Commentary
Computational disease modeling – fact or fiction?
-
* Corresponding authors: Jesper N Tegnér jesper.tegner@ki.se - Albert Compte acompte@clinic.ub.es
- † Equal contributors
1 Computational Medicine group, Department of Medicine, Center for Molecular Medicine, Karolinska University Hospital, Solna, Stockholm, Sweden
2 Institut d'Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Villarroel 170, 08036 Barcelona, Spain
3 Functional Genomics and Systems Biology for Health, LGN-UMR 7091, CNRS and Pierre & Marie Curie University of Paris VI, 7, rue Guy Moquet – BP8 – 94801 Villejuif cedex, France
4 Division of Trauma/Critical Care, Department of Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
5 Department of clinical and experimental medicine, Cell biology and diabetes research centre, Linköping University, Linköping, SE58185, Sweden
6 Center for Inflammation and Regenerative Modeling and CRISMA laboratory, Department of Critical Care Medicine, University of Pittsburgh, 3550 Terrace, Pittsburgh, PA 15261, USA
7 Group for Neural Theory, DEC-ENS, 3 rue d'Ulm, 75005 Paris, France
8 Departments of Pathology and Computational Biology, University of Pittsburgh, 3550 Terrace St., Pittsburgh, PA 15261, USA
9 Laboratory for Social and Neural Systems Research, Institute for Empirical Research in Economics, University of Zurich, Zurich, Switzerland
10 Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, UK
11 CNRS FRE 3190 IBISC, Evry, France; and University Evry-Val d'Essonne, Evry, France
12 Institut d'Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Hospital Clinic. Villarroel 170, 08036 Barcelona, Spain
BMC Systems Biology 2009, 3:56 doi:10.1186/1752-0509-3-56
Published: 4 June 2009Abstract
Background
Biomedical research is changing due to the rapid accumulation of experimental data at an unprecedented scale, revealing increasing degrees of complexity of biological processes. Life Sciences are facing a transition from a descriptive to a mechanistic approach that reveals principles of cells, cellular networks, organs, and their interactions across several spatial and temporal scales. There are two conceptual traditions in biological computational-modeling. The bottom-up approach emphasizes complex intracellular molecular models and is well represented within the systems biology community. On the other hand, the physics-inspired top-down modeling strategy identifies and selects features of (presumably) essential relevance to the phenomena of interest and combines available data in models of modest complexity.
Results
The workshop, "ESF Exploratory Workshop on Computational disease Modeling", examined the challenges that computational modeling faces in contributing to the understanding and treatment of complex multi-factorial diseases. Participants at the meeting agreed on two general conclusions. First, we identified the critical importance of developing analytical tools for dealing with model and parameter uncertainty. Second, the development of predictive hierarchical models spanning several scales beyond intracellular molecular networks was identified as a major objective. This contrasts with the current focus within the systems biology community on complex molecular modeling.
Conclusion
During the workshop it became obvious that diverse scientific modeling cultures (from computational neuroscience, theory, data-driven machine-learning approaches, agent-based modeling, network modeling and stochastic-molecular simulations) would benefit from intense cross-talk on shared theoretical issues in order to make progress on clinically relevant problems.