DataSheet1_Predicting Experimental Sepsis Survival with a Mathematical Model of Acute Inflammation.PDF

Sepsis is characterized by an overactive, dysregulated inflammatory response that drives organ dysfunction and often results in death. Mathematical modeling has emerged as an essential tool for understanding the underlying complex biological processes. A system of four ordinary differential equations (ODEs) was developed to simulate the dynamics of bacteria, the pro- and anti-inflammatory responses, and tissue damage (whose molecular correlate is damage-associated molecular pattern [DAMP] molecules and which integrates inputs from the other variables, feeds back to drive further inflammation, and serves as a proxy for whole-organism health status). The ODE model was calibrated to experimental data from E. coli infection in genetically identical rats and was validated with mortality data for these animals. The model demonstrated recovery, aseptic death, or septic death outcomes for a simulated infection while varying the initial inoculum, pathogen growth rate, strength of the local immune response, and activation of the pro-inflammatory response in the system. In general, more septic outcomes were encountered when the initial inoculum of bacteria was increased, the pathogen growth rate was increased, or the host immune response was decreased. The model demonstrated that small changes in parameter values, such as those governing the pathogen or the immune response, could explain the experimentally observed variability in mortality rates among septic rats. A local sensitivity analysis was conducted to understand the magnitude of such parameter effects on system dynamics. Despite successful predictions of mortality, simulated trajectories of bacteria, inflammatory responses, and damage were closely clustered during the initial stages of infection, suggesting that uncertainty in initial conditions could lead to difficulty in predicting outcomes of sepsis by using inflammation biomarker levels.

脓毒症（Sepsis）以过度激活且失调的炎症反应为核心特征，此类反应可引发器官功能障碍，往往会导致患者死亡。数学建模现已成为解析其内在复杂生物学过程的关键工具。研究人员构建了包含四个常微分方程（ordinary differential equations，ODEs）的系统模型，用于模拟细菌、促炎与抗炎反应以及组织损伤的动态变化；其中组织损伤的分子对应物为损伤相关分子模式（damage-associated molecular pattern，DAMP）分子，该分子可整合其他变量的输入信号，反馈驱动进一步的炎症反应，并可作为整体机体健康状态的替代指标。该常微分方程模型基于大肠杆菌（Escherichia coli，E. coli）感染基因同源大鼠的实验数据进行校准，并通过该类动物的死亡率数据完成验证。在模拟感染实验中，通过改变系统内的初始接种量、病原体生长速率、局部免疫反应强度以及促炎反应激活程度，该模型可输出康复、无菌性死亡或脓毒症性死亡三种结局。总体而言，当细菌初始接种量提升、病原体生长速率加快或宿主免疫反应减弱时，出现脓毒症性死亡结局的概率更高。该模型表明，控制病原体或免疫反应相关参数的微小变化，即可解释实验中观察到的脓毒症大鼠死亡率差异。研究人员开展了局部敏感性分析，以明确此类参数对系统动态的影响程度。尽管该模型可准确预测死亡率，但在感染初始阶段，模拟得到的细菌、炎症反应及损伤轨迹高度聚集，这表明初始条件的不确定性可能导致利用炎症生物标志物水平预测脓毒症结局时存在困难。