Enter the text here that is the new abstract information for your application. This section must be no longer than 30 lines of text. 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 computational tools are needed to screen the BMP pathway to better understand promiscuous signaling phenomena. This project aims to develop machine learning tools to quantify our uncertainty of cell signaling systems such as the BMP pathway and reduce the number of experiments required to understand cell systems. To do this, 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 computational tools will demonstrate how to optimally collect experimental data in biological systems while quantifying uncertainty of underlying biological models. These methods can be more broadly translated to systems biology models outside of the BMP pathway.