Inference of biochemical network models in S-system using multiobjective optimization approach

• Published in: Bioinformatics Vol. 24; no. 8; pp. 1085 - 1092
• Main Authors: Liu, Pang-Kai, Wang, Feng-Sheng
• Format: Journal Article
• Language: English
• Published: Oxford Oxford University Press 15.04.2008; Oxford Publishing Limited (England)
• Subjects: Algorithms; Bioinformatics; Biological and medical sciences; Computer Simulation; Fundamental and applied biological sciences. Psychology; Gene Expression Profiling - methods; General aspects; Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects); Models, Biological; Models, Statistical; Protein Interaction Mapping - methods; Proteome - metabolism; Signal Transduction - physiology; Software; Inference; Models; Bioinformatics; Optimization; Network
• ISSN: 1367-4803; 1367-4811; 1460-2059; 1367-4811
• DOI: 10.1093/bioinformatics/btn075

Motivation: The inference of biochemical networks, such as gene regulatory networks, protein–protein interaction networks, and metabolic pathway networks, from time-course data is one of the main challenges in systems biology. The ultimate goal of inferred modeling is to obtain expressions that quantitatively understand every detail and principle of biological systems. To infer a realizable S-system structure, most articles have applied sums of magnitude of kinetic orders as a penalty term in the fitness evaluation. How to tune a penalty weight to yield a realizable model structure is the main issue for the inverse problem. No guideline has been published for tuning a suitable penalty weight to infer a suitable model structure of biochemical networks. Results: We introduce an interactive inference algorithm to infer a realizable S-system structure for biochemical networks. The inference problem is formulated as a multiobjective optimization problem to minimize simultaneously the concentration error, slope error and interaction measure in order to find a suitable S-system model structure and its corresponding model parameters. The multiobjective optimization problem is solved by the ε-constraint method to minimize the interaction measure subject to the expectation constraints for the concentration and slope error criteria. The theorems serve to guarantee the minimum solution for the ε-constrained problem to achieve the minimum interaction network for the inference problem. The approach could avoid assigning a penalty weight for sums of magnitude of kinetic orders. Contact: chmfsw@ccu.edu.tw Supplementary information: Supplementary data are available at Bioinformatics online.