Collaborative Proposal: NCR-Circuit Dynamics

利用遗传调控网络的组合分解成子网络，并利用酵母的多功能性将每个子网络编码为一个单独的菌株，本研究将解剖，反卷积和破译氮分解代谢抑制的动力学。基于使用转录因子融合蛋白，活细胞成像和转录率定量测量的详细测量，研究人员将开发复杂酵母网络动态行为的精确模型。定量模型将为发现数学定理提供一个平台，这些定理将遗传回路的结构与它们的动力学和功能联系起来。这个理论反过来将允许研究电路相对于参数及其输出的鲁棒性。将要开发的数学理论将使研究人员能够简化大型生物网络的模拟。近年来，人们越来越意识到生物有机体和工程系统之间的联系。有一些基因网络控制和调节其他基因的行为，并最终调节所有的生物过程。这些基因网络类似于电路和开关。人们越来越愿意和渴望利用数学和计算技术来利用有关生物控制电路结构的所有现有信息，以了解预测它们的基本规则，并获得预测其输出的能力。了解这些网络的动态将会给人类疾病的治疗带来革命性的变化，并为新一代强大的电子和数字设备的开发提供洞察力。该项目将对研究生教育和招收数学研究生从事跨学科科学工作，以及招收生物学、计算机科学和医学学生进入跨学科和新兴的生物医学信息学领域产生重大影响。

Using a combinatorial decomposition of a genetic regulatory network into sub-networks and harnessing the versatility of yeast to encode each of these as a separate strain, this research will dissect, deconvolve and decipher the dynamics of nitrogen catabolite repression. Based on detailed measurements using transcription factor fusion proteins, live cell imaging, and quantitative measurement of transcription rate, the investigors will develop precise models of the dynamical behavior of the complex yeast network. Quantitative models will provide a platform for for the discovery of mathematical theorems, that relate the structure of genetic circuits to their dynamics and function. This theory in turn will allow a study of the robustness of the circuit with respect to parameters and its output. The mathematical theory that will be developed will, among other things, allow researchers to streamline simulations of large biological networks. In recent years there has been a growing awareness of the connections between biological organisms and engineering systems. There are networks of genes that control and regulate the behavior of other genes and ultimately all biological processes. These networks of genes are analogous to electrical circuits and switches. There has been an expanding willingness and desire to use mathematical and computational techniques to leverage all existing information regarding the structure of biological control circuitry to understand the basic rules on which they are predicated and to acquire the ability to predict their output. Understanding the dynamics of such networks promises revolutionary changes in the treatment of human diseases as well as providing insight for the development of a new generations of robust electronic and digital devices. This project will have a significant impact on graduate education and on recruitment of mathematics graduate students for interdisciplinary scientific work as well as the recruitment of biology, computer science and medical students into the interdisciplinary and emerging field of biomedical informatics.