Abstract
Computational modelling of biochemical systems based on top-down and bottom-up approaches has been well studied over the last decade. In this research, after illustrating 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 strategy is applied to the top-down modelling approach to compose models, and simulated annealing is employed in the bottom-up modelling approach to explore potential interactions based on models constructed from the top-down modelling process. Both the top-down and bottom-up approaches support stepwise modular addition or subtraction for the model evolution. Experimental results indicate that our modelling approach is feasible to learn the relationships among biochemical reactants qualitatively. In addition, hidden reactants of the target biochemical system can be obtained by generating complex reactants in corresponding composed models. Moreover, qualitatively learned models with inferred reactants and alternative topologies can be used for further web-lab experimental investigations by biologists of interest, which may result in a better understanding of the system.
Highlights
The goal of understanding species behaviour and essential functions of a natural biochemical system can be achieved by obtaining information of individual parts and corresponding interactions within the system
We focus on qualitative modelling of biochemical systems, which has a similar task to analyse and obtain the structure of a target biochemical system using qualitative states abstracted from numerical quantitative data
We briefly introduce the formation of components from two sets of reactant labels provided by the users: given a set of reactants as species Sspecies and another set of reactants as enzymes Senzymes, each element in Sspecies is selected in turn to be combined with each element in Senzymes to produce a complex and a new reactant, based on the mass-action 1 (MA1) kinetic law (Breitling et al 2008)
Summary
The goal of understanding species behaviour and essential functions of a natural biochemical system can be achieved by obtaining information of individual parts and corresponding interactions within the system. Computational modelling of biochemical systems aims to generate models representing target biochemical systems in terms of behaviour and interactions among biochemical components controlled by kinetic rates and concentrations of species. Qualitative states of reactants in target biochemical systems are used to guide the model construction during the evolutionary modelling process These developed and evolved models are used by biologists and modellers to confirm experimental results, verify hypotheses made upon the target biochemical system, and predict undiscovered biochemical processes with hidden components. We focus on qualitative modelling of biochemical systems, which has a similar task to analyse and obtain the structure of a target biochemical system using qualitative states abstracted from numerical quantitative data. A model should at least consist of one component, which ensures a minimal model structure for further evolutionary model construction
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