Journal
BMC SYSTEMS BIOLOGY
Volume 7, Issue -, Pages -Publisher
BMC
DOI: 10.1186/1752-0509-7-134
Keywords
Acetate overflow; Gene expression; Proteomics; Systems biology; Metabolic pathways analysis; Mixed-integer linear programming
Categories
Funding
- Basque Government
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available