Bridging the gaps in systems biology
Review article, 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
Author
Marija Cvijovic
University of Gothenburg
Chalmers, Mathematical Sciences, Mathematics
Joachim Almquist
Chalmers, Chemical and Biological Engineering, Life Sciences
Jonas Hagmar
Fraunhofer-Chalmers Centre
Stefan Hohmann
University of Gothenburg
H. M. Kaltenbach
Swiss Federal Institute of Technology in Zürich (ETH)
Edda Klipp
Humboldt University of Berlin
Marcus Krantz
Humboldt University of Berlin
P. Mendes
University of Manchester
S. Nelander
Uppsala University
Jens B Nielsen
Chalmers, Chemical and Biological Engineering, Life Sciences
A. Pagnani
Polytechnic University of Turin
N. Przulj
Imperial College London
A. Raue
University of Freiburg
J. Stelling
Swiss Federal Institute of Technology in Zürich (ETH)
S. Stoma
Institut National de Recherche en Informatique et en Automatique (INRIA)
F. Tobin
Tobin Consulting LLC
J. A. H. Wodke
Humboldt University of Berlin
R. Zecchina
Polytechnic University of Turin
Mats Jirstrand
Fraunhofer-Chalmers Centre
Molecular Genetics and Genomics
1617-4615 (ISSN) 1617-4623 (eISSN)
Vol. 289 5 727-734Areas of Advance
Life Science Engineering (2010-2018)
Subject Categories
Bioinformatics and Systems Biology
DOI
10.1007/s00438-014-0843-3