1. BeReTa: a systematic method for identifying target transcriptional regulators to enhance microbial production of chemicals
- Author
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Gwanggyu Sun, Dong-Yup Lee, Byung-Gee Kim, and Minsuk Kim
- Subjects
0301 basic medicine ,Statistics and Probability ,Transcription, Genetic ,Computer science ,medicine.drug_class ,In silico ,Cell ,Antibiotics ,Gene regulatory network ,Streptomyces coelicolor ,Computational biology ,Models, Biological ,Biochemistry ,03 medical and health sciences ,Escherichia coli ,medicine ,Transcriptional regulation ,Production (economics) ,Computer Simulation ,Gene Regulatory Networks ,Molecular Biology ,biology ,Reproducibility of Results ,Gene Expression Regulation, Bacterial ,biology.organism_classification ,Computer Science Applications ,Computational Mathematics ,030104 developmental biology ,medicine.anatomical_structure ,Computational Theory and Mathematics ,Algorithms ,Metabolic Networks and Pathways - 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). Supplementary information Supplementary data are available at Bioinformatics online.
- Published
- 2016