Monte Carlo sampling and principal component analysis of flux distributions yield topological and modular information on metabolic networks

Sarıyar, Berna; Perk, Sinem; Akman, Uğur; Hortaçsu, Amable

doi:10.1016/j.jtbi.2006.03.007

Cited by 25 publications

(15 citation statements)

References 25 publications

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“…This study demonstrates the power of SPM methodologies in achieving this goal; however, further work is needed to reach an index of transplantability. The data gathered here can also be used for more sophisticated metabolic analyses which reveal more details of the cellular function [46], [47], [48].…”

Section: Discussionmentioning

confidence: 99%

A Metabolic Index of Ischemic Injury for Perfusion-Recovery of Cadaveric Rat Livers

et al. 2011

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With over 110,000 patients waiting for organ transplantation, the current crisis in organ transplantation is based on a lack of donors after brain-death (DBD). A very large alternative pool of donor organs that remain untapped are the donors after cardiac death (DCD), recovered after cardiac activity has ceased and therefore sustained some ischemic injury. Machine perfusion has been proposed as a novel modality of organ preservation and treatment to render such cadaveric organs, and in particular livers, transplantable. Two key issues that remain unaddressed are how to assess whether a DCD liver is damaged beyond repair, and whether machine perfusion has rendered an injured organ sufficiently viable for transplantation. In this work, we present a metabolic analysis of the transient responses of cadaveric rat livers during normothermic machine perfusion (NMP), and develop an index of ischemia that enables evaluation of the organ ischemic injury level. Further, we perform a discriminant analysis to construct a classification algorithm with >0.98 specificity to identify whether a given perfused liver is ischemic or fresh, in effect a precursor for an index of transplantability and a basis for the use of statistical process control measures for automated feedback control of treatment of ischemic injury in DCD livers. The analyses yield an index based on squared prediction error (SPE) as log(SPE) >1.35 indicating ischemia. The differences between metabolic functions of fresh and ischemic livers during perfusion are outlined and the metabolites that varied significantly for ischemic livers are identified as ornithine, arginine, albumin and tyrosine.

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Section: Discussionmentioning

confidence: 99%

A Metabolic Index of Ischemic Injury for Perfusion-Recovery of Cadaveric Rat Livers

et al. 2011

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“…keeping the current uncertainty) [13,[47][48][49][50][51]. This way, the available experimental data (measured fluxes) and the first principles knowledge captured by the model (stoichiometry) are coupled together, providing a new richer dataset amenable to further analysis with a multivariate statistical method.…”

Section: Monte Carlo Sampling Methodsmentioning

confidence: 99%

MCR-ALS on metabolic networks: Obtaining more meaningful pathways

Folch-Fortuny

Tortajada

Prats-Montalbán

et al. 2015

Chemometrics and Intelligent Laboratory Systems

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With the aim of understanding the flux distributions across a metabolic network, i.e. within living cells, Principal Component Analysis (PCA) has been proposed to obtain a set of orthogonal components (pathways) capturing most of the variance in the flux data. The problems with this method are (i) that no additional information can be included in the model, and (ii) that orthogonality imposes a hard constraint, not always reasonably. To overcome these drawbacks, here we propose to use a more flexible approach such as Multivariate Curve Resolution -Alternating Least Squares (MCR-ALS) to obtain this set of biological pathways through the network. By using this method, different constraints can be included in the model, and the same source of variability can be present in different pathways, which is reasonable from a biological standpoint. This work follows a methodology developed for Pichia pastoris cultures grown on different carbon sources, lately presented in [González Martínez et al. 2014].In this paper a different grey modelling approach, which aims to incorporate a priori knowledge through constraints on the modelling algorithms, is applied to the same case of study. The results of both models are compared to show their strengths and weaknesses.

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“…PCA is the initial stage of extensive biological studies [21][22][23][24]. The basic aim of the PCA is to explain the total system variability by a small number k of the principal components while p components explain the total variability [25].…”

Section: Principal Component Analysis (Pca)mentioning

confidence: 99%