Next Article in Journal
Cell-Type Specific Metabolic Flux Analysis: A Challenge for Metabolic Phenotyping and a Potential Solution in Plants
Next Article in Special Issue
Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling
Previous Article in Journal
Metabolic Profile of the Cellulolytic Industrial Actinomycete Thermobifida fusca
Previous Article in Special Issue
Effects of Storage Time on Glycolysis in Donated Human Blood Units
Article Menu
Issue 4 (December) cover image

Export Article

Open AccessArticle
Metabolites 2017, 7(4), 58; https://doi.org/10.3390/metabo7040058

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.
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)
View Full-Text   |   Download PDF [1952 KB, uploaded 14 November 2017]   |  

Abstract

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
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Supplementary material

Share & Cite This Article

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Metabolites EISSN 2218-1989 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top