Experimental and mathematical analysis of delay in transcripitional signaling

描述(由申请人提供)：基因调控网络构成细胞决策过程的基础。对通过这种网络的信息流的描述对于理解它们的功能是必不可少的。这种描述由于信号传输的非瞬时性质而变得复杂。信号以转录因子的形式，经历了首先是信使核糖核酸，然后是蛋白质的顺序组装。这种“转录延迟”很重要，因为它影响双输入转录逻辑门的同时性，以及其他功能依赖于信号到达时间的基序。延迟也可能导致非平凡的动态，例如包含反馈的基因网络中的振荡。包含延迟的基因网络的数学模型在过去已经被使用。然而，到目前为止，还没有系统的努力来从实验和理论上表征转录延迟及其对网络行为的影响。这项提案中概述的工作将在实验上建立延迟对转录信号的影响，并为生成和分析包含延迟的基因网络模型提供新的理论技术。这项工作是高度跨学科的，包括合成生物学、微流体工程、理论生物学和动力系统的各个方面。具体地说，PI将：1)创建由公共网络基序和可变长度转录级联组成的新型合成基因网络；2)使用微流控设备和延时荧光显微镜在单细胞水平表征合成网络的动力学；3)从数学上表征转录延迟的量和变异性作为级联长度的函数；4)检验实验表征的延迟对简单基因网络中动力学和计算的影响；5)在小型合成基因网络中实验测试这些理论预测；以及6)生成和分析将转录延迟纳入基因调控网络模型的新的数学简化技术。

DESCRIPTION (provided by applicant): Gene regulatory networks form the basis for cellular decision-making processes. A description of the flow of information through such networks is essential to understanding their function. Such a description is complicated by the non-instantaneous nature of signal transmission. Signals, in the form of transcription factors, undergo a sequential assembly of first mRNA and then protein. This "transcriptional delay" is important because it influences the simultaneity at dual-input transcriptional logic gates, and other motifs whose functions rely on the timing of arriving signals. Delay can also lead to nontrivial dynamics such as oscillations in gene networks containing feedback. Mathematical models of gene networks that incorporate delay have been used in the past. However, to date there has been no systematic effort to experimentally and theoretically characterize transcriptional delay and its effects on network behavior. The work outlined in this proposal will experimentally establish the effects of delay on transcriptional signaling and provide novel theoretical techniques for generating and analyzing models of gene networks incorporating delay. The work is highly interdisciplinary, incorporating aspects of synthetic biology, microfluidc engineering, theoretical biology, and dynamical systems. Specifically, the PIs will: 1) create novel synthetic gene networks consisting of common network motifs and variable length transcriptional cascades; 2) characterize the dynamics of the synthetic networks at the single-cell level using microfluidic devices and time-lapse fluorescence microscopy; 3) mathematically characterize the amount and variability of transcriptional delay as a function of cascade length; 4) examine the impact of experimentally characterized delay on the dynamics and computation in simple gene networks; 5) experimentally test these theoretical predictions in small synthetic gene networks, and 6) generate and analyze novel mathematical reduction techniques for incorporating transcriptional delay into models of gene regulatory networks.