期刊
BIOINFORMATICS
卷 33, 期 1, 页码 87-94出版社
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btw557
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资金
- National Research Foundation of Korea (NRF)-Ministry of Science, ICT & Future Planning [NRF2013R1A2A2A01069197, NRF-2014K1A3A1A20034749]
- project of Global PhD Fellowship which NRF [NRF-2012H1A2A1001956]
- Synthetic Biology Initiative [DPRT/943/09/14]
- Academic Research Fund of the National University of Singapore [R-279-000-476-112]
- National Research Foundation of Korea [2012H1A2A1001956] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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.
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