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PLoS Comput Biol. 2019 Mar 8;15(3):e1006835. doi: 10.1371/journal.pcbi.1006835. eCollection 2019 Mar.

OptRAM: In-silico strain design via integrative regulatory-metabolic network modeling.

Shen F1,2,3, Sun R1,2, Yao J2, Li J1,2, Liu Q2, Price ND4, Liu C2, Wang Z1,2.

Author information

1
Bio-X Institutes, Key laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, China.
2
School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
3
Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan, United States of America.
4
Institute for Systems Biology, Seattle, Washington, United States of America.

Abstract

The ultimate goal of metabolic engineering is to produce desired compounds on an industrial scale in a cost effective manner. To address challenges in metabolic engineering, computational strain optimization algorithms based on genome-scale metabolic models have increasingly been used to aid in overproducing products of interest. However, most of these strain optimization algorithms utilize a metabolic network alone, with few approaches providing strategies that also include transcriptional regulation. Moreover previous integrated approaches generally require a pre-existing regulatory network. In this study, we developed a novel strain design algorithm, named OptRAM (Optimization of Regulatory And Metabolic Networks), which can identify combinatorial optimization strategies including overexpression, knockdown or knockout of both metabolic genes and transcription factors. OptRAM is based on our previous IDREAM integrated network framework, which makes it able to deduce a regulatory network from data. OptRAM uses simulated annealing with a novel objective function, which can ensure a favorable coupling between desired chemical and cell growth. The other advance we propose is a systematic evaluation metric of multiple solutions, by considering the essential genes, flux variation, and engineering manipulation cost. We applied OptRAM to generate strain designs for succinate, 2,3-butanediol, and ethanol overproduction in yeast, which predicted high minimum predicted target production rate compared with other methods and previous literature values. Moreover, most of the genes and TFs proposed to be altered by OptRAM in these scenarios have been validated by modification of the exact genes or the target genes regulated by the TFs, for overproduction of these desired compounds by in vivo experiments cataloged in the LASER database. Particularly, we successfully validated the predicted strain optimization strategy for ethanol production by fermentation experiment. In conclusion, OptRAM can provide a useful approach that leverages an integrated transcriptional regulatory network and metabolic network to guide metabolic engineering applications.

PMID:
30849073
PMCID:
PMC6426274
DOI:
10.1371/journal.pcbi.1006835
[Indexed for MEDLINE]
Free PMC Article

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