Minsuk Kim1, Gwanggyu Sun1, Dong-Yup Lee2,3, Byung-Gee Kim1. 1. 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. 2. Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore 117576, Singapore. 3. Bioprocessing Technology Institute; Agency for Science, Technology and Research (A*STAR), 20 Biopolis Way, #06-01, Centros, Singapore 138668, Singapore.
Abstract
MOTIVATION: 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. RESULTS: 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.krSupplementary information: Supplementary data are available at Bioinformatics online.
MOTIVATION: 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. RESULTS: 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.krSupplementary information: Supplementary data are available at Bioinformatics online.
Authors: Selva Rupa Christinal Immanuel; Mario L Arrieta-Ortiz; Rene A Ruiz; Min Pan; Adrian Lopez Garcia de Lomana; Eliza J R Peterson; Nitin S Baliga Journal: NPJ Syst Biol Appl Date: 2021-12-06