Characterization of a Droplet Microfluidic High Throughput Screening Device and Developing Machine Learning Algorithms to Study the Bone Morphogenetic Protein Signaling Pathway

项目摘要 一些细胞信号传导系统通过混杂信号传导机制来运作，其中多个配体可以结合 在启动导致基因表达的下游信号级联之前， 存在于细胞中的混杂信号系统在许多不同类型的生物过程中普遍存在， 发展和维持，疾病，包括癌症。骨形态发生途径（BMP）是一种 理想的混杂信号通路研究，因为，10个不同的BMP配体作为生长因子， 每一种都与所述途径的I型或II型受体竞争性结合。最近的工作创造了数学 BMP通路中混杂相互作用的模型能够复制实验性的 通过给药BMP-应答细胞系观察BMP途径信号传导，所述BMP-应答细胞系在施用时表达YFP。 BMP基因表达被激活，以6倍的BMP配体滴定系列。然而，之前的结果依赖于 BMP途径的矩阵组合筛选，以检查响应并拟合参数的小子集 复制实验结果的数学模型。继续筛选配体组合 结果以这种方式导致所需的配体筛选数量的指数增加。更好 需要硬件和数学工具来筛选BMP通路，以更好地理解混杂的BMP通路。 信号现象。该项目旨在开发一种液滴微流控设备，即DropShop平台， 能够以高通量方式筛选BMP配体组合。为了做到这一点， 腺鼠BMP-反应细胞系将通过一种新的微滴微流控技术进行筛选， 使用微载体的细胞培养方法。DropShop平台的液滴微流控将得到优化 用这种新的细胞培养方法。在某种细胞类型中筛选BMP配体的原理证明 将在该系统中通过使用通常用于高浓度的荧光测量系统来证明 通量液滴微流体筛选。最后，将开发机器学习方法来优化 筛选配体以减少确定BMP数学模型参数的时间，以及 帮助选择表征实验结果的正确模型。由此产生的系统将 展示了能够自动确定微滴微流体装置的概念验证 混杂信号通路中的机制模型及其参数。

PROJECT SUMMARY Some cell signaling systems operate by a mechanism of promiscuous signaling, where multiple ligands can bind to a single receptor before starting a downstream cascade of signaling that results in gene expression. Promiscuous signaling systems present in cells are prevalent in many different types of biological processes from development and maintenance, to disease, including cancer. The bone morphogenetic pathway (BMP) is an ideal promiscuous signaling pathway to study because, of the 10 distinct BMP ligands that act as growth factors, each competitively binds with a type I or type II receptor of the pathway. Recent work created mathematical models of the promiscuous interactions within the BMP pathway that were able to replicate experimental observations of BMP pathway signaling by dosing a BMP-responsive cell line, which expressed YFP when the BMP gene expression was activated, to a 6-fold BMP ligand titration series. However, previous results relied on matrix combination screening of BMP pathway to examine responses and fit a small subset of the parameters of the mathematical models replicating experimental results. Continuing to screen combinations of ligands results in this manner results in an exponential increase in the number of ligand screens required. Better hardware and mathematical tools are needed to screen the BMP pathway to better understand promiscuous signaling phenomena. This project aims to develop a droplet microfluidic device, the DropShop platform, that can screen BMP ligand combinations in a high throughput manner. To do this, an adherent epithelial mammary gland murine BMP-responsive cell line will be adapted to screening by droplet microfluidics through a novel method of cell culture using microcarriers. The droplet microfluidics of the DropShop platform will be optimized to work with the novel cell culture method. Proof of principle of screening of BMP ligands in a certain cell type will be demonstrated in this system by use of a fluorescent measurement system typically used in high throughput droplet microfluidic screening. Finally, machine learning methods will be developed to optimize screening of ligands to reduce the time to determine parameter of the BMP mathematical model, as well as help in selecting the correct model that characterizes experimental results. The resulting system will demonstrate a proof of concept for a droplet microfluidic device capable of automatically determining mechanistic models and their parameters in promiscuous signaling pathways.