Bringing Genomes to Life:

the use of genome-scale models

High throughput (HT) data generation in biology has led to the availability of vast amounts of chemical compositional data about cells. These developments have led to the emergence of systems biology that is widely viewed as being comprised of four steps: 1) information about cellular components, 2) reconstruction of biochemical reaction networks, 3) formulation of in silico model of network functions (i.e. phenotypes) and 4) measurement of phenotypic responses and their comparison to computed properties. Disagreement leads to an iterative model building procedure. HT phenotyping is one of the limiting steps in this process.

Reconstruction of genome-scale networks for metabolism and regulation in single cellular organisms in now possible, and efforts in reconstructing networks in human cells have begun. In silico models that characterize their function can be used to analyze, interpret and predict the genotype-phenotype relationship. Reconstructed genome-scale models for E. coli and Yeast that include metabolism, regulation and transcription/translation have been formulated. These models integrate and represent a wide variety of high-throughput data.

Genome-scale models can be used to analyze the phenotypic consequences of gene deletions, optimal growth rates, the outcome of adaptive evolution, and for design of strains for bioprocessing. Examples in all these categories will be given, with emphasis on the computational and experimental analysis of adaptive evolution. Full characterization of adaptive evolutionary processes in terms of genome-wide expression profiling and full DNA re-sequencing has been performed. Thus both the genetic and epigenetic changes underlying adaptive evolution have been measured on a genome-scale and this data can be interpreted with the genome-scale in silico models.