BeReTa: a systematic method for identifying target transcriptional regulators to enhance microbial production of chemicals

We present BeReTa (Beneficial Regulator Targeting), a new algorithm for prioritization of TR manipulation targets, which makes use of unintegrated network models. BeReTa identifies TR manipulation targets by evaluating regulatory strengths of interactions and beneficial effects of reactions, and subsequently assigning beneficial scores for the TRs. We demonstrate that BeReTa can predict both known and novel TR manipulation targets for enhanced production of various chemicals in Escherichia coli. Furthermore, through a case study of antibiotics production in Streptomyces coelicolor, we successfully demonstrate its wide applicability to even less-studied organisms. To the best of our knowledge, BeReTa is the first strain design algorithm exclusively designed for predicting TR manipulation targets. Availability and Implementation: MATLAB code is available at https://github.com/kms1041/BeReTa (github). Contact: byungkim@snu.ac.kr Supplementary information: Supplementary data are available at Bioinformatics online.
Source: Bioinformatics - Category: Bioinformatics Authors: Tags: SYSTEMS BIOLOGY Source Type: research