Ernst-Abbe University of Applied Sciences Jena, Carl-Zeiss-Promenade 2, 07745 Jena, Germany.
Bio Systems Analysis Group, Department of Mathematics and Computer Science, University of Jena, Ernst-Abbe-Platz 2, 07743 Jena, Germany.
Viruses. 2019 May 16;11(5):449. doi: 10.3390/v11050449.
Influenza A virus is recognized today as one of the most challenging viruses that threatens both human and animal health worldwide. Understanding the control mechanisms of influenza infection and dynamics is crucial and could result in effective future treatment strategies. Many kinetic models based on differential equations have been developed in recent decades to capture viral dynamics within a host. These models differ in their complexity in terms of number of species elements and number of reactions. Here, we present a new approach to understanding the overall structure of twelve influenza A virus infection models and their relationship to each other. To this end, we apply chemical organization theory to obtain a hierarchical decomposition of the models into chemical organizations. The decomposition is based on the model structure (reaction rules) but is independent of kinetic details such as rate constants. We found different types of model structures ranging from two to eight organizations. Furthermore, the model's organizations imply a partial order among models entailing a hierarchy of model, revealing a high model diversity with respect to their long-term behavior. Our methods and results can be helpful in model development and model integration, also beyond the influenza area.
甲型流感病毒是目前被认为对全球人类和动物健康威胁最大的病毒之一。了解流感感染和动态的控制机制至关重要，这可能会导致未来有效的治疗策略。近几十年来，已经开发了许多基于微分方程的动力学模型来捕捉宿主内的病毒动态。这些模型在物种元素的数量和反应的数量方面在复杂性上有所不同。在这里，我们提出了一种新的方法来理解十二种甲型流感病毒感染模型的整体结构及其相互关系。为此，我们应用化学组织理论将模型分解为化学组织。该分解基于模型结构（反应规则），但与动力学细节（如速率常数）无关。我们发现了从两种到八种组织的不同类型的模型结构。此外，模型的组织意味着模型之间存在偏序，从而揭示了模型在长期行为方面的高度多样性。我们的方法和结果可有助于模型开发和模型集成，甚至超越流感领域。
