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Mark G. Poolman

Researcher at Oxford Brookes University

Publications -  48
Citations -  2595

Mark G. Poolman is an academic researcher from Oxford Brookes University. The author has contributed to research in topics: Metabolic network & Light intensity. The author has an hindex of 25, co-authored 44 publications receiving 2211 citations. Previous affiliations of Mark G. Poolman include Montana State University & University of Oxford.

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A genome-scale metabolic model of Cupriavidus necator H16 integrated with TraDIS and transcriptomic data reveals metabolic insights for biotechnological applications

TL;DR: A genome-scale metabolic model of C. necator H16 is presented, which can provide useful insights for designing metabolic engineering strategies and is indicated how experimental and computational predictions can be used to improve model structure and, thus, model accuracy.
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Publisher Correction: MEMOTE for standardized genome-scale metabolic model testing

Christian Lieven, +84 more
- 19 Mar 2020 - 
TL;DR: An amendment to this paper has been published and can be accessed via a link at the top of the paper.
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Substrate Utilisation and Energy Metabolism in Non-Growing Campylobacter jejuni M1cam

TL;DR: This work integrated phenotypic microarrays and genome-scale metabolic modelling (GSM) to investigate the survival of C. jejuni on 95 substrates and identified amino acids, organic acids and H2, as single substrates supporting survival without growth.
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Genome-Scale Metabolic Modelling Approach to Understand the Metabolism of the Opportunistic Human Pathogen Staphylococcus epidermidis RP62A

TL;DR: It is shown that this strain has no real substrate auxotrophies, although removal of proline from the media has the highest impact on the model and the experimental growth characteristics, and Amino acids, proline, valine, alanine, glutamate and arginine are preferred sources of energy and biomass production.
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A Novel Algorithm to Calculate Elementary Modes: Analysis of Campylobacter jejuni Metabolism

TL;DR: In this paper , a linear programming algorithm is proposed to decompose a steady-state flux vector into a set of weighted elementary modes such that the sum of these elementary modes is equal to the original flux vector.