of the system.
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