Abstract:

Biological systems are fundamentally composed of two types of information: genes encoding proteins to execute the functions of life and regulatory networks controlling the hierarchical flow of information and metabolic capacity of an organism. Consequently, life science research is shifting away from a reductionist approach to a system-based perspective to better support metabolic engineering efforts. We present a novel integrated whole genome/gene expression data analysis strategy to identify critical rate-limiting steps in metabolic systems.   By assuming a systematic approach for identifying novel regulatory elements of related cellular processes based on large sets of mRNA profiling experiments to a highly-relevant production organism (B. subtilis), we were able to reconstruct metabolic pathways, analyze regulatory networks and predict relevant transcription factor binding sites potentially influencing information flow and metabolic capacity of the organism. We will also discuss how this data driven computational approach can be used to obtain a comprehensive understanding of the metabolism of current production strains and factors distinguishing them from their low-producing ancestor strains.  In conclusion, using transcriptomics, proteomics and metabolomics profiling data with a cross-omics integrative software solution like Genedata Phylosopher^® , enables investigators to further support a systems biology approach to metabolic engineering.