Mathias John
University of Rostock
19 Papers
191 Citations
Mathias John is an academic researcher from University of Rostock. The author has contributed to research in topics: Computer science & Population. The author has an hindex of 12, co-authored 19 publications. Previous affiliations of Mathias John include university of lille.
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Papers
Modeling leucine's metabolic pathway and knockout prediction improving the production of surfactin, a biosurfactant from Bacillus subtilis
François Coutte,Joachim Niehren,Joachim Niehren,Debarun Dhali,Mathias John,Cristian Versari,Philippe Jacques +6 more
TL;DR: The effectiveness of the knockout prediction approach based on formal models for metabolic reaction networks with partial kinetic information is shown, and the hypothesis that precursors supply is one of the main parameters to optimize surfactin overproduction is confirmed.
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Combining micro and macro-modeling in DEVS for computational biology
Adelinde M. Uhrmacher,Roland Ewald,Mathias John,Carsten Maus,Matthias Jeschke,Susanne Biermann +5 more
- 09 Dec 2007
TL;DR: Multi-Level-DEVS (or m^- DEVS) supports an explicit description of macro and micro level, information at macro level can be accessed from micro level and vice versa, micro models can be synchronously activated by the macro model and also themicro models can trigger the dynamics at macrolevel.
The Attributed Pi Calculus
Mathias John,Cédric Lhoussaine,Joachim Niehren,Adelinde M. Uhrmacher +3 more
- 12 Oct 2008
TL;DR: Two examples underline the applicability of the attributed pi calculus to systems biology: Euglena's movement in phototaxis, and cooperative protein binding in gene regulation of bacteriophage lambda.
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Biochemical reaction rules with constraints
Mathias John,Cédric Lhoussaine,Joachim Niehren,Cristian Versari +3 more
- 26 Mar 2011
TL;DR: It is proved that React(C) can express the stochastic π-calculus, in contrast to previous rule-based programming languages, and further illustrate the high expressiveness ofreact(C), an expressive programming language for stochastically modeling and simulation in systems biology.
Elucidating the sources of β-catenin dynamics in human neural progenitor cells.
TL;DR: A stochastic model of the pathway in single cells from the reference model in literature is derived and extended and it is shown that the impact of the cell cycle asynchrony on wet-lab results that average over cell populations is negligible and additional evidence that self-induced Wnt signaling occurs in RVM cells is provided.