MS02 - OTHE-10

Emerging areas in Mathematical Biology: Celebrating research from the Mathematical Biosciences Institute (Part 1)

Monday, July 14 at 3:50pm

SMB2025 Follow

Note: this minisymposia has multiple sessions. The other session is: and Part-2.

Veronica Ciocanel (Duke University), Hye-Won Kang, University of Maryland Baltimore County

Description:

This minisymposium aims to highlight recent advances in emerging areas and novel mathematical approaches for addressing biological questions. The sessions will cover a broad spectrum of themes that integrate data-driven mathematical modeling and real-world applications. Topics covered include advances in stochastic and continuum modeling of molecular and regulatory mechanisms in cells, network dynamics in infectious disease and ecological systems, and data-driven methods for sleep homeostasis and disorders. These sessions will also celebrate the impactful mathematical biology research of former Mathematical Biosciences Institute (MBI) postdocs and visitors whose career was influenced by their time and connections made while at the MBI.

Scott McKinley

Tulane University

"Robust inference and model selection for particle tracking in live cells"

There is now an expansive collection of mathematical work on building models for the transport of intracellular cargo by molecular motors. Commonly studied cargo undergo “saltatory” motion (bidirectional ballistic motion, intermixed with periods of stationarity) along often unobserved microtubules. Traditionally microparticle transport is quantified in terms of mean-squared displacement, but this ubiquitous statistic averages over periods of motion and pauses, eliminating important biophysical information. In this talk, I will discuss our group’s approach to segmentation analysis: an in-house changepoint detection algorithm coupled with a focus on summary statistics that are robust with respect to the inevitable mistakes that changepoint detection algorithms make.

Peter Kramer

Rensselaer Polytechnic Institute

"Molecular Mechanisms in Actively Driven Passively Crosslinked Microtubule Pairs"

We apply stochastic modeling to interpret in vitro experiments involving microtubules interacting with the passive crosslinker PRC1 while being crowdsurfed by kinesin in a gliding assay configuration. When an antiparallel pair of microtubules is crosslinked by PRC1, the kinesin slides the microtubules apart while the PRC1 resists this separation. We examine molecular-scale mechanisms for the two distinct modes of resistance which are observed in experiments. We further describe a supporting model for how the microtubules being slid by kinesin respond to the load from the PRC1 crosslinkers.

Yangyang Wang

Brandeis University

"A conceptual framework for modeling a latching mechanism for cell cycle regulation"

Two identical van der Pol oscillators with mutual inhibition are considered as a conceptual framework for modeling a latching mechanism for cell cycle regulation. In particular, the oscillators are biased to a latched state in which there is a globally attracting steady-state equilibrium without coupling. The inhibitory coupling induces stable alternating large-amplitude oscillations that model the normal cell cycle. A homoclinic bifurcation within the model is found to be responsible for the transition from normal cell cycling to endocycles in which only one of the two oscillators undergoes large-amplitude oscillations.

Wenrui Hao

Pennsylvania State University

"A Systematic Computational Framework for Practical Identifiability Analysis"

Practical identifiability is a fundamental challenge in data-driven modeling of mathematical systems. In this talk, I will present our recent work on a novel framework for practical identifiability analysis, designed to assess parameter identifiability in mathematical models of biological systems. I will begin with a rigorous mathematical definition of practical identifiability and establish its equivalence to the invertibility of the Fisher Information Matrix. Our framework connects practical identifiability with coordinate identifiability, introducing a novel metric that simplifies and accelerates parameter identifiability evaluation compared to the profile likelihood method. Additionally, we incorporate new regularization terms to address non-identifiable parameters, enhancing uncertainty quantification and improving model reliability. To support experimental design, we propose an optimal data collection algorithm that ensures all model parameters are practically identifiable. Applications to Hill functions, neural networks, and dynamic biological models illustrate the framework’s effectiveness in uncovering critical biological processes and identifying key observable variables.

#SMB2025 Follow

Annual Meeting for the Society for Mathematical Biology, 2025.