Increased use of computers to create predictive models of human disease is likely following a European Science Foundation (ESF) workshop that urged collaboration between field specialists.
Human disease research operates at different levels and scales that current computer modelling approaches often fail to properly exploit, although there are some exceptions to this rule, for example with multiple sclerosis.
A major aim of the ESF workshop was to create a more coherent body of expertise across the whole field of computational disease analysis.
Albert Compte, from the Institut d'Investigacions Biomediques August Pi Sunyer (IDIBAPS) in Barcelona and co-convenor of the ESF workshop, said: 'So far, novel modelling approaches have been confined to a specific disease or a particular level of description.' For example, a model might be confined just to the molecular level or the cellular level.
The ESF workshop highlighted the benefits that could be obtained from integrating data from different levels.
This can provide more detailed and flexible models, with greater power to identify causes of diseases and predict possible cures in future.
One potential problem when building sophisticated disease models operating at different levels is that they can become too complex, with a lack of sufficient data for useful analysis.
This can be resolved by selecting a simpler model that corresponds only to the experimental data that actually exists.
Delegates at the workshop heard how for MS, selection of the model could be tuned to the data to make best use of the experimental results obtained in a study.
Jesper Tegner, from the Atherosclerosis Research Unit at the Karolinska Institute Centre for Molecular Medicine (CMM) in Stockholm, Sweden, and another co-convenor of the ESF workshop, said: 'The immune system is clearly central for MS.
'However, the trick in the case of MS is to represent different aspects of the immune system according to the available data.
'The objective isn't to model the whole immune system.
'One interesting level of abstraction was the presentation of agent-based modelling of MS where individual cells operated as agents, thus omitting the intracellular machinery.' In other words, the detailed interior workings of the cells could be ignored because that would have made the model overcomplicated, with insufficient data at the different levels to produce any useful insights.
In other experiments, data about varying levels of gene expression was obtained, which required different models with networks of graphs.
These highlighted the patterns of gene expression associated with a particular disease, such as MS.
Yet another valuable application of computer-based mathematical disease models is studying addiction to drugs such as nicotine.
Compte added: 'There are two contending views on how neurons and their connections in subcortical nuclei are affected by nicotine.
'This computer model allows us to reconcile the apparently contradictory results obtained from in vitro and in vivo experiments and thus provides a single theoretical proposal of how nicotine affects neuronal circuits in the brain and causes addiction.' Tegner and others at the workshop were confident a coherent framework for building multi-level mathematical models will help understanding of diseases and conditions such as drug addiction.
