Systems-level measurements of biophysical parameters in the Dorsal/NF-kappaB pathway

胚胎的复杂性是惊人的：它最初只是一个细胞，但随着时间的推移，它会填充数万个甚至更多的细胞。为了开始破译发育的复杂性，以及在胚胎发育过程中帮助决定细胞命运的分子的作用，科学家和工程师构建了描述发育过程的数学模型。然而，随着这些数学系统生物学模型变得更加详细，为了描述发展的机械复杂性，它们也变得更加不确定。为了克服与机械系统生物学模型相关的这种不确定性，研究人员将以果蝇黑腹果蝇为模式生物，进行精心设计的实验，以测量活胚胎中的分子和组织尺度特性。该项目将导致详细了解果蝇胚胎中的细胞如何获得它们的命运，增加我们对非哺乳动物系统以及最终哺乳动物系统中的发育过程的洞察。除了培训研究生，该项目的更广泛影响包括扩大和在线传播系统生物学和工程的研究生课程，以及进一步培养K-12水平的学术课程，将生物和工程原理协同结合起来，作为与北卡罗来纳州立大学科学之家合作举办的为期数天的高中生夏令营的一部分。在过去的15年里，我们对发育生物学的理解被实时的、基于图像的实验平台彻底改变了，这些平台使我们能够获取大量的量化数据集，并提供了关于胚胎发生过程中组织模式的新视角。结合实验和计算的进展，这个项目的总体目标是对个体生物物理参数和组织水平的行为进行详细的测量，以限制早期果蝇胚胎中背部/核因子；B信号模块的预测性、计算性模型。背部信号模块由转录因子DRADAL在整个胚胎的背腹轴上的空间梯度浓度组成。胚胎中的细胞以浓度依赖的方式对背部做出反应，因此背部的空间梯度将背腹轴划分为不同的子域。该项目将对控制胚胎蛋白质浓度的单个生物物理参数和全球梯度进行测量，包括核进出口速率、胚胎水平的背部运动以及背部与其细胞质抑制剂仙人掌的结合和解离速率。实验数据将作为参数优化的模型约束，并结合生成预测系统生物学模型，用于准确预测野生型和突变型果蝇胚胎的背部活动梯度。该项目将促进我们对胚胎发育过程中背部梯度形成动力学的理解，并提供一个有价值的原型，它演示了如何使用实验设计来约束系统生物学模型，方法是同时整合局部(单个参数的测量)和全局(全模型约束)生成的测量。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The complexity of an embryo is staggering: it begins as a single cell, but over time becomes populated with tens of thousands of cells or more. To begin to decipher the complexity of development and the role of molecules that help to determine cell fate during embryonic development, scientists and engineers have constructed mathematical models that describe developmental processes. However, as these mathematical systems biology models become more detailed, in order to describe the mechanistic complexity of development, they also become more uncertain. To overcome this uncertainty associated with mechanistic systems biology models, using the fruit fly Drosophila melanogaster as a model organism, the investigator will conduct carefully-designed experiments to measure molecular and tissue scale properties in live embryos. The project will result in a detailed understanding of how cells in the Drosophila embryo acquire their fate, increasing our insight into developmental processes in non-mamalian and ultimately mammalian systems. In addition to training graduate students, the broader impacts of the project include the expansion and online dissemination of a graduate course on systems biology and engineering, and further cultivation of academic programs at the K-12 level that synergistically combine biological and engineering principles as part of a multi-day summer camp for high school students, which is held in partnership with NC State University's Science House. In the past decade and a half, our understanding of developmental biology has been revolutionized by live, real-time, image-based experimental platforms, which enable the acquisition of vast quantitative datasets and provide novel views of tissue patterning during embryogenesis. Integrating experimental and computational advances, the overall objective of this project is to perform detailed measurements of individual biophysical parameters and tissue-level behavior to constrain a predictive, computational model of the Dorsal/NF-κB signaling module in the early Drosophila embryo. The Dorsal signaling module consists of spatially graded concentration of a transcription factor, Dorsal, throughout the dorsal-ventral axis of the embryo. The cells in the embryo respond to Dorsal in a concentration-dependent fashion, so that the spatial gradient of Dorsal patterns the dorsal-ventral axis into distinct sub-domains. The project will conduct measurements of individual biophysical parameters and global gradients that control embryonic protein concentrations, including nuclear import/export rates, embryo-level movement of Dorsal, and binding and dissociation rates of Dorsal with its cytoplasmic inhibitor Cactus. The experimental data will be used as model constraints for parameter optimization, and integrated to generate a predictive systems biology model for accurate prediction of the Dorsal activity gradient in wildtype and mutant Drosophila embryos. The project will advance our understanding of the dynamics of Dorsal gradient formation during embryogenesis, and provide a valuable prototype that demonstrates the use of experimental design to constrain systems biology models through the concurrent integration of locally (measurements of individual parameters) and globally (full-model constraint) generated measures.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.