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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 Arabidopsis and Some of Its Properties

TL;DR: Using techniques based on linear programming, the construction and analysis of a genome-scale metabolic model of Arabidopsis (Arabidopsis thaliana) primarily derived from the annotations in the Aracyc database is described.
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MEMOTE for standardized genome-scale metabolic model testing

Christian Lieven, +84 more
- 01 Mar 2020 - 
TL;DR: A community effort to develop a test suite named MEMOTE (for metabolic model tests) to assess GEM quality, and advocate adoption of the latest version of the Systems Biology Markup Language level 3 flux balance constraints (SBML3FBC) package as the primary description and exchange format.
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A Diel Flux Balance Model Captures Interactions between Light and Dark Metabolism during Day-Night Cycles in C3 and Crassulacean Acid Metabolism Leaves.

TL;DR: A diel flux balance modeling framework that integrates temporally separated metabolic networks provides realistic descriptions of light and dark metabolism in C3 and CAM leaves and suggests that energetics and nitrogen use efficiency are unlikely to have been drivers for the evolution of CAM.
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Modelling photosynthesis and its control

TL;DR: The dynamic and steady-state behaviour of a computer simulation of the Calvin cycle reactions of the chloroplast, including starch synthesis and degradation, and triose phosphate export have been investigated and are shown to be broadly consistent with observations on transgenic plants.
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A genome-scale metabolic model accurately predicts fluxes in central carbon metabolism under stress conditions.

TL;DR: Under both conditions, the genome-scale model was able to predict both the direction and magnitude of the changes in flux: namely, increased TCA cycle and decreased phosphoenolpyruvate carboxylase flux at high temperature and a general decrease in fluxes under hyperosmotic stress.