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Article

Metabolomic Modularity Analysis (MMA) to Quantify Human Liver Perfusion Dynamics

1
Center for Engineering in Medicine, Harvard Medical School, Massachusetts General Hospital & Shriners Hospital for Children, 51 Blossom Street, Boston, MA 02114, USA
2
Department of Control and Dynamic Systems, California Institute of Technology, Pasadena, CA 91125, USA
*
Author to whom correspondence should be addressed.
Metabolites 2017, 7(4), 58; https://doi.org/10.3390/metabo7040058
Received: 18 August 2017 / Revised: 24 October 2017 / Accepted: 8 November 2017 / Published: 13 November 2017
(This article belongs to the Special Issue Metabolomics Modelling)
Large-scale -omics data are now ubiquitously utilized to capture and interpret global responses to perturbations in biological systems, such as the impact of disease states on cells, tissues, and whole organs. Metabolomics data, in particular, are difficult to interpret for providing physiological insight because predefined biochemical pathways used for analysis are inherently biased and fail to capture more complex network interactions that span multiple canonical pathways. In this study, we introduce a nov-el approach coined Metabolomic Modularity Analysis (MMA) as a graph-based algorithm to systematically identify metabolic modules of reactions enriched with metabolites flagged to be statistically significant. A defining feature of the algorithm is its ability to determine modularity that highlights interactions between reactions mediated by the production and consumption of cofactors and other hub metabolites. As a case study, we evaluated the metabolic dynamics of discarded human livers using time-course metabolomics data and MMA to identify modules that explain the observed physiological changes leading to liver recovery during subnormothermic machine perfusion (SNMP). MMA was performed on a large scale liver-specific human metabolic network that was weighted based on metabolomics data and identified cofactor-mediated modules that would not have been discovered by traditional metabolic pathway analyses. View Full-Text
Keywords: metabolic networks; liver; cofactors; modularity metabolic networks; liver; cofactors; modularity
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MDPI and ACS Style

Sridharan, G.V.; Bruinsma, B.G.; Bale, S.S.; Swaminathan, A.; Saeidi, N.; Yarmush, M.L.; Uygun, K. Metabolomic Modularity Analysis (MMA) to Quantify Human Liver Perfusion Dynamics. Metabolites 2017, 7, 58. https://doi.org/10.3390/metabo7040058

AMA Style

Sridharan GV, Bruinsma BG, Bale SS, Swaminathan A, Saeidi N, Yarmush ML, Uygun K. Metabolomic Modularity Analysis (MMA) to Quantify Human Liver Perfusion Dynamics. Metabolites. 2017; 7(4):58. https://doi.org/10.3390/metabo7040058

Chicago/Turabian Style

Sridharan, Gautham V., Bote G. Bruinsma, Shyam S. Bale, Anandh Swaminathan, Nima Saeidi, Martin L. Yarmush, and Korkut Uygun. 2017. "Metabolomic Modularity Analysis (MMA) to Quantify Human Liver Perfusion Dynamics" Metabolites 7, no. 4: 58. https://doi.org/10.3390/metabo7040058

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