Bridging the gaps in systems biology
Journal 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.

Model standards

Model merging

Sensitivity analysis


Systems biology


Marija Cvijovic

Chalmers, Mathematical Sciences, Mathematics

University of Gothenburg

Joachim E Almqvist

Chalmers, Chemical and Biological Engineering, Life Sciences, System Biology

Jonas Hagmar

Stefan Hohmann

University of Gothenburg

H. M. Kaltenbach

Edda Klipp

Marcus Krantz

P. Mendes

S. Nelander

Jens B Nielsen

Chalmers, Chemical and Biological Engineering, Life Sciences, System Biology

A. Pagnani

N. Przulj

A. Raue

J. Stelling

S. Stoma

F. Tobin

J. A. H. Wodke

R. Zecchina

Mats Jirstrand

Molecular Genetics and Genomics

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

Vol. 289 727-734

Areas of Advance

Life Science Engineering

Subject Categories

Bioinformatics and Systems Biology