Bridging the gaps in systems biology
Artikel i vetenskaplig tidskrift, 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.

Model standards

Model merging

Sensitivity analysis

Modeling

Systems biology

Författare

Marija Cvijovic

Chalmers, Matematiska vetenskaper, Matematik

Göteborgs universitet

Joachim E Almqvist

Kemi- och bioteknik, Livsvetenskaper, Systembiologi

Jonas Hagmar

Stefan Hohmann

Göteborgs universitet

H. M. Kaltenbach

Edda Klipp

Marcus Krantz

P. Mendes

S. Nelander

Jens B Nielsen

Kemi- och bioteknik, Livsvetenskaper, Systembiologi

A. Pagnani

N. Przulj

A. Raue

J. Stelling

S. Stoma

F. Tobin

J. A. H. Wodke

R. Zecchina

Molecular Genetics and Genomics

1617-4615 (ISSN) 1617-4623 (eISSN)

Vol. 289 727-734

Styrkeområden

Livsvetenskaper och teknik

Ämneskategorier

Bioinformatik och systembiologi

DOI

10.1007/s00438-014-0843-3