Dagasso Gabrielle, Urban Joanna, Kwiatkowska Mila
Department of Computing Science, Thompson Rivers University, Kamloops, 805 TRU Way, V2C 0C8, Canada.
Department of Biological Sciences, Thompson Rivers University, Kamloops, 805 TRU Way, V2C 0C8, Canada.
Procedia Comput Sci. 2021;185:144-151. doi: 10.1016/j.procs.2021.05.016. Epub 2021 Jun 10.
Mathematical modelling helps to describe the functional and causal relationships between objects in the physical world. The complexity of these models increases as more components and variables are added to maintain and observe. Differential equations are regularly used in these models, as they are able to display the interactions between several variables and describe non-linear behaviour. Differential equations are commonly used in immune response mathematical models to help describe these complex and dynamic interactions within the immune system of the organism. Time delays in the immune system are common and are often disregarded due to the low-resolution of models, which provide limited description of the specific section of immune system being studied. The few models that incorporate time delays are mostly at the epidemiological level, to track the spread of the virus in the population. In this paper we review the applications of the models based on differential equations and describe their potential utilization for the studies of immune response in SARS-CoV-2.
数学建模有助于描述物理世界中对象之间的功能和因果关系。随着添加更多组件和变量以进行维护和观测,这些模型的复杂性会增加。微分方程经常用于这些模型中,因为它们能够展示多个变量之间的相互作用并描述非线性行为。微分方程常用于免疫反应数学模型,以帮助描述生物体免疫系统内这些复杂且动态的相互作用。免疫系统中的时间延迟很常见,并且由于模型分辨率低,这些时间延迟通常被忽略,而低分辨率模型对所研究的免疫系统特定部分的描述有限。少数纳入时间延迟的模型大多处于流行病学层面,用于追踪病毒在人群中的传播。在本文中,我们回顾基于微分方程的模型的应用,并描述它们在研究新冠病毒免疫反应方面的潜在用途。