BeReTa: a systematic method for identifying target transcriptional regulators to enhance microbial production of chemicals.
Modulation of regulatory circuits governing the metabolic processes is a crucial step for developing microbial cell factories. Despite the prevalence of in silico strain design algorithms, most of them are not capable of predicting required modifications in regulatory networks. Although a few algorithms may predict relevant targets for transcriptional regulator (TR) manipulations, they have limited reliability and applicability due to their high dependency on the availability of integrated metabolic/regulatory models.



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.



MATLAB code is available at https://github.com/kms1041/BeReTa (github).



byungkim@snu.ac.krSupplementary information: Supplementary data are available at Bioinformatics online.
School of Chemical and Biological Engineering, Institute of Molecular Biology and Genetics, and Bioengineering Institute, Seoul National University, 1, Gwanak-ro, Gwanak-gu, Seoul 151-742, Republic of Korea.
Minsuk
M
Kim
School of Chemical and Biological Engineering, Institute of Molecular Biology and Genetics, and Bioengineering Institute, Seoul National University, 1, Gwanak-ro, Gwanak-gu, Seoul 151-742, Republic of Korea.
Gwanggyu
G
Sun
Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore 117576, Singapore.|Bioprocessing Technology Institute; Agency for Science, Technology and Research (A*STAR), 20 Biopolis Way, #06-01, Centros, Singapore 138668, Singapore.
Dong-Yup
DY
Lee
School of Chemical and Biological Engineering, Institute of Molecular Biology and Genetics, and Bioengineering Institute, Seoul National University, 1, Gwanak-ro, Gwanak-gu, Seoul 151-742, Republic of Korea.
Byung-Gee
BG
Kim
D000465:Algorithms; D003198:Computer Simulation; D004926:Escherichia coli / Q000235:genetics / Q000378:metabolism; D015964:Gene Expression Regulation, Bacterial; D053263:Gene Regulatory Networks; D053858:Metabolic Networks and Pathways; D008954:Models, Biological; D015203:Reproducibility of Results; D048372:Streptomyces coelicolor / Q000235:genetics / Q000378:metabolism; D014158:Transcription, Genetic