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A Computational Systems Biology Approach to Reveal the Molecular Basis of Complex Diseases

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Modelling molecular networks

European researchers developed novel biology network models as a means of identifying pathways implicated in human disease. Long-term, this is expected to open up new avenues for therapy.

Health

Complex diseases such as cancer and Parkinson's disease are multifactorial with strong genetic and environmental influences. Despite significant efforts to elucidate their pathophysiology, our understanding remains limited and most complex diseases are incurable. Technological advances in research enable the high-throughput analysis of the genomic and transcriptomic profile of cells, providing an unprecedented plethora of information on multiple levels. Individually, each of these analyses portrays only part of the whole picture, thereby requiring an integrative approach. However, combining this information into a meaningful output presents a challenge. The scope of the EU-funded 'A computational systems biology approach to reveal the molecular basis of complex diseases' (EYLCOMPDISSYSBIO) project was to develop a framework that could perform such integrative analysis of different omics data in complex diseases. In this context, researchers adapted the previously developed ResponseNet framework for the analysis of human data. In its modified form, ResponseNet could be used to identify signalling and regulatory pathways as well as protein–protein interactions. Researchers applied it in melanoma to unveil pathways associated with specific mutations and to identify the connecting events between two key anti-inflammatory proteins, alpha-1-antitripsin and IL-1 receptor antagonist. Using tissue expression profiles, the consortium also constructed protein interaction networks for 16 different tissues. Comparative analysis of these networks highlighted certain mechanisms implicated in hereditary diseases, underscoring the predictive capacity of the system. A computational approach offers the advantage of being able to process and combine large sets of data from different omic technologies. This will undoubtedly prove useful for elucidating the mechanisms underlying complex diseases and identifying potential therapeutic targets.

Keywords

Molecular networks, biology network models, complex diseases, computational systems biology, molecular basis, regulatory pathways, protein–protein interactions, melanoma, protein interaction networks, omic technologies

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