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Metabolomic Modularity Analysis (MMA) to Quantify Human Liver Perfusion Dynamics.
Sridharan, Gautham Vivek; Bruinsma, Bote Gosse; Bale, Shyam Sundhar; Swaminathan, Anandh; Saeidi, Nima; Yarmush, Martin L; Uygun, Korkut.
Afiliación
  • Sridharan GV; Center for Engineering in Medicine, Harvard Medical School, Massachusetts General Hospital & Shriners Hospital for Children, 51 Blossom Street, Boston, MA 02114, USA. gvsridharan@gmail.com.
  • Bruinsma BG; Center for Engineering in Medicine, Harvard Medical School, Massachusetts General Hospital & Shriners Hospital for Children, 51 Blossom Street, Boston, MA 02114, USA. botebruinsma@gmail.com.
  • Bale SS; Center for Engineering in Medicine, Harvard Medical School, Massachusetts General Hospital & Shriners Hospital for Children, 51 Blossom Street, Boston, MA 02114, USA. shyam.bale@gmail.com.
  • Swaminathan A; Department of Control and Dynamic Systems, California Institute of Technology, Pasadena, CA 91125, USA. aswamina@caltech.edu.
  • Saeidi N; Center for Engineering in Medicine, Harvard Medical School, Massachusetts General Hospital & Shriners Hospital for Children, 51 Blossom Street, Boston, MA 02114, USA. Nsaeidi@mgh.harvard.edu.
  • Yarmush ML; Center for Engineering in Medicine, Harvard Medical School, Massachusetts General Hospital & Shriners Hospital for Children, 51 Blossom Street, Boston, MA 02114, USA. ireis@sbi.org.
  • Uygun K; Center for Engineering in Medicine, Harvard Medical School, Massachusetts General Hospital & Shriners Hospital for Children, 51 Blossom Street, Boston, MA 02114, USA. Uygun.Korkut@mgh.harvard.edu.
Metabolites ; 7(4)2017 11 13.
Article en En | MEDLINE | ID: mdl-29137180
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Metabolites Año: 2017 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Suiza
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Metabolites Año: 2017 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Suiza