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Turnover Dependent ...
Turnover Dependent Phenotypic Simulation: A Quantitative Constraint-Based Simulation Method That Accommodates All Main Strain Design Strategies
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- Pereira, Rui (author)
- Chalmers tekniska högskola,Chalmers University of Technology,Universidade do Minho,University of Minho
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- Vilaca, P. (author)
- Universidade do Minho,University of Minho
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- Maia, P. (author)
- Universidade do Minho,University of Minho
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- Nielsen, Jens B, 1962 (author)
- Chalmers tekniska högskola,Chalmers University of Technology,Danmarks Tekniske Universitet,Technical University of Denmark
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- Rocha, I. (author)
- Nova University of Lisbon, Portugal,Universidade do Minho,University of Minho
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(creator_code:org_t)
- 2019-03-29
- 2019
- English.
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In: ACS Synthetic Biology. - : American Chemical Society (ACS). - 2161-5063. ; 8:5, s. 976-988
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Abstract
Subject headings
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- The uncertain relationship between genotype and phenotype can make strain engineering an arduous trial and error process. To identify promising gene targets faster, constraint-based modeling methodologies are often used, although they remain limited in their predictive power. Even though the search for gene knockouts is fairly established in constraint-based modeling, most strain design methods still model gene up/down-regulations by forcing the corresponding flux values to fixed levels without taking in consideration the availability of resources. Here, we present a constraint-based algorithm, the turnover dependent phenotypic simulation (TDPS) that quantitatively simulates phenotypes in a resource conscious manner. Unlike other available algorithms, TDPS does not force flux values and considers resource availability, using metabolite production turnovers as an indicator of metabolite abundance. TDPS can simulate up-regulation of metabolic reactions as well as the introduction of heterologous genes, alongside gene deletion and down-regulation scenarios. TDPS simulations were validated using engineered Saccharomyces cerevisiae strains available in the literature by comparing the simulated and experimental production yields of the target metabolite. For many of the strains evaluated, the experimental production yields were within the simulated intervals and the relative strain performance could be predicted with TDPS. However, the algorithm failed to predict some of the production changes observed experimentally, suggesting that further improvements are necessary. The results also showed that TDPS may be helpful in finding metabolic bottlenecks, but further experiments would be required to confirm these findings.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Maskinteknik -- Teknisk mekanik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Mechanical Engineering -- Applied Mechanics (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Bioinformatik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Bioinformatics (hsv//eng)
- NATURVETENSKAP -- Biologi -- Bioinformatik och systembiologi (hsv//swe)
- NATURAL SCIENCES -- Biological Sciences -- Bioinformatics and Systems Biology (hsv//eng)
Keyword
- network rigidity
- metabolic engineering
- metabolite turnovers
- genome-scale models
- Saccharomyces cerevisiae
- phenotype simulation
Publication and Content Type
- art (subject category)
- ref (subject category)
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