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An integrative top-down and bottom-up qualitative model construction framework for exploration of biochemical systems.

Authors
  • Wu, Zujian1
  • Pang, Wei2
  • Coghill, George M2
  • 1 College of Information Science and Technology, Jinan University, Guangzhou, 510632 People's Republic of China ; School of Natural and Computing Sciences, University of Aberdeen, Aberdeen, AB24 3UE Scotland, UK. , (China)
  • 2 School of Natural and Computing Sciences, University of Aberdeen, Aberdeen, AB24 3UE Scotland, UK.
Type
Published Article
Journal
Soft computing
Publication Date
Jan 01, 2015
Volume
19
Issue
6
Pages
1595–1610
Identifiers
PMID: 25999782
Source
Medline
Keywords
License
Unknown

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.

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