BME PhD Preliminary Exam announcement for Alexa Petrucciani (E. Pienaar, advisor)

Everyone is invited to attend the public presentation beginning at 8:00am.

Research Title: Agent-Based Modeling of Cell Culture Granuloma Models: Separating the Impact of Dimension, Collagen, and Non-Immune Cells

Date: Wednesday, January 18th, 2023

Time: 8:00am

Location: MJIS 2001 and with the following zoom link:

https://purdue-edu.zoom.us/j/9607220370

Thesis Committee:

• Dr. Elsje Pienaar - Major Professor
• Dr. Tamara Kinzer-Ursem
• Dr. Nan Kong
• Dr. Nelita du Plessis
• Dr. Bradley Allen

Abstract: 

Tuberculosis (TB) remains a global public health crisis, causing over 10 million new infections and 1.6 million deaths in 2021 alone. TB is caused by Mycobacterium tuberculosis (Mtb), which is spread via respiratory droplets. After inhalation, the bacteria initiate heterogeneous pathology in the lungs, including granulomas and cavities. Granulomas are organized structures of immune cells, traditionally though to contain the bacteria, and cavities are pathological spaces are caused by the destruction of extracellular matrix (ECM), which can worsen disease outcomes and cause long-lasting pulmonary impairment. In vitro methods are commonly used to study host-pathogen interactions in Mtb infection, and recent developments have led to more advanced models that represent the TB granuloma environment more closely than the traditional cell culture counterparts. These advances include the development of 3D models, the inclusion of physiological ECM components like collagen, and the co-culturing of immune cells with neighboring nonimmune cells. Increasing complexity has been accomplished in a piece-wise manner – minimally necessary components are included to minimize cost while maintaining throughput and tractability. This creates a need for tools to analyze these systems and, more importantly, integrate the independent data created. We propose using agent-based modeling of in vitro Mtb infection models to separate out the contributions of dimension, collagen, and neighboring cells to clinically relevant outputs: bacterial load and ECM destruction. The model can provide insights into the role of granuloma structure, elucidate the emergence of cavities, predict drug targets, and generate and test hypotheses in tandem with in vitro models.