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Fine-tuning tomato agronomic properties by computational genome redesign.

Carrera J, Fernández Del Carmen A, Fernández-Muñoz R, Rambla JL, Pons C, Jaramillo A, Elena SF, Granell A - PLoS Comput. Biol. (2012)

Bottom Line: Considering cells as biofactories, we aimed to optimize its internal processes by using the same engineering principles that large industries are implementing nowadays: lean manufacturing.Based on predicted metabolic profiles, a close association with agronomic and organoleptic properties of the ripe fruit was revealed with high statistical confidence.Inspired in a synthetic biology approach, the model was used for exploring the landscape of all possible local transcriptional changes with the aim of engineering tomato fruits with fine-tuned biotechnological properties.

View Article: PubMed Central - PubMed

Affiliation: Instituto de Biologa Molecular y Celular de Plantas, Consejo Superior de Investigaciones Cientificas-UPV, Valencia, Spain. Javier.Carrera@synth-bio.org

ABSTRACT
Considering cells as biofactories, we aimed to optimize its internal processes by using the same engineering principles that large industries are implementing nowadays: lean manufacturing. We have applied reverse engineering computational methods to transcriptomic, metabolomic and phenomic data obtained from a collection of tomato recombinant inbreed lines to formulate a kinetic and constraint-based model that efficiently describes the cellular metabolism from expression of a minimal core of genes. Based on predicted metabolic profiles, a close association with agronomic and organoleptic properties of the ripe fruit was revealed with high statistical confidence. Inspired in a synthetic biology approach, the model was used for exploring the landscape of all possible local transcriptional changes with the aim of engineering tomato fruits with fine-tuned biotechnological properties. The method was validated by the ability of the proposed genomes, engineered for modified desired agronomic traits, to recapitulate experimental correlations between associated metabolites.

Show MeSH
Lean Manufacturing as a model applied in systems and synthetic biology.From omic data (transcriptomics, metabolomics and phenomics), a quantitative global model was constructed using reverse engineering methods. The predictive model was used to propose genome perturbations, to improve desired phenotypes with relevant biotechnological applications. The genome perturbations were guided by an in silico optimization that imposed the desired selective pressure.
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pcbi-1002528-g001: Lean Manufacturing as a model applied in systems and synthetic biology.From omic data (transcriptomics, metabolomics and phenomics), a quantitative global model was constructed using reverse engineering methods. The predictive model was used to propose genome perturbations, to improve desired phenotypes with relevant biotechnological applications. The genome perturbations were guided by an in silico optimization that imposed the desired selective pressure.

Mentions: We have extended our recently developed inference methodology, InferGene[7], to obtain a gene regulatory model coupled to metabolism that allows us analyzing optimality in terms of specified agronomic and organoleptic properties of the tomato fruit (Figure 1). For this, we have taken advantage of an experimentally characterized subset of the metabolome of 169 tomato RILs, which includes the accumulation levels of 67 metabolites in the fruit and that contribute to the flavor (sugars, acids and some volatiles), aroma (volatiles) and other quality traits (such as color and healthy carotenoids and vitamins). Moreover, we have also used the information on transcript levels from fruits for a subset of the 50 RILs analyzed at the metabolic level, to select 5592 non-redundant genes that were consistently expressed in those fruit samples (see Methods).


Fine-tuning tomato agronomic properties by computational genome redesign.

Carrera J, Fernández Del Carmen A, Fernández-Muñoz R, Rambla JL, Pons C, Jaramillo A, Elena SF, Granell A - PLoS Comput. Biol. (2012)

Lean Manufacturing as a model applied in systems and synthetic biology.From omic data (transcriptomics, metabolomics and phenomics), a quantitative global model was constructed using reverse engineering methods. The predictive model was used to propose genome perturbations, to improve desired phenotypes with relevant biotechnological applications. The genome perturbations were guided by an in silico optimization that imposed the desired selective pressure.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3369923&req=5

pcbi-1002528-g001: Lean Manufacturing as a model applied in systems and synthetic biology.From omic data (transcriptomics, metabolomics and phenomics), a quantitative global model was constructed using reverse engineering methods. The predictive model was used to propose genome perturbations, to improve desired phenotypes with relevant biotechnological applications. The genome perturbations were guided by an in silico optimization that imposed the desired selective pressure.
Mentions: We have extended our recently developed inference methodology, InferGene[7], to obtain a gene regulatory model coupled to metabolism that allows us analyzing optimality in terms of specified agronomic and organoleptic properties of the tomato fruit (Figure 1). For this, we have taken advantage of an experimentally characterized subset of the metabolome of 169 tomato RILs, which includes the accumulation levels of 67 metabolites in the fruit and that contribute to the flavor (sugars, acids and some volatiles), aroma (volatiles) and other quality traits (such as color and healthy carotenoids and vitamins). Moreover, we have also used the information on transcript levels from fruits for a subset of the 50 RILs analyzed at the metabolic level, to select 5592 non-redundant genes that were consistently expressed in those fruit samples (see Methods).

Bottom Line: Considering cells as biofactories, we aimed to optimize its internal processes by using the same engineering principles that large industries are implementing nowadays: lean manufacturing.Based on predicted metabolic profiles, a close association with agronomic and organoleptic properties of the ripe fruit was revealed with high statistical confidence.Inspired in a synthetic biology approach, the model was used for exploring the landscape of all possible local transcriptional changes with the aim of engineering tomato fruits with fine-tuned biotechnological properties.

View Article: PubMed Central - PubMed

Affiliation: Instituto de Biologa Molecular y Celular de Plantas, Consejo Superior de Investigaciones Cientificas-UPV, Valencia, Spain. Javier.Carrera@synth-bio.org

ABSTRACT
Considering cells as biofactories, we aimed to optimize its internal processes by using the same engineering principles that large industries are implementing nowadays: lean manufacturing. We have applied reverse engineering computational methods to transcriptomic, metabolomic and phenomic data obtained from a collection of tomato recombinant inbreed lines to formulate a kinetic and constraint-based model that efficiently describes the cellular metabolism from expression of a minimal core of genes. Based on predicted metabolic profiles, a close association with agronomic and organoleptic properties of the ripe fruit was revealed with high statistical confidence. Inspired in a synthetic biology approach, the model was used for exploring the landscape of all possible local transcriptional changes with the aim of engineering tomato fruits with fine-tuned biotechnological properties. The method was validated by the ability of the proposed genomes, engineered for modified desired agronomic traits, to recapitulate experimental correlations between associated metabolites.

Show MeSH