Date of Graduation

5-2025

Document Type

Thesis

Degree Name

Bachelor of Science in Chemical Engineering

Degree Level

Undergraduate

Department

Chemical Engineering

Advisor/Mentor

Richardson, William

Abstract

Heart disease is the leading cause of death worldwide for both men and women, and cardiac fibroblasts are critical to heart form and function. Essential roles of cardiac fibroblasts include cell-cell communication, cell-cell signaling, and synthesizing and degrading the extracellular matrix (ECM), which controls structural support and tissue repair of the heart. Abnormal fibroblast signaling can lead to negative impacts, including fibrosis, impaired heart function, and inflammation. A computational model of cardiac fibroblast signaling on Netflux and MATLAB software was adapted and utilized to determine the effect of patient-specific levels of cytokines, hormones, growth factors, peptides, and membrane tension on the ECM. This model integrates 191 reactions and 121 nodes, with 11 inputs that result in 19 outputs describing ECM impacts. Typical values for each of the 11 inputs, as well as standard deviation data produced the 500 random computer-generated patients that were analyzed. Cluster analysis, including hierarchical clustering and principal component analysis (PCA), were performed to determine if certain subgroups of patients had similar ECM responses. PCA analysis provided the clearest clusters, with one cluster of patients resulting in lower ECM responses on average. ECM nodes were split into groups based on average activity level, and a system model on Cytoscape revealed four transcriptional regulation nodes associated with high-activity ECM nodes. In sum, this work advanced our understanding of patient-specific differences in cardiac fibroblast signaling related to fibrotic disease.

Keywords

Heart disease; computational model; Netflux; fibrosis

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