Advances in the integration of transcriptional regulatory information into genome-scale metabolic models.
A major goal of systems biology is to build predictive computational models of cellular metabolism. Availability of complete genome sequences and wealth of legacy biochemical information has led to the reconstruction of genome-scale metabolic networks in the last 15 years for several organisms across the three domains of life. Due to paucity of information on kinetic parameters associated with metabolic reactions, the constraint-based modelling approach, flux balance analysis (FBA), has proved to be a vital alternative to investigate the capabilities of reconstructed metabolic networks. In parallel, advent of high-throughput technologies has led to the generation of massive amounts of omics data on transcriptional regulation comprising mRNA transcript levels and genome-wide binding profile of transcriptional regulators. A frontier area in metabolic systems biology has been the development of methods to integrate the available transcriptional regulatory information into constraint-based models of reconstructed metabolic networks in order to increase the predictive capabilities of computational models and understand the regulation of cellular metabolism. Here, we review the existing methods to integrate transcriptional regulatory information into constraint-based models of metabolic networks.
The Institute of Mathematical Sciences (IMSc), Chennai, India.
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RP
Vivek-Ananth
The Institute of Mathematical Sciences (IMSc), Chennai, India. Electronic address: asamal@imsc.res.in.
Areejit
A
Samal
D000818:Animals; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods*; D053263:Gene Regulatory Networks / Q000235:genetics*; D006801:Humans; D053858:Metabolic Networks and Pathways / Q000235:genetics*; D008957:Models, Genetic; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D049490:Systems Biology / Q000379:methods*; D014158:Transcription, Genetic
Data integration; Flux balance analysis; Integrated regulatory-metabolic models; Metabolic networks; Transcriptional regulatory networks