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Poster: Biochemical signaling: A discrete simulation with memory enhancement

机译:海报:生化信号:具有记忆增强功能的离散模拟

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We will describe the memory enhancement and give a simple three reaction model to illustrate the differences between our technique and a continuous, concentration-based approach using a system of ordinary differential equations. Furthermore, we provide our results from the modeling of two well-known models: the Lotka-Volterra predator-prey and a circadian rhythm model. For these models, we provide the results of our simulation technique in comparison to results from ordinary differential equations and the Gillespie Algorithm. We show that our algorithm, while being faster than Gillespie's approach, is capable of generating oscillatory behavior where ordinary differential equations do not.
机译:我们将描述内存增强,并给出一个简单的三个反应模型来说明我们的技术与使用常微分方程组的基于浓度的连续方法之间的差异。此外,我们从两个著名模型的建模中提供了结果:Lotka-Volterra捕食者-猎物模型和昼夜节律模型。对于这些模型,我们提供了与普通微分方程和Gillespie算法所得结果相比较的仿真技术结果。我们证明了我们的算法虽然比Gillespie的方法要快,但能够在普通微分方程不产生振荡行为的情况下产生振荡行为。

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