title: Towards the automated engineering of a synthetic genome.
abstract: The development of the technology to synthesize new genomes and to introduce them into hosts with inactivated wild-type chromosome opens the door to new horizons in synthetic biology. Here it is of outmost importance to harness the ability of using computational design to predict and optimize a synthetic genome before attempting its synthesis. The methodology to computationally design a genome is based on an optimization that computationally mimics genome evolution. The biggest bottleneck lies on the use of an appropriate fitness function. This fitness function, usually cell growth, relies on the ability to quantitatively model the biochemical networks of the cell at the genome scale using parameters inferred from high-throughput data. Computational methods integrating such models in a common multilayer design platform can be used to automatically engineer synthetic genomes under physiological specifications. We describe the current state-of-the-art on automated methods for engineering or re-engineering synthetic genomes. We restrict ourselves to global models of metabolism, transcription and DNA structure. Although we are still far from the de novo computational genome design, it is important to collect all relevant work towards this goal. Finally, we discuss future perspectives about the practicability of an automated methodology for such computational design of synthetic genomes.
authors: Instituto de Biología Molecular y Celular de Plantas, Consejo Superior de Investigaciones Científicas-UPV, 46022 València, Spain.
authors: Javier
authors: J
authors: Carrera
authors: Guillermo
authors: G
authors: Rodrigo
authors: Alfonso
authors: A
authors: Jaramillo
mesh_terms: D001331:Automation / Q000379:methods*; D003198:Computer Simulation; D019143:Evolution, Molecular; D053263:Gene Regulatory Networks; D005813:Genes, Synthetic; D005818:Genetic Engineering / Q000379:methods* / Q000639:trends; D016678:Genome; D053858:Metabolic Networks and Pathways; D008954:Models, Biological