%0 Journal Article
%T Computational models in systems biology
%A Laurence Loewe
%A Jane Hillston
%J Genome Biology
%D 2008
%I BioMed Central
%R 10.1186/gb-2008-9-12-328
%X One of the chief goals of systems biology is to build mechanistic mathematical models of biological systems to further the understanding of biological detail. Such models often aim at predicting the outcome of potentially interesting biological experiments, and if such predictions are confirmed by wet-lab observations, an important step forward is made. How exactly such models are constructed and how predictions are computed were at the core of a recent conference on Computational Methods in Systems Biology that brought 80 participants to Rostock, Germany (for conference proceedings see volume 5307 of Lecture Notes in Bioinformatics http://dx.doi.org/10.1007/978-3-540-88562-7 webcite).A simplistic approach to model construction might be to capture everything that is known about a system and simulate it in supercomputers. While this is appropriate for some systems, it is impossible or highly impracticable for many others. This is mostly due to the complexity of biological systems, which demand simplification to make them amenable to modeling. Such simplifications have to capture the essence of the processes of interest, while neglecting as many of the less important details as possible. Thus, one can consider model building in systems biology as the art of building caricatures of life: capture the essence, ignore the rest.Two formalisms called process algebras and Petri nets offer alternative ways of constructing computational systems biology models. Both are concerned with how to specify (mostly quantitative) models of molecular reaction networks in an abstract way that is independent of particular mathematical techniques of analysis. Access to techniques such as differential equations or stochastic simulations is then facilitated automatically by software tools that translate the abstract model into a concrete model that is ready for computation. The advantage of this approach is that one needs to describe the model only once in order to access a variety of analyti
%U http://genomebiology.com/2008/9/12/328