Real-Time Coupling of Gene Networks in Single Cells

PI：Animesh Ray单细胞基因网络的实时耦合概述基因调控网络的复杂动力学构成了一系列基本生物学问题的基础，包括维持正常细胞状态、细胞对内部和外部信号的反应、生物系统固有噪声的处理以及生物体的进化轨迹。此外，含有相同基因组的细胞在其表观遗传状态中几乎总是异质性的，其中少数关键调控因子的随机波动(内在噪声)和生化机械的一般波动(外在噪声)的影响严重影响网络动力学。处理这些过程的数学方法还没有完全开发出来。在该方案中，利用了一种新的合成生物学方法，并结合了随机进化博弈论的交互建模。后者是噪声相互作用系统的理想建模框架，其中组件状态的概率分布相互影响。两个合成遗传网络将在两个分开的酵母细胞中设计和实施，这两个细胞在其他方面是相同的(交配型基因座除外)。这两个网络中的每一个都将一种共同的代谢物作为输入，并输出两种荧光蛋白质中的一种，这些蛋白质将通过实时成像技术在单细胞中进行测量。这两个网络将通过配对细胞而相互耦合，并将监视耦合的动态。这些网络的设计使它们既有机会相互合作，也有机会相互干扰。耦合系数是可调的。将设计一个随机进化博弈论模型来确定与实验结果的一致性。这里提出的理论和实验都是高风险的：1)以前从未研究过单细胞中两个合成遗传电路的实时耦合动力学；2)这里提出的特定的基因网络还没有被研究过，所以不能保证观察到的动力学会反映模拟的动力学；3)还没有人尝试(据我们所知)对单细胞基因表达动力学进行配对细胞成像；4)随机进化博弈理论在数学科学中是一个相对较新的研究领域，它对基因调控网络动力学的适用性还不确定。考虑到这些不确定性，并考虑到如果该项目成功，有可能推进一种关于生物网络动力学的新方法，该项目被认为适合急于提交。从这些研究中获得的见解将对理解网络如何在噪声环境中通信以及具有随机行为的模态理性实体如何在通信网络中合作或竞争具有价值。

PI: Animesh Ray Real-time coupling of gene networks in single cells SUMMARY The complex dynamics of gene regulatory networks underlie a host of fundamental biological questions including the maintenance of normal cellular states, response of cells to internal and external signals, handling of noise inherent in biological systems, and the evolutionary trajectory of organisms. Moreover, cells containing identical gene sets nearly always are heterogeneous in their epigenetic states, where the effects of stochastic fluctuation of a small number of key regulators (intrinsic noise) and general fluctuation in biochemical machinery (extrinsic noise) critically affect network dynamics. Mathematical methods for tackling these processes are not yet fully developed. In this proposal advantage is taken of a novel synthetic biology approach coupled with interaction modeling by stochastic evolutionary game theory. The latter is an ideal modeling framework for noisy interacting systems where the probability distribution of component states influence one another. Two synthetic genetic networks will be designed and implemented within two separate yeast cells that are otherwise genetically identical (except at the mating type locus). Each of these two networks takes as input a common metabolite and outputs one of two fluorescent proteins that will be measured in single cells by real time imaging techniques. The two networks will be coupled to each other by mating the cells and the coupled dynamics will be monitored. The networks are designed such that they have the opportunity to both cooperate and interfere with each other. The coupling coefficients are tunable. A stochastic evolutionary game theoretic model will be devised to determine agreement with experimental results. Both the theory and experiments proposed here are high-risk: 1) Real-time dynamics of coupling of two synthetic genetic circuits in single cells have never been studied before; 2) The particular gene networks proposed here have not been studied, so there is no guarantee that the observed dynamics will reflect simulated dynamics; 3) Imaging mating cells for single cell gene expression dynamics has not yet been attempted (to our knowledge); 4) Stochastic evolutionary game theory is a relatively new area of research in mathematical sciences and its applicability to gene regulatory network dynamics is uncertain. Given these uncertainties, and given the potential for advancing a new way of thinking about biological network dynamics if the project is successful, this project is deemed suitable for an EAGER submission. Insights learned from these studies will be valuable for understanding how networks communicate in a noisy environment and how modally rational entities with stochastic behavior cooperate or compete in communication networks.