Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 534740
Title From correlation to causation : analysis of metabolomics data using systems biology approaches
Author(s) Rosato, Antonio; Tenori, Leonardo; Cascante, Marta; Atauri Carulla, Pedro Ramon De; Martins dos Santos, Vitor A.P.; Saccenti, Edoardo
Source Metabolomics 14 (2018)4. - ISSN 1573-3882
DOI http://dx.doi.org/10.1007/s11306-018-1335-y
Department(s) Systems and Synthetic Biology
VLAG
Publication type Refereed Article in a scientific journal
Publication year 2018
Keyword(s) Association network - Correlation network - Enrichment analysis - Network analysis - Pathway
Abstract Introduction: Metabolomics is a well-established tool in systems biology, especially in the top–down approach. Metabolomics experiments often results in discovery studies that provide intriguing biological hypotheses but rarely offer mechanistic explanation of such findings. In this light, the interpretation of metabolomics data can be boosted by deploying systems biology approaches. Objectives: This review aims to provide an overview of systems biology approaches that are relevant to metabolomics and to discuss some successful applications of these methods. Methods: We review the most recent applications of systems biology tools in the field of metabolomics, such as network inference and analysis, metabolic modelling and pathways analysis. Results: We offer an ample overview of systems biology tools that can be applied to address metabolomics problems. The characteristics and application results of these tools are discussed also in a comparative manner. Conclusions: Systems biology-enhanced analysis of metabolomics data can provide insights into the molecular mechanisms originating the observed metabolic profiles and enhance the scientific impact of metabolomics studies.
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