Bridging the gaps in systems biology |
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Authors: | Marija Cvijovic Joachim Almquist Jonas Hagmar Stefan Hohmann Hans-Michael Kaltenbach Edda Klipp Marcus Krantz Pedro Mendes Sven Nelander Jens Nielsen Andrea Pagnani Natasa Przulj Andreas Raue Jörg Stelling Szymon Stoma Frank Tobin Judith A H Wodke Riccardo Zecchina Mats Jirstrand |
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Institution: | 1. Department of Mathematical Sciences, Chalmers University of Technology and University of Gothenburg, Chalmers Tv?rgata 3, 412 96, G?teborg, Sweden 2. Fraunhofer-Chalmers Centre, G?teborg, Sweden 3. University of Gothenburg, G?teborg, Sweden 4. ETH, Zürich, Switzerland 5. Humboldt University, Berlin, Germany 6. Manchester University, Manchester, UK 7. Uppsala Univeristy, Uppsala, Sweden 8. Chalmers University of Technology, G?teborg, Sweden 9. Politecnico di Torino, Turin, Italy 10. Imperial College London, London, UK 11. University of Freiburg, Freiburg im Breisgau, Germany 12. INRIA, Paris, France 13. Tobin Consulting LLC, New Jersey, USA
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Abstract: | Systems biology aims at creating mathematical models, i.e., computational reconstructions of biological systems and processes that will result in a new level of understanding—the elucidation of the basic and presumably conserved “design” and “engineering” principles of biomolecular systems. Thus, systems biology will move biology from a phenomenological to a predictive science. Mathematical modeling of biological networks and processes has already greatly improved our understanding of many cellular processes. However, given the massive amount of qualitative and quantitative data currently produced and number of burning questions in health care and biotechnology needed to be solved is still in its early phases. The field requires novel approaches for abstraction, for modeling bioprocesses that follow different biochemical and biophysical rules, and for combining different modules into larger models that still allow realistic simulation with the computational power available today. We have identified and discussed currently most prominent problems in systems biology: (1) how to bridge different scales of modeling abstraction, (2) how to bridge the gap between topological and mechanistic modeling, and (3) how to bridge the wet and dry laboratory gap. The future success of systems biology largely depends on bridging the recognized gaps. |
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