MS05 - OTHE-02

Emerging Technologies in Biomedical Computational Modeling and Measurement

Wednesday, July 16 at 10:20am

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Joanna Wares (University of Richmond), Luis Melara, Shippensburg University

Description:

This special session explores cutting-edge computational and measurement techniques across diverse biomedical domains. Presentations will address critical methodological challenges and innovative approaches in biological sensing, virtual clinical research, quantum mechanics in population dynamics, and substance identification. The session highlights interdisciplinary strategies for advancing scientific understanding through sophisticated technological interventions, ranging from nanoscale device optimization to quantum computational modeling and advanced substance identification protocols.

Luis Melara

Shippensburg University

"Optimal Bandwith Selection in Bio-FET Measurements"

The use of stochastic regression to separate signal from noise produced by Bio-FETs will be discussed in this talk. The noise realized by BioFETs interferes with quantitative and qualitative analysis, thus determining optimal bandwidth associated with experimental Bio-FET data measurements is an important task. Presented results suggest consistent across aspect rations and a choice of stochastic regression kernel function and yield what appear to be good results.

Joanna R. Wares

University of Richmond

"Comparison of Virtual Clinical Trial Techniques"

Virtual clinical trials (VCTs) are growing in popularity as a tool for quantitatively predicting heterogeneous treatment responses across a population. In the context of a VCT, a plausible patient is an instance of a mathematical model with parameter (or attribute) values chosen to reflect features of the disease and response to treatment for that particular patient. In a previous work, we rigorously quantified the impact that VCT design choices have on VCT prediction. We found that the prior distribution, rather than the inclusion/exclusion criteria, has a larger impact on the heterogeneity of the plausible population. Yet, the percent of treatment responders in the plausible population was more sensitive to the inclusion/exclusion criteria utilized. Here I discuss past results and preview a new study that seeks to understand how the underlying complexity of the chosen mathematical model affects the results of virtual clinical trials.

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Annual Meeting for the Society for Mathematical Biology, 2025.