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OptRAM: In-silico strain design via integrative regulatory-metabolic network modeling
- Source :
- PLoS Computational Biology, Vol 15, Iss 3, p e1006835 (2019), PLoS Computational Biology
- Publication Year :
- 2019
- Publisher :
- Public Library of Science (PLoS), 2019.
-
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.<br />Author summary Computational strain design algorithms based on genome-scale metabolic models have increasingly been used to guide rational strain design for metabolic engineering. However, most strain optimization algorithms only utilize a metabolic network alone and cannot provide strategies that also involve transcriptional regulation. In this paper, we developed a novel strain design algorithm, named OptRAM (Optimization of Regulatory And Metabolic Network), which can identify combinatorial optimization strategies including overexpression, knockdown or knockout of both transcription factors and metabolic genes, based on our previous IDREAM integrated network framework. OptRAM uses simulated annealing with a novel objective function, which can ensure a favorable coupling between the production of a desired chemical and cell growth. This strategy can be deployed for strain design of bacteria, archaea or eukaryotes. The other advantage of OptRAM compared with previous heuristic approaches is that we systematically evaluated the implementation cost of different solutions and selected strain designs which are more likely to be achievable in experiments. Through the in-silico strain design case studies for producing succinate, 2,3-butanediol, and ethanol in yeast, we demonstrated that OptRAM can identify strategies that increase production beyond what is seen currently, or found as potential designs using alternative methods. We also validated the modified genes chosen by OptRAM in example cases against previous in vivo experiments in the LASER database. Additionally, we experimentally validated the ethanol strain design by evaluating its performance in fermentation. OptRAM provides a robust approach to strain design across gene regulatory network modification and metabolic engineering.
- Subjects :
- 0301 basic medicine
Computer science
Gene regulatory network
Gene Expression
Metabolic network
Yeast and Fungal Models
0302 clinical medicine
Gene Regulatory Networks
Biology (General)
Simulated Annealing
Butylene Glycols
Ecology
Organic Compounds
Transcriptional Control
Applied Mathematics
Simulation and Modeling
Eukaryota
Chemistry
Experimental Organism Systems
Metabolic Engineering
Computational Theory and Mathematics
Modeling and Simulation
Physical Sciences
Simulated annealing
Metric (mathematics)
Network Analysis
Algorithms
Metabolic Networks and Pathways
Research Article
Computer and Information Sciences
QH301-705.5
In silico
Genes, Fungal
Saccharomyces cerevisiae
Computational biology
Research and Analysis Methods
Models, Biological
Metabolic engineering
Metabolic Networks
Saccharomyces
03 medical and health sciences
Cellular and Molecular Neuroscience
Model Organisms
Genetics
Gene Regulation
Computer Simulation
Molecular Biology
Ecology, Evolution, Behavior and Systematics
Ethanol
Organic Chemistry
Chemical Compounds
Organisms
Fungi
Biology and Life Sciences
Computational Biology
Yeast
030104 developmental biology
Alcohols
Mutation
Animal Studies
Combinatorial optimization
Flux (metabolism)
Mathematics
030217 neurology & neurosurgery
Subjects
Details
- Language :
- English
- ISSN :
- 15537358
- Volume :
- 15
- Issue :
- 3
- Database :
- OpenAIRE
- Journal :
- PLoS Computational Biology
- Accession number :
- edsair.doi.dedup.....539cceb7e2a305aca4d9577d3493cdb2