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

> **NIH NIH F31** · UNIVERSITY OF CALIFORNIA-IRVINE · 2023 · $41,294

## Abstract

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.

## Key facts

- **NIH application ID:** 10553603
- **Project number:** 5F31GM145188-02
- **Recipient organization:** UNIVERSITY OF CALIFORNIA-IRVINE
- **Principal Investigator:** Vincent David Zaballa
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $41,294
- **Award type:** 5
- **Project period:** 2022-01-01 → 2024-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10553603

## Citation

> US National Institutes of Health, RePORTER application 10553603, Characterization of a Droplet Microfluidic High Throughput Screening Device and Developing Machine Learning Algorithms to Study the Bone Morphogenetic Protein Signaling Pathway (5F31GM145188-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10553603. Licensed CC0.

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