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Continuous Description of Discrete Biological Data: Algorithms Based on a Stochastic Flow Model

机译:离散生物数据的连续描述:基于随机流模型的算法

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摘要

The applicability of differential equations to description of integer values dynamics in bio-informatics is investigated. It is shown that a differential model may be interpreted as a continuous analogue of a stochastic flow. The method of construction of a quasi-Poisson flow on the base of multi-dimension differential equations is proposed. Mathematical correctness of the algorithm is proven. The system has been studied by a computer simulation and a discrete nature of processes has been taken into account. The proposed schema has been applied to the classical Volterra's models, which are widely used for description of biological systems. It has been demonstrated that although behaviour of discrete and continuous models is similar, some essential qualitative and quantitative differences in their dynamics take place.
机译:研究了微分方程在生物信息学中描述整数动态的适用性。结果表明,差分模型可以解释为随机流的连续模拟。提出了基于多维微分方程的拟泊松流构造方法。证明了该算法的数学正确性。该系统已通过计算机仿真进行了研究,并已考虑到过程的离散性。提议的方案已应用于经典的Volterra模型,该模型被广泛用于描述生物系统。已经证明,尽管离散模型和连续模型的行为是相似的,但它们的动力学仍存在一些本质上的定性和定量差异。

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