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Titlebook: Computational Methods in Systems Biology; 20th International C Ion Petre,Andrei P?un Conference proceedings 2022 The Editor(s) (if applicab

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21#
發(fā)表于 2025-3-25 03:57:23 | 只看該作者
22#
發(fā)表于 2025-3-25 11:21:21 | 只看該作者
23#
發(fā)表于 2025-3-25 12:19:20 | 只看該作者
Implementing Microsoft Dynamicsraction. These provide a very faithful approximation of concrete runs on the desired level of granularity, at a low cost. Our experiments demonstrate the speedups in the simulations while preserving key dynamical as well as quantitative properties.
24#
發(fā)表于 2025-3-25 16:18:02 | 只看該作者
25#
發(fā)表于 2025-3-25 22:30:43 | 只看該作者
The SAP S/4HANA Project: How It Should Be,eter space describing physiologically relevant levels of crowding by means of Gaussian Process regression. We demonstrate the method on an enzyme-catalyzed reaction and a genetic feedback loop, showing good agreement between the time-dependent distributions of molecule numbers predicted by the effective CME and CA simulations.
26#
發(fā)表于 2025-3-26 01:02:16 | 只看該作者
27#
發(fā)表于 2025-3-26 04:29:58 | 只看該作者
28#
發(fā)表于 2025-3-26 12:07:37 | 只看該作者
Bayesian Learning of?Effective Chemical Master Equations in?Crowded Intracellular Conditionseter space describing physiologically relevant levels of crowding by means of Gaussian Process regression. We demonstrate the method on an enzyme-catalyzed reaction and a genetic feedback loop, showing good agreement between the time-dependent distributions of molecule numbers predicted by the effective CME and CA simulations.
29#
發(fā)表于 2025-3-26 13:37:46 | 只看該作者
Conference proceedings 2022deling, simulation, analysis, design and control of biological systems. The papers are arranged thematically as follows: Chemical reaction networks; Boolean networks; continuous and hybrid models; machine learning; software..
30#
發(fā)表于 2025-3-26 17:12:44 | 只看該作者
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