Characterization of Gradient-Responsive Genetic Programs Using Light Sensors

描述（由申请人提供）：空间梯度使细胞能够精确地组织成复杂的多细胞模式。形态发生素由细胞簇释放并向外扩散以形成梯度。周围的细胞使用由整合的遗传逻辑、反馈和细胞间通讯组成的调控遗传程序将这种梯度转化为基因表达模式。这项建议的中心目标是确定遗传程序将梯度转化为基因表达模式的设计原则。我们的方法将利用两个新工具。第一种是一组红色和绿色光传感器，其以分级方式激活信号通路作为光强度的函数。这些将被用来传递光梯度，这些光梯度将被遗传程序作为输入进行处理。第二种是一套计算方法，使我们能够将所需的遗传程序转换为DNA序列。这种模型引导的设计使我们能够列举和评估许多遗传程序在硅片上，然后构建和实验测试最有趣的程序库。此外，生物物理方法，预测调整表达水平，通过改变核糖体结合位点序列将利用定量比较突变的鲁棒性和进化的程序。这些目标围绕着关于调节网络如何处理单个和多个梯度信号的假设检验而组织。具体来说，我们将测试以下理论：（i）嵌入式前馈回路提高了对影响表达水平的突变的鲁棒性（目标1）。(ii)反馈回路和空间双稳性增加了模式的清晰度和鲁棒性（目标1）。(iii)集成相反梯度的程序提高了鲁棒性和模式演化性（目标2）。(iv)相反的梯度可以用作尺寸无关中心查找算法的一部分（目标2）。利用人工合成的遗传电路和E.大肠杆菌作为一个模型系统，将有可能描述许多遗传程序的梯度处理特性，并与完全参数化的数学模型进行定量比较。这种方法将阐明如何组织监管网络的原则，以处理梯度，然后可以推广到其他系统。

DESCRIPTION (provided by applicant): Spatial gradients enable cells to precisely organize into complex multicellular patterns. Morphogens are released by clusters of cells and diffuse outward to form a gradient. Surrounding cells convert this gradient into patterns of gene expression using regulatory genetic programs composed of integrated genetic logic, feedback, and cell-cell communication. The central objective of this proposal is to identify the design principles by which genetic programs convert gradients into patterns of gene expression. Our approach will harness two new tools. The first is a set of red and green light sensors that activate a signaling pathway in a graded manner as a function of light intensity. These will be used to deliver light gradients that will be processed as inputs by a genetic program. The second is a suite of computational methods that enable us to convert a desired genetic program into a DNA sequence. This model-guided design allows us to enumerate and evaluate many genetic programs in silico and then construct and experimentally test a library of the most interesting programs. Further, a biophysical method that predicatively tunes expression levels by changing the ribosome binding site sequence will be harnessed to quantitatively compare the mutational robustness and evolvability of programs. The Aims are organized around the testing of hypotheses as to how regulatory networks process single and multiple gradient signals. Specifically, we will test the following theories: (i) Embedded feedforward loops improve robustness to mutations that affect expression levels (Aim 1). (ii) Feedback loops and spatial bistability increase pattern sharpness and robustness (Aim 1). (iii) Programs that integrate opposing gradients have improved robustness and pattern evolvability (Aim 2). (iv) Opposing gradients can be used as part of size-independent center-finding algorithms (Aim 2). The use of synthetic genetic circuits and E. coli as a model system will make it possible to characterize the gradient-processing properties of many genetic programs and make quantitative comparisons to a fully parameterized mathematical model. This approach will elucidate principles of how regulatory networks are organized to process gradients that can then be generalized to other systems.