In April 2016 Manchester eScholar was replaced by the University of Manchester’s new Research Information Management System, Pure. In the autumn the University’s research outputs will be available to search and browse via a new Research Portal. Until then the University’s full publication record can be accessed via a temporary portal and the old eScholar content is available to search and browse via this archive.

Modelling and simulation for metabolomics data analysis.

Mendes, P; Camacho, D; de la Fuente, A

Biochemical Society transactions. 2005;33(Pt 6):1427-9.

Access to files

Full-text and supplementary files are not available from Manchester eScholar. Full-text is available externally using the following links:

Full-text held externally

Abstract

The advent of large data sets, such as those produced in metabolomics, presents a considerable challenge in terms of their interpretation. Several mathematical and statistical methods have been proposed to analyse these data, and new ones continue to appear. However, these methods often disagree in their analyses, and their results are hard to interpret. A major contributing factor for the difficulties in interpreting these data lies in the data analysis methods themselves, which have not been thoroughly studied under controlled conditions. We have been producing synthetic data sets by simulation of realistic biochemical network models with the purpose of comparing data analysis methods. Because we have full knowledge of the underlying 'biochemistry' of these models, we are better able to judge how well the analyses reflect true knowledge about the system. Another advantage is that the level of noise in these data is under our control and this allows for studying how the inferences are degraded by noise. Using such a framework, we have studied the extent to which correlation analysis of metabolomics data sets is capable of recovering features of the biochemical system. We were able to identify four major metabolic regulatory configurations that result in strong metabolite correlations. This example demonstrates the utility of biochemical simulation in the analysis of metabolomics data.

Bibliographic metadata

Content type:
Published date:
Language:
eng
Abbreviated journal title:
ISSN:
Place of publication:
England
Volume:
33
Issue:
Pt 6
Pagination:
1427-9
Digital Object Identifier:
10.1042/BST20051427
Pubmed Identifier:
16246137
Pii Identifier:
BST20051427
Access state:
Active

Institutional metadata

University researcher(s):

Record metadata

Manchester eScholar ID:
uk-ac-man-scw:67055
Created by:
Pedrosa Mendes, Pedro
Created:
21st October, 2009, 11:33:04
Last modified by:
Pedrosa Mendes, Pedro
Last modified:
21st October, 2009, 11:33:04

Can we help?

The library chat service will be available from 11am-3pm Monday to Friday (excluding Bank Holidays). You can also email your enquiry to us.