In silico analysis of SNPs and other high-throughput data

Abstract

The availability and accessibility of high-throughput and biological legacy data have allowed mathematical analyses of genome-scale metabolic networks and models. Model formulation is centered on the conservation principles of mass and charge. Thermodynamic information is generally incorporated by means of reaction reversibility. If further experimental data are available, such as kinetic parameters, models describing system evolution over time can be developed. The type of data available largely determines the type of model (and subsequently the type of analysis) that can be performed. Different modeling approaches offer different advantages. Detailed kinetic models can make specific predictions about network functional states given knowledge about the enzyme parameter variations resulting from single-nucleotide polymorphisms (SNPs). They also require a large amount of experimental data, which is rarely available. On the other hand, although current formulations using the constraint-based optimization framework do not offer information about metabolite concentrations or time-dependent changes, it is a remarkably flexible modeling framework and permits the integration of a large amount of very different data types.