基因电路在脑卒中预警、监测及治疗中的机理研究

Cerebrovascular disease, such as stroke, has already become one of the most threaten killers in the world. It also has the extremely high rates of physical disabilities and recurrence. However, the appearance of genetic circuit has brought a new hope to diagnose and treat stroke rapidly and accurately. Our team has already designed a set of genetic circuits which can be used in monitoring of stroke in this project for the first time. AT-Ⅲ, MTHFR, CRP, S-100, CYP2C19, CYP2C9, VKORC1, etc.，will serve as the initial selection of the stroke biological markers and more efficient markers will be explored and selected by our team. According to the selected markers and their corresponding promoters, this project will optimize the RBS (Ribosome Binding Site) of specific gene unit to complete the design of boundary detection circuit. Then, we will establish biochemical reactions model, ODE model with estimates of kinetic parameters and stochastic model under the actual noise conditions. Special software (BioSpice, for example), will also be used to simulate these models. During the process of simulation, we will optimize the kinetic parameters, such as protein degradation rate, mRNA decay rate, Transcription rate, etc. And we will design and optimize single indicator monitoring circuit. Besides, the multi-indicator integration monitoring circuit will be established in consideration of different sensibilities of the markers towards stoke. Moreover, researches at diagnosis, early warning and treatment of recurrence and metastasis of stroke will be made. At last, the project is also about to conduct corresponding biomedical validation tests, to explore genetic circuit design method on higher abstracted level and general genetic monitoring treatment circuit design, and to prepare the basic researches for clinical application.

脑血管疾病已成为威胁人类健康与生命的头号杀手，病后残障率和复发率都很高。基因电路为实现快速、准确地诊断与治疗带来了新希望。本项目将基因电路首次用于脑卒中监测中。探索生物学标志物(AT-Ⅲ, MTHFR, CRP, S-100, CYP2C19, CYP2C9, VKORC1等)及对应的启动子，分别优化特定基因单元的核糖体结合位点(RBS)等序列，完成边界检测电路设计；建立生化反应模型、动力学参数估计ODE模型及实际噪声条件下的随机模型，由专用BioSpice等软件进行仿真，优化动力学参数(蛋白降解速率、mRNA 降解速率、转录速率等)，完善单指标监测电路的设计；根据标志物诱发脑卒中敏感性不同，优化设计多指标融合监测电路，开展脑卒中的预警、诊断及治疗研究；进行相应的生物医学试验验证，探索更高抽象层面上的基因电路设计方法及通用性的基因监测及治疗电路与系统，为临床医学应用作好基础研究准备。

脑卒中已成为全球第一大死因，具有极高的发病率、致死率及致残率。项目将基因电路技术首次用于脑卒中监测中，为快速准确地诊断、预警及临床治疗打下坚实的基础。.提出了结合脑卒中标志物的互信息和主成分分析的支持向量机模型，实现了对脑卒中标志物的影响的优化。.通过互信息分析法，研究了临床上23种检测标志物与脑卒中患病的相关性。通过仿真实验，根据脑卒中生化反应动力学参数，确定了置信区间阈值，优化出了与脑卒中最相关的主成分，作为支持向量机模型的输入变量。进而通过支持向量机的学习，实现了对这些主成分标志物的联合检测，探索了不同标志物对脑卒中患病的影响。.设计了脑卒中的单指标基因电路和多指标融合检测基因电路。.根据质量作用定理，在生化反应方程的基础上，建立了单指标电路微分方程模型。借助反相器的结构，构建出了脑卒中单指标检测电路。利用iBiosim等软件进行仿真实验，优化动力学参数，分析了脑卒中单指标检测电路主要模块及作用效果的影响因素。使用了Matlab对OR-Gate1与 OR-Gate2进行数值仿真，并对两者的输出效果进行了对比，选取OR-Gate2作为电路或门。.通过搭建脑卒中监测电路系统，将多个电路进行融合分析，优化设计了完整的脑卒中预警、监测及治疗融合基因电路。融合电路系统能汇总多个监测电路的输出结果，综合各级模块的输出，实现逻辑判断，显著提高了输出结果准确率。.提出了一种代码级的基因电路设计方法。.研究了基因电路的通用性及类似集成电路拥有代码级等更高层次的设计方法。通过分析基因电路与数字电路基本逻辑的对应关系，使用代码编译进行功能抽象，高效准确地完成了复杂电路的设计。同时该方法具有普适性，可以用于其他疾病（如肺癌、乳腺癌等）的监测治疗基因电路设计。.研究结果表明，项目采用已经搭建完成的基因电路元件库中相应功能的模型替代门级电路中的各个元件，能获得适用于多种标志物的检测基因电路，从而实现对脑卒中疾病的临床检测与诊断。..