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

液滴微流体高通量筛选装置的表征和开发机器学习算法来研究骨形态发生蛋白信号通路

• 批准号: 10553603
• 负责人: Vincent David Zaballa
• 金额: $ 4.13万
• 依托单位: UNIVERSITY OF CALIFORNIA-IRVINE
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10553603
• 关键词: Acceleration; Adherent Culture; BMP4; Bayesian learning; Binding; Biochemical; Biological; Biological Assay; Biological Process; Bone Morphogenetic Proteins; Buffers; Cell Culture Techniques; Cell Line; Cells; Competitive Binding; Complex; Data; Development; Devices; Dimensions; Disease; Dose; Engineering; Equipment; Exhibits; Experimental Designs; Foundations; Gene Expression; Growth; Growth Factor; Hour; Incubated; Kinetics; Laboratories; Learning; Ligands; Link; Liquid substance; Machine Learning; Maintenance; Malignant Neoplasms; Mammary gland; Mathematics; Measurement; Methods; Microfluidic Microchips; Microfluidics; Mineral Oil; Modeling; Mus; Oils; Parameter Estimation; Pathway interactions; Phase; Phenotype; Play; Polystyrenes; Process; Rapid screening; Reaction; Reagent; Recommendation; Reporter; Role; Sampling; Series; Signal Pathway; Signal Transduction; Software Tools; System; Systems Integration; Techniques; Time; Titrations; Update; Work; aqueous; biochemical model; bone; cell type; combinatorial; data acquisition; design; experimental study; high dimensionality; high throughput screening; learning algorithm; learning strategy; machine learning algorithm; machine learning method; mammary epithelium; mathematical model; novel; receptor; receptor binding; response; screening; statistics; tool; tumor; unsupervised learning

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.

项目概要 一些细胞信号系统通过混杂信号机制运作，其中多个配体可以结合 在启动导致基因表达的下游信号传导级联之前，先将其结合到单个受体上。 细胞中存在的混杂信号系统普遍存在于许多不同类型的生物过程中 发育和维持，以预防疾病，包括癌症。骨形态发生途径（BMP）是一条 理想的混杂信号通路研究，因为在 10 种不同的 BMP 配体中，充当生长因子， 每种受体都与该途径的 I 型或 II 型受体竞争性结合。最近的工作创造了数学 BMP 通路内混杂相互作用的模型能够复制实验 通过给予 BMP 响应细胞系观察 BMP 通路信号传导，该细胞系在 BMP基因表达被激活，以6倍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，用于检查反应并拟合一小部分参数 复制实验结果的数学模型。继续筛选配体组合 这种方式的结果导致所需配体筛选的数量呈指数增加。更好的 需要硬件和数学工具来筛选 BMP 通路，以更好地理解混杂性 信号现象。该项目旨在开发一种液滴微流控装置，即 DropShop 平台， 可以以高通量的方式筛选BMP配体组合。为此，需要粘附上皮乳腺 腺鼠 BMP 反应细胞系将通过一种新型方法适应液滴微流体筛选 使用微载体进行细胞培养的方法。 DropShop平台的液滴微流控将得到优化 使用新颖的细胞培养方法。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进行演示 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 展示能够自动确定液滴微流体装置的概念证明 混杂信号通路中的机制模型及其参数。