Metabolism and Ageing Network

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Current Participants: Sudharshan RaviLakshmi Narayanan

Continued advances in high-throughput omics technology have produced a tsunami of biological data, fueling the new era of personalized medicine. Presently, we face the challenge of extracting from such data, key biological insights for the understanding of cellular and organismal phenotypes and importantly, their alterations in different contexts such as diseases and ageing. On its own, each omics dataset (e.g., genomics, epigenomics, transcriptomics, proteomics, metabolomics, etc.) gives only a partial view of the state of the cell or tissue or organism. Only through the appropriate integration of these datasets can we gain a complete systems-wide and mechanistic understanding of the processes that give rise to observed phenotypes.

In this project, we are tackling the challenge of omics data integration specifically in the context of metabolic diseases. We are developing tools for the creation of gene-protein-phenotype networks from omics databases and metabolic network analysis of genome scale metabolic models. From such network-based data integration, we aim to provide (1) mechanistic explanations, such as alterations in metabolic pathways, underlying different classes of metabolic diseases; (2) prediction of phenotypes associated with perturbations to metabolic pathways; and possibly (3) new mechanism-based taxonomy of metabolic diseases.

Meanwhile, metabolic syndrome (MetS) is a multiplex risk factor for major chronic diseases such as diabetes and cardiovascular disease. With modernization and changes in dietary intake, MetS has become more prevalent among adults over the world. Among other factors, ageing is known to be an important contributor to MetS. In a related project, we are integrating omics data related to ageing and longevity, again through the analysis of genome scale metabolic models, for the construction of gene-protein-phenotype network. Here, our goals are to gain insights into age-related changes in metabolism in higher organisms, including human; (2) to map metabolic alterations due to ageing to age-related syndrome and diseases, and (3) to identify potential genes related to ageing and longevity in human from model organisms, such as C. elegans, mouse, and fruit-fly.

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Thu Jun 22 04:32:45 CEST 2017
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