Graduate Student Poster Presentation

(Chimezie and Tingting)

Our mathematics department was well represented at the 2025 Mathematics of Data, Dynamics, and Life Sciences (MDDLS) Conference in Irvine, California on 20-21 March 2025. Our current graduate student Chimezie Izuazu presented a poster and our alumna Tingting Tang presented a paper!

(Chimezie with some other conference attendees)

Chimezie received travel support from the NSF-Simons Center for Multiscale Cell Fate Research. He expressed his pleasure in this opportunity to showcase his ongoing PhD work, interact with like-minded researchers, and make new connections. He also wanted to extend his heartfelt gratitude to his mentor and adviser, Dr. Cameron Browne, the mathematics department, and his fellow student Jenita Jahangir for their support.

The Mathematics of Data, Dynamics, and Life Sciences (MDDLS) Conference is a two-day applied mathematics meeting hosted by the NSF-Simons Center For Multiscale Cell Fate at the University of California, Irvine. The goal is to bring together mathematicians specializing in data science, dynamical systems, and other areas of applied mathematics to explore cutting-edge approaches with potential applications in the life sciences.

(Chimezie explaining his research to a fellow researcher)

The title of Chimezie's poster presentation is Stochastic Analysis of Interplay Among Drug Mode of Action, Nutrient Availability, and Antibiotic Resistance. You can see some details in the photo of the poster. The abstract follows for those who want more.

Abstract: Based on published estimates in 2019 (the Lancet), antimicrobial resistance (AMR)-associated illnesses killed more people than HIV/AIDS or malaria. Therefore, AMR is a major public health threat of the 21st century. Understanding AMR evolution is critical for mitigating this issue. Mathematical models, particularly Markov chains, have gained considerable attention for understanding this evolution. The resulting analytical and numerical studies not only validate relevant experimental results but also offer more insight into the rescue mechanism of an organism in deteriorating environments. However, most existing models assume standing genetic variation or the introduction of a new bacterial strain from an external source and do not consider the possibility of pure drug-induced or random mutation of reference bacterial strains. Thus, we propose a pharmacodynamics-based continuous-time Markov chain considering the emergence of a bacterial strain via random or drug-induced mutations. Particularly, the proposed model is a generalized birth--death process with immigration. Besides horizontal gene transfer, the model explicitly captures the stochasticity of de novo emergence of a resistant bacterial strain. Moreover, we incorporate pharmacokinetics, which has been relatively unexplored. Further, we explore the effects of nutrient availability on antibiotic resistance and susceptibility. Using the proposed model, we aim to facilitate precise decision-support tools for antibiotic treatment, such as when to choose a biostatic or biocidal drug and administer low or high doses.