Turnover Dependent Phenotypic Simulation: A Quantitative Constraint-Based Simulation Method That Accommodates All Main Strain Design Strategies

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Pereira, RuiChalmers tekniska högskola,Chalmers University of Technology,Universidade do Minho,University of Minho (author)

Turnover Dependent Phenotypic Simulation: A Quantitative Constraint-Based Simulation Method That Accommodates All Main Strain Design Strategies

Article/chapterEnglish2019

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2019-03-29
American Chemical Society (ACS),2019

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LIBRIS-ID:oai:research.chalmers.se:348c7e79-4770-4ed1-b722-0e6349d2ec89
https://research.chalmers.se/publication/510115URI
https://doi.org/10.1021/acssynbio.8b00248DOI

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Language:English
Summary in:English

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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 and genre

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
network rigidity
metabolic engineering
metabolite turnovers
genome-scale models
Saccharomyces cerevisiae
phenotype simulation

Added entries (persons, corporate bodies, meetings, titles ...)

Vilaca, P.Universidade do Minho,University of Minho (author)
Maia, P.Universidade do Minho,University of Minho (author)
Nielsen, Jens B,1962Chalmers tekniska högskola,Chalmers University of Technology,Danmarks Tekniske Universitet,Technical University of Denmark(Swepub:cth)nielsenj (author)
Rocha, I.Nova University of Lisbon, Portugal,Universidade do Minho,University of Minho (author)
Chalmers tekniska högskolaUniversidade do Minho (creator_code:org_t)

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In:ACS Synthetic Biology: American Chemical Society (ACS)8:5, s. 976-9882161-5063

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By the author/editor: Pereira, Rui; Vilaca, P.; Maia, P.; Nielsen, Jens B, ...; Rocha, I.

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ENGINEERING AND TECHNOLOGY: ENGINEERING AND ...; and Mechanical Engin ...; and Applied Mechanic ...

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