Integrating gene and protein expression data with genome-scale metabolic networks to infer functional pathways

BMC Systems Biology

Francisco J Planes³  John E Beasley¹  Angel Rubio³  Kaspar Valgepea²  Jon Pey³ 

[1] Mathematical Sciences, Brunel University, John Crank 505, Kingston Lane, Uxbridge UB8 3PH, UK;Competence Centre of Food and Fermentation Technologies, Akadeemia tee 15a, 12618 Tallinn, Estonia;CEIT and TECNUN, University of Navarra, Manuel de Lardizabal 15, 20018 San Sebastian, Spain

关键词: Mixed-integer linear programming;    Metabolic pathways analysis;    Systems biology;    Proteomics;    Gene expression;    Acetate overflow;   

Others  :  1141707
DOI  :  10.1186/1752-0509-7-134

 received in 2013-06-20, accepted in 2013-11-27,  发布年份 2013

【 摘 要 】

Background

The study of cellular metabolism in the context of high-throughput -omics data has allowed us to decipher novel mechanisms of importance in biotechnology and health. To continue with this progress, it is essential to efficiently integrate experimental data into metabolic modeling.

Results

We present here an in-silico framework to infer relevant metabolic pathways for a particular phenotype under study based on its gene/protein expression data. This framework is based on the Carbon Flux Path (CFP) approach, a mixed-integer linear program that expands classical path finding techniques by considering additional biophysical constraints. In particular, the objective function of the CFP approach is amended to account for gene/protein expression data and influence obtained paths. This approach is termed integrative Carbon Flux Path (iCFP). We show that gene/protein expression data also influences the stoichiometric balancing of CFPs, which provides a more accurate picture of active metabolic pathways. This is illustrated in both a theoretical and real scenario. Finally, we apply this approach to find novel pathways relevant in the regulation of acetate overflow metabolism in Escherichia coli. As a result, several targets which could be relevant for better understanding of the phenomenon leading to impaired acetate overflow are proposed.

Conclusions

A novel mathematical framework that determines functional pathways based on gene/protein expression data is presented and validated. We show that our approach is able to provide new insights into complex biological scenarios such as acetate overflow in Escherichia coli.

【 授权许可】

   
2013 Pey et al.; licensee BioMed Central Ltd.

【 图 表 】

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