Computational disease modeling – fact or fiction?

Abstract

BackgroundBiomedical 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.ResultsThe 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.ConclusionDuring 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.

Simulations of symptomatic treatments for Alzheimer's disease: computational analysis of pathology and mechanisms of drug action

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“…These results suggest that computational-based modeling [9] could become a critical tool for improving pharmaceutical research and development, especially for complex diseases [1]. …”

Developing predictive molecular maps of human disease through community-based modeling

“…Herein lies an opportunity to transform our understanding of the molecular underpinnings of disease and develop modeling frameworks that can describe complex systems and predict their behavior. At one level, a simple pairwise analysis of alterations in human diseases may be useful for providing lists of altered components, but to uncover the essential mechanistic relationships between molecular changes and disease, more integrative modeling methods that combine multiple complex molecular traits with phenotypic outcomes will be required 2–5 . It is probable that the particular approach used will be linked to the question being addressed, such that problems of classification—for example, for disease outcome or drug response—may require different models from those used for questions directed at understanding mechanisms and predicting therapeutic intervention points.…”

The road from systems biology to systems medicine

“…In pediatric research, it also entails normal and abnormal developmental processes (5). Understanding such diseases requires not only knowledge of cells and subcellular processes but also a conceptual integration of data and models across multiple levels of structural and functional organization (6,7). This in turn requires an integration of multiple kinds of expertise and data types from molecular and cell biology, physiology, clinical medicine, and epidemiology.…”