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Principles to Build a Stochastic Model for a Minimal Biological Cell with Built-in Feedback Reaction Capabilities

机译:建立一个带内置反馈反应能力的最小生物细胞随机模型的原则

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There are two basic approaches used to develop the so called genetic algorithms: the deterministic approach, and the second one is the stochastic approach, with algorithms that add randomness to the model. The stochastic model is based on multiple reactions of molecules that can occur in spatially homogenous system, a situation that is characteristic to the natural biological cells. The randomness is a must to have a simulation model behavior that corresponds to the real phenomena. To each simulation model, another problem is to add the feedback reactions that brings the cell model closer to a real one. A cell model built on those principles is describe. The original contribution of this paper is to establish the basic principles that proved to work with that specific cell model.
机译:有两种基本方法用于开发所谓的遗传算法:确定性方法,第二个是随机方法,算法为模型添加随机性。随机模型基于在空间均匀的系统中可能发生的多种分子反应,是对天然生物细胞的特征的情况。随机性是必须具有与真实现象对应的模拟模型行为。对于每个模拟模型,另一个问题是添加将细胞模型更接近真实的反馈反应。描述了那些基础的细胞模型是描述的。本文的原始贡献是建立证明与该特定细胞模型合作的基本原则。

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