Software that goes with the flow in systems biology
1 Control and Dynamical Systems, California Institute of Technology, Pasadena, CA 91125, USA
2 EMBL-European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
BMC Biology 2010, 8:140 doi:10.1186/1741-7007-8-140Published: 29 November 2010
A recent article in BMC Bioinformatics describes new advances in workflow systems for computational modeling in systems biology. Such systems can accelerate, and improve the consistency of, modeling through automation not only at the simulation and results-production stages, but also at the model-generation stage. Their work is a harbinger of the next generation of more powerful software for systems biologists.
See research article: http://www.biomedcentral.com/1471-2105/11/582/abstract/ webcite
Ever since the rise of systems biology at the end of the last century, mathematical representations of biological systems and their activities have flourished. They are being used to describe everything from biomolecular networks, such as gene regulation, metabolic processes and signaling pathways, at the lowest biological scales, to tissue growth and differentiation, drug effects, environmental interactions, and more. A very active area in the field has been the development of techniques that facilitate the construction, analysis and dissemination of computational models. The heterogeneous, distributed nature of most data resources today has increased not only the opportunities for, but also the difficulties of, developing software systems to support these tasks. The work by Li et al.  published in BMC Bioinformatics represents a promising evolutionary step forward in this area. They describe a workflow system - a visual software environment enabling a user to create a connected set of operations to be performed sequentially using seperate tools and resources. Their system uses third-party data resources accessible over the Internet to elaborate and parametrize (that is, assign parameter values to) computational models in a semi-automated manner. In Li et al.'s work, the authors point towards a promising future for computational modeling and simultaneously highlight some of the difficulties that need to be overcome before we get there.