Imperial College London


Faculty of Natural SciencesDepartment of Life Sciences

Director Centre for Bioinformatics



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BibTex format

author = {Bang, J-W and Crockford, DJ and Holmes, E and Pazos, F and Sternberg, MJE and Muggleton, SH and Nicholson, JK},
doi = {10.1021/pr070350l},
journal = {J Proteome Res},
pages = {497--503},
title = {Integrative top-down system metabolic modeling in experimental disease states via data-driven Bayesian methods.},
url = {},
volume = {7},
year = {2008}

RIS format (EndNote, RefMan)

AB - Multivariate metabolic profiles from biofluids such as urine and plasma are highly indicative of the biological fitness of complex organisms and can be captured analytically in order to derive top-down systems biology models. The application of currently available modeling approaches to human and animal metabolic pathway modeling is problematic because of multicompartmental cellular and tissue exchange of metabolites operating on many time scales. Hence, novel approaches are needed to analyze metabolic data obtained using minimally invasive sampling methods in order to reconstruct the patho-physiological modulations of metabolic interactions that are representative of whole system dynamics. Here, we show that spectroscopically derived metabolic data in experimental liver injury studies (induced by hydrazine and alpha-napthylisothiocyanate treatment) can be used to derive insightful probabilistic graphical models of metabolite dependencies, which we refer to as metabolic interactome maps. Using these, system level mechanistic information on homeostasis can be inferred, and the degree of reversibility of induced lesions can be related to variations in the metabolic network patterns. This approach has wider application in assessment of system level dysfunction in animal or human studies from noninvasive measurements.
AU - Bang,J-W
AU - Crockford,DJ
AU - Holmes,E
AU - Pazos,F
AU - Sternberg,MJE
AU - Muggleton,SH
AU - Nicholson,JK
DO - 10.1021/pr070350l
EP - 503
PY - 2008///
SN - 1535-3893
SP - 497
TI - Integrative top-down system metabolic modeling in experimental disease states via data-driven Bayesian methods.
T2 - J Proteome Res
UR -
UR -
VL - 7
ER -