Jared Noel has received SURF funding for his computational modeling project with Dr. William Richardson

Author: Jared Noel| Major: Chemical engineering, applied mathematics| Semester: Spring 2025

I began conducting research as an undergraduate freshman through the First Year Engineering Program’s Honors Research Experience, where I worked in an experimental materials chemistry lab. This experience taught me the basics of scientific research, but as I took more classes in my applied mathematics major, I became increasingly interested in using computational tools, especially to solve biological problems. This led me to Dr. William Richardson’s lab, where I am personalizing a computational model to predict individual patient responses to myocardial fibrosis. Heart failure is a devastating condition that affects over 6.5 million Americans, and despite advances in treatment, the five-year mortality rate remains high. One reason for this is the condition called myocardial fibrosis, where the tissue of the heart becomes stiff due to excessive accumulation of extracellular matrix proteins, making it harder for the heart to pump blood efficiently. A device called the LVAD (left ventricular assist device) is often used to reduce the stress on the heart and slow this process, but full recovery remains rare. This is largely due to the complex nature of the network of interconnected biochemical and biomechanical stimuli that effect myocardial fibrosis. This complexity is further compounded by individual genetic differences between patients. Advanced computational modeling techniques make it possible to map these complex networks. I utilize these techniques to simulate how heart cells respond to different chemical and mechanical contexts of patients. These simulations help identify personalized drug targets, which can then be further validated using cell cultures. Ultimately, this could lead to tailored treatments for heart failure patients that are based on each individual’s biology.
Because we didn’t yet have real patient data this semester, I simulated it. I ran hundreds of simulations where I randomized the inputs to mimic the diversity we would expect in real patients, and then I analyzed these results as if they were outputs obtained from real patients. To do this, I performed principal component analysis which clustered the responses into groups. The idea is that each group represents a unique response profile that characterizes a set of patients. Then, by analyzing the inputs of the patients in these sets, I could identify input signatures that yield predictable responses. One particularly exciting method I used to achieve these was the Mapper algorithm of topological data analysis, which helps to visualize high-dimensional data in a network form. This technique allowed me to build a network, where the nodes and connections represented relationships among input types, and the network’s orientation in space represents the output responses. This method can help to reveal complex hidden structures in the data that traditional methods do not.
Next semester, I will be continuing this work. By then, we will have actual patient data to use, and I can use this data instead of the randomized inputs to run my simulations. Then, I can use the same computational framework to analyze actual patient responses. After identifying promising drug targets for individual patients, I can begin to perform experimental validation using in vitro cell cultures. I can culture cells to simulate the patient contexts from the models and then record the concentrations of outputted proteins after the culture period. Comparing these results to those predicted by the model will provide additional insights into the efficacy of the model and the potential for this framework to predict drug targets for real patients. This research has not only deepened my understanding of computational biology but has also shaped my career goals. I now plan to pursue a PhD in computational biology, focusing on the interdisciplinary field of computational genomics. It’s incredibly fulfilling to know that my work could one day help tailor therapies for real patients, giving them a better chance at recovery and a better quality of life.