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.

项目摘要 某些细胞信号系统通过杂交信号的机制运行，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之前，要进行单个受体。 细胞中存在的混杂信号传导系统在许多不同类型的生物过程中普遍存在 开发和维持，包括疾病，包括癌症。骨形态发生途径（BMP）是 理想的杂交信号通路学习途径，因为在10个不同的BMP配体中，充当生长因子， 每种竞争性都与途径的I型或II型受体结合。最近的工作创造了数学 BMP途径内混杂相互作用的模型，能够复制实验 通过给出BMP响应细胞系来观察BMP途径信号传导，该细胞系在表达YFP时 BMP基因表达被激活为6倍BMP配体滴定系列。但是，先前的结果依赖 BMP途径的基质组合筛选检查响应并适合参数的一小部分 在复制实验结果的数学模型中。继续筛选配体的组合 以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途径以更好地理解混杂 信号现象。该项目旨在开发液滴微流体设备，即Dropshop平台， 可以以高通量方式筛选BMP配体组合。为此，粘附的上皮乳房 腺体鼠BMP响应细胞系将通过小滴微流体通过新颖 使用微载体的细胞培养方法。 Dropshop平台的液滴微流体将被优化 使用新型细胞培养方法。在某种细胞类型中筛选BMP配体的原理证明 将在此系统中使用荧光测量系统在此系统中进行证明 吞吐量液滴微流体筛选。最后，将开发机器学习方法以优化 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。结果系统将 演示能够自​​动确定的液滴微流体设备的概念证明 机械模型及其参数在混杂的信号通路中。