TRFBA : An algorithm to integrate genome-scale metabolic and transcriptional regulatory networks with incorporation of expression data
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2017-04-01
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Abstract
Motivation: Integration of different biological networks and data-types has been a major challenge in systems biology. The present study introduces the transcriptional regulated flux balance analysis (TRFBA) algorithm that integrates transcriptional regulatory and metabolic models using a set of expression data for various perturbations. Results: TRFBA considers the expression levels of genes as a new continuous variable and introduces two new linear constraints. The first constraint limits the rate of reaction(s) supported by a metabolic gene using a constant parameter (C) that converts the expression levels to the upper bounds of the reactions. Considering the concept of constraint-based modeling, the second set of constraints correlates the expression level of each target gene with that of its regulating genes. A set of constraints and binary variables was also added to prevent the second set of constraints from overlapping. TRFBA was implemented on Escherichia coli and Saccharomyces cerevisiae models to estimate growth rates under various environmental and genetic perturbations. The error sensitivity to the algorithm parameter was evaluated to find the best value of C. The results indicate a significant improvement in the quantitative prediction of growth in comparison with previously presented algorithms. The robustness of the algorithm to change in the expression data and the regulatory network was tested to evaluate the effect of noisy and incomplete data. Furthermore, the use of added constraints for perturbations without their gene expression profile demonstrates that these constraints can be applied to improve the growth prediction of FBA. Availability and Implementation: TRFBA is implemented in Matlab software and requires COBRA toolbox. Source code is freely available at http://sbme.modares.ac.ir.
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Publisher Copyright: © The Author 2016. Published by Oxford University Press.
Keywords
1.6 Biological sciences, 2.9 Industrial biotechnology, 3.1 Basic medicine, 1.1. Scientific article indexed in Web of Science and/or Scopus database, Statistics and Probability, Biochemistry, Molecular Biology, Computer Science Applications, Computational Theory and Mathematics, Computational Mathematics
Citation
Motamedian, E, Mohammadi, M, Shojaosadati, S A & Heydari, M 2017, 'TRFBA : An algorithm to integrate genome-scale metabolic and transcriptional regulatory networks with incorporation of expression data', Bioinformatics, vol. 33, no. 7, pp. 1057-1063. https://doi.org/10.1093/bioinformatics/btw772