动力系统方法与某些复杂疾病恶性突变的早期预警信号

Many evidences suggested that during the progression of many complex diseases, the deteriorations are generally not smooth but abrupt, which may cause a critical transition from one state to another at a tipping point, corresponding to a bifurcation of the dynamical system for the underlying mechanism. A pre-disease state is assumed to exist before reaching the tipping point between a normal state and a disease state. Beside, a complex disease is generally resulted not from the malfunction of individual molecules but from the dynamical interplay of a group of correlated molecules or network. Therefore, in order to describe the underlying dynamical mechanism of complex diseases, their evolutions are often modeled as time-dependent nonlinear dynamical systems, in which the abrupt deterioration is viewed as the phase transition at a bifurcation point. By exploiting rich information from time-course high-throughput omic data and based on dynamical bifurcation theory, we will construct regulation networks that describe the progression of complex diseases. We will then theoretically prove that some generic properties suffice to provide early-warning signals for the imminent catastrophic phase transition when the system approaches to the tipping point, so as to prevent qualitative deterioration by taking appropriate intervention actions. Based on these critical behaviors and generic properties of the network system, a prediction method towards the sudden deterioration of complex diseases will be developed in the dynamical network level. The effectiveness of the method will be validated by numerical simulation and functional analysis based on experimental data.

现代医学和生物学的研究成果表明，在生物体的各个器官内，是各个功能模块的动态协同作用共同决定了器官的功能和状态，因此，我们把复杂疾病的发展过程看做是一个复杂动力系统的演变，把影响疾病的外在因素视为动力系统中的参数，把参与疾病演变的分子浓度当作系统中的状态变量，于是疾病的突然恶化现象就对应了系统的突变现象。我们将收集某些复杂疾病的多层次时序列数据，并对这些高通量数据进行分析、整理；根据整合后的实验数据和分子生物学理论，构造能够表征疾病发展的动态调控网络，并进一步建立相应的动力系统。针对不同的复杂疾病，从理论和数值分析两个方面寻找网络中携带预警信号的重要功能模块，探测动力系统发生突变的临界点，提炼出有针对性的早期预警突变的方法，建立动态网络层次的预警体系。

在本项目的资助下，我们根据申请书中所列的研究内容，针对某些复杂疾病的突变现象，采用计划任务书中所列的研究计划和研究路线开展研究工作，并发表了三篇论文。在这些工作中我们首先针对具有突变现象的复杂疾病，在大样本、小噪声条件下，我们总结和发展了利用生物分子动态网络标志物来预测疾病发展中的关键时间节点的方法，并成功地应用到肺部损伤产生突变的病例中；另外，我们针对一类具有应用价值的广义KdV方程，研究其动力学行为，并发现了新的分支现象。我们的工作达到了预期的目标。