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Johann M. Rohwer

Researcher at Stellenbosch University

Publications -  85
Citations -  3157

Johann M. Rohwer is an academic researcher from Stellenbosch University. The author has contributed to research in topics: Flux (metabolism) & Python (programming language). The author has an hindex of 29, co-authored 80 publications receiving 2822 citations. Previous affiliations of Johann M. Rohwer include Humboldt University of Berlin & Max Planck Society.

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Ribosome and transcript copy numbers, polysome occupancy and enzyme dynamics in Arabidopsis

TL;DR: Quantitative molecular information about the numbers of ribosomes, of transcripts for 35 enzymes in central metabolism and their loading into polysomes is used to estimate translation rates in Arabidopsis rosettes, and the consequences for important sub‐processes in plant growth are explored.
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Analysis of sucrose accumulation in the sugar cane culm on the basis of in vitro kinetic data

TL;DR: In view of the modelling results, overexpression of the fructose or glucose transporter or the vacuolar sucrose import protein, as well as reduction of cytosolic neutral invertase levels, appear to be the most promising targets for genetic manipulation.
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Modelling cellular systems with PySCeS

TL;DR: The Python Simulator for Cellular Systems (PySCeS) is an extendable research tool for the numerical analysis and investigation of cellular systems.
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SBML Level 3: an extensible format for the exchange and reuse of biological models

Sarah M. Keating, +146 more
TL;DR: The latest edition of the Systems Biology Markup Language (SBML) is reviewed, a format designed for this purpose that leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models.
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Network Analysis of Enzyme Activities and Metabolite Levels and Their Relationship to Biomass in a Large Panel of Arabidopsis Accessions

TL;DR: This work profile maximum catalytic activities of 37 enzymes from central metabolism and generate a matrix to investigate species-wide connectivity between metabolites, enzymes, and biomass and shows that biomass can be predicted by two independent integrative metabolic biomarkers: preferential investment in photosynthetic machinery and optimization of carbon use.