Curator

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Prof. Dr., Ph. D. Thomas Lengauer

Phone:+49 681 9325-3000Fax:+49 681 9325-3099

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Complex Systems . Computer Science . Immunobiology . Infection Biology . Mathematics . Medicine

Computational disease models

Computational models of disease are invaluable for interpreting large swathes of patient-specific data. More sophisticated statistical, algorithmic and numerical methods are needed to analyse datasets with complex biological and clinical information. Significant progress will be made if separate models of the disease process — operating at different levels and spanning a range of timescales — can be combined.

The collection and storage of biomolecular and clinical information is more accessible and cheaper than ever. A major challenge now is to make sense of the vast volumes of data being produced, which is where complex computational models can play a vital role.

Gathering clinical data can lead to the identification of ‘molecular fingerprints’ for patients, concerning genotype, gene or protein expression, or metabolite levels. Zoom Image
Gathering clinical data can lead to the identification of ‘molecular fingerprints’ for patients, concerning genotype, gene or protein expression, or metabolite levels. [less]

Advances in technology and the development of new experimental methods have had a significant impact on the study of disease. This has led to new research directions, including: the acquisition of detailed ‘molecular fingerprints’ from patients containing information, for instance, on genotype, gene or protein expression, or metabolite levels; the study of intracellular processes in healthy and diseased tissue via the manipulation of gene activity within cells; and the construction of comprehensive disease-specific databases that combine patients’ medical history with laboratory and clinical data as well as saving relevant tissue samples1.

Computational analysis and modelling can help make sense of the data being collected. Initially, molecular fingerprints can be used to identify biomarkers that signal an elevated risk of acquiring a disease or to confirm diagnosis. Information about intracellular processes can be used to construct artificial networks of the molecular interactions involved and evaluate their role in the disease, while more complex quantitative dynamic models can track the underlying molecular processes over time. Such networks might also help to predict both the likely course of a disease and its response to treatment2.

 
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