Fine-tuning tomato agronomic properties by computational genome redesign.
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.
Instituto de Biologa Molecular y Celular de Plantas, Consejo Superior de Investigaciones Cientificas-UPV, Valencia, Spain. Javier.Carrera@synth-bio.org
Javier
J
Carrera
Asun
A
Fernández Del Carmen
Rafael
R
Fernández-Muñoz
Jose Luis
JL
Rambla
Clara
C
Pons
Alfonso
A
Jaramillo
Santiago F
SF
Elena
Antonio
A
Granell
D000383:Agriculture; D001709:Biotechnology; D019295:Computational Biology; D003198:Computer Simulation; D005638:Fruit / Q000235:genetics / Q000254:growth & development / Q000378:metabolism; D055786:Gene Knockout Techniques; D005818:Genetic Engineering; D018745:Genome, Plant; D016014:Linear Models; D018551:Solanum lycopersicum / Q000235:genetics* / Q000254:growth & development / Q000378:metabolism; D055442:Metabolome; D008957:Models, Genetic; D010641:Phenotype; D030821:Plants, Genetically Modified; D058615:Synthetic Biology; D059467:Transcriptome; D015854:Up-Regulation