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El desarrollo de tecnologías para sintetizar nuevos genomas e introducirlos dentro de hospedadores con sus respectivos cromosomas naturales inactivados abre las puertas a nuevos horizontes en biología sintética. Es de suma importancia aprovechar la habilidad de usar métodos computacionales para predecir y optimizar genomas sintéticos antes de llevar a cabo su síntesis. El objetivo de esta tesis es propulsar la ingeniería de genomas sintéticos de una célula procariota y otras dos eucariotas usando modelos cuantitativos a escala genómica. Así pues, he desarrollado una nueva metodología para el diseño automatizado de genomas sintéticos que se basa en una optimización que computacionalmente imita la evolución natural de genomas. Primero, he abordado el diseño de la red genómica transcripcional de Escherichia coli con adaptación a entornos cambiantes. Aplicando métodos de ingeniería reversa a los datos masivos disponibles de la transcripción y señalización para la bacteria, tratamos entender los principios de diseño que determinan la regulación de su transcriptoma. Encontramos que el genoma de E. coli puede ser rediseñado de tal forma que tenga una estructura reguladora transcripcional más simple manteniendo aún la respuesta fisiológica global ante ambientes fluctuantes. Estos genomas son más sensibles y muestran una respuesta más robusta ante ambientes agresivos. Segundo, he estudiado cómo los virus reprograman los chasis celulares de sus hospedadores asumiendo que existen mecanismos por los cuales los virus son capaces de superar con éxito las defensas expuestas por los hospedadores y modificar la expresión de sus genes en su propio beneficio. He desarrollado un nuevo modelo cuantitativo de la regulaci\'on transcripcional a nivel genómico de Arabidopsis thaliana para explorar el paisaje de posibles genomas rediseñados. Encontré un conjunto básico de genes del hospedador cuyos silenciamiento o sobre-expresión dieron pie a la predicción de perfiles de transcripción que s

[EN] Transcriptional profiling has been widely used as a tool for unveiling the coregulations of genes in response to genetic and environmental perturbations. These coregulations have been used, in a few instances, to infer global transcriptional regulatory models. Here, using the large amount of transcriptomic information available for the bacterium Escherichia coli, we seek to understand the design principles determining the regulation of its transcriptome. Combining transcriptomic and signaling data, we develop an evolutionary computational procedure that allows obtaining alternative genomic transcriptional regulatory network (GTRN) that still maintains its adaptability to dynamic environments. We apply our methodology to an E. coli GTRN and show that it could be rewired to simpler transcriptional regulatory structures. These rewired GTRNs still maintain the global physiological response to fluctuating environments. Rewired GTRNs contain 73% fewer regulated operons. Genes with similar functions and coordinated patterns of expression across environments are clustered into longer regulated operons. These synthetic GTRNs are more sensitive and show a more robust response to challenging environments. This result illustrates that the natural configuration of E. coli GTRN does not necessarily result from selection for robustness to environmental perturbations, but that evolutionary contingencies may have been important as well. We also discuss the limitations of our methodology in the context of the demand theory. Our procedure will be useful as a novel way to analyze global transcription regulation networks and in synthetic biology for the de novo design of genomes.

[EN] 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.

Understanding the mechanisms by which plants trigger host defenses in response to viruses has been a challenging problem owing to the multiplicity of factors and complexity of interactions involved. The advent of genomic techniques, however, has opened the possibility to grasp a global picture of the interaction. Here, we used Arabidopsis thaliana to identify and compare genes that are differentially regulated upon infection with seven distinct (+)ssRNA and one ssDNA plant viruses. In the first approach, we established lists of genes differentially affected by each virus and compared their involvement in biological functions and metabolic processes. We found that phylogenetically related viruses significantly alter the expression of similar genes and that viruses naturally infecting Brassicaceae display a greater overlap in the plant response. In the second approach, virus-regulated genes were contextualized using models of transcriptional and protein-protein interaction networks of A. thaliana. Our results confirm that host cells undergo significant reprogramming of their transcriptome during infection, which is possibly a central requirement for the mounting of host defenses. We uncovered a general mode of action in which perturbations preferentially affect genes that are highly connected, central and organized in modules. © 2012 Rodrigo et al.

[EN] The molecular mechanisms underlying viral pathogenesis are yet poorly understood owed to the large number of factors involved and the complexity of their interactions. Could we identify a minimal set of host transcription factors (TF) whose misregulation would result in the transcriptional profile characteristic of infected cells in absence of the virus? How many of such sets exist? Are all orthogonal or share critical TFs involved in specific biological functions? We have developed a computational methodology that uses a quantitative model of the transcriptional regulatory network (TRN) of Arabidopsis thaliana to explore the landscape of all possible re-engineered TRNs whose transcriptomic profiles mimic those observed in infected plants. We found core sets containing between six and 34 TFs, depending on the virus, whose in silico knockout or overexpression in the TRN resulted in transcriptional profiles that minimally deviate from those observed in infected plants.