Department of Biomedical Engineering, Rutgers University, 599 Taylor Road, Piscataway, NJ 08854, USA.
J Theor Biol. 2013 Dec 7;338:9-15. doi: 10.1016/j.jtbi.2013.08.011. Epub 2013 Aug 21.
The human body can be viewed as a dynamical system, with physiological states such as health and disease broadly representing steady states. From this perspective, and given inter- and intra-individual heterogeneity, an important task is identifying the propensity to transition from one steady state to another, which in practice can occur abruptly. Detecting impending transitions between steady states is of significant importance in many fields, and thus a variety of methods have been developed for this purpose, but lack of data has limited applications in physiology. Here, we propose a model-based approach towards identifying critical transitions in systemic inflammation based on a minimal amount of assumptions about the availability of data and the structure of the system. We derived a warning signal metric to identify forthcoming abrupt transitions occurring in a mathematical model of systemic inflammation with a gradually increasing bacterial load. Intervention to remove the inflammatory stimulus was successful in restoring homeostasis if undertaken when the warning signal was elevated rather than waiting for the state variables of the system themselves to begin moving to a new steady state. The proposed combination of data and model-based analysis for predicting physiological transitions represents a step forward towards the quantitative study of complex biological systems.
人体可以被视为一个动力系统，健康和疾病等生理状态广泛代表着稳定状态。从这个角度来看，考虑到个体间和个体内的异质性，一个重要的任务是确定从一个稳定状态向另一个稳定状态转变的倾向，而在实践中，这种转变可能是突然发生的。在许多领域，检测稳定状态之间即将发生的转变具有重要意义，因此已经开发了多种用于此目的的方法，但由于数据缺乏，在生理学中的应用受到限制。在这里，我们提出了一种基于模型的方法，用于根据关于数据可用性和系统结构的最小假设来识别全身性炎症中的关键转变。我们推导出了一个警告信号指标，用于识别在全身性炎症的数学模型中出现的即将发生的突然转变，该模型的细菌负荷逐渐增加。如果在警告信号升高时进行干预以消除炎症刺激，而不是等待系统自身的状态变量开始移动到新的稳定状态，那么恢复体内平衡的成功率会更高。用于预测生理转变的数据和基于模型的分析的这种组合代表了朝着对复杂生物系统进行定量研究迈出的一步。
