论文标题

研究在细胞因子和T细胞相互作用过程中观察到的复杂模式的特性的随机方法

Stochastic approach to study the properties of the complex patterns observed in cytokine and T-cells interaction process

论文作者

Singh, Moirangthem Shubhakanta, Singh, Mairembam Kelvin, Singh, R. K. Brojen

论文摘要

复杂系统中的模式存储了需要探索的系统的隐藏信息。我们提出了一个简单的细胞因子和T细胞相互作用的模型,并通过构造系统的主方程并求解随机框架中的模型。该模型的解决概率分布函数在大种群中显示了经典的泊松模式$ m,z \ rightArrow大$,表明该系统具有吸引细胞因子种群的大量小规模随机过程,通过隔离非兰多姆过程来吸引系统吸引系统的吸引力。此外,在大型$ \ langle z \ rangle $限制中,模式转换为经典的正常模式,在那里,不相关的小规模波动被擦除以形成一种常规但无记忆的时空聚合模式。使用FANO因子的估计噪声清楚地表明,细胞因子动力学是噪声引起的过程,使系统远离平衡。

Patterns in complex systems store hidden information of the system which is needed to be explored. We present a simple model of cytokine and T-cells interaction and studied the model within stochastic framework by constructing Master equation of the system and solving it. The solved probability distribution function of the model show classical Poisson pattern in the large population limit $M,Z\rightarrow large$ indicating the system has the tendency to attract a large number small-scale random processes of the cytokine population towards the basin of attraction of the system by segregating from nonrandom processes. Further, in the large $\langle Z\rangle$ limit, the pattern transform to classical Normal pattern, where, uncorrelated small-scale fluctuations are wiped out to form a regular but memoryless spatiotemporal aggregated pattern. The estimated noise using Fano factor shows clearly that the cytokine dynamics is noise induced process driving the system far away from equilibrium.

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