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


Model merging

Model standards

Sensitivity analysis


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 5 727-734

Areas of Advance

Life Science Engineering (2010-2018)

Subject Categories

Bioinformatics and Systems Biology



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