Bridging the gaps in systems biology
Reviewartikel, 2014
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.
Systems biology
Modeling
Model merging
Model standards
Sensitivity analysis
Författare
Marija Cvijovic
Göteborgs universitet
Chalmers, Matematiska vetenskaper, Matematik
Joachim Almquist
Chalmers, Kemi- och bioteknik, Livsvetenskaper
Jonas Hagmar
Stiftelsen Fraunhofer-Chalmers Centrum för Industrimatematik
Stefan Hohmann
Göteborgs universitet
H. M. Kaltenbach
Eidgenössische Technische Hochschule Zürich (ETH)
Edda Klipp
Humboldt-Universität zu Berlin
Marcus Krantz
Humboldt-Universität zu Berlin
P. Mendes
University of Manchester
S. Nelander
Uppsala universitet
Jens B Nielsen
Chalmers, Kemi- och bioteknik, Livsvetenskaper
A. Pagnani
Politecnico di Torino
N. Przulj
Imperial College London
A. Raue
Albert-Ludwigs-Universität Freiburg
J. Stelling
Eidgenössische Technische Hochschule Zürich (ETH)
S. Stoma
Institut National de Recherche en Informatique et en Automatique (INRIA)
F. Tobin
Tobin Consulting LLC
J. A. H. Wodke
Humboldt-Universität zu Berlin
R. Zecchina
Politecnico di Torino
Mats Jirstrand
Stiftelsen Fraunhofer-Chalmers Centrum för Industrimatematik
Molecular Genetics and Genomics
1617-4615 (ISSN) 1617-4623 (eISSN)
Vol. 289 5 727-734Styrkeområden
Livsvetenskaper och teknik (2010-2018)
Ämneskategorier
Bioinformatik och systembiologi
DOI
10.1007/s00438-014-0843-3