MS06 - MFBM-11

Women in Mathematical Biology

Thursday, July 17 at 10:20am

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Margherita Maria Ferrari (University of Manitoba), Daniel Cruz, University of Florida

Description:

Interdisciplinary work involving mathematics and biology has effectively driven advancements in both fields through novel methods, results, and open questions. And though mathematical biology is traditionally associated with differential equations, several other areas of mathematics have been implemented to further our understanding of biology over the last few decades, including algebra, topology, and combinatorics. This session will focus on both the diversity of the areas of mathematics applied to biology and the diversity of the researchers engaged in this work. To highlight the breadth of these mathematical approaches, topics will include differential equations, graph theory, and machine learning. These approaches are applied to genomics, epidemiology, and other areas in biology. Moreover, we have invited a group of junior and senior speakers who belong to under-represented minorities in STEM in order to emphasize the diversity of researchers within the mathematical biology community.

Stacey Smith?

The University of Ottawa

"The implications of micro-host--pathogen co-evolutionary outcomes on macro-epidemics"

Host defence and pathogen virulence both interplay and mutually influence the evolutionary processes of each another. Host–pathogen co-evolutionary outcomes have potentially significant impacts on population dynamics and vice versa. To investigate host–pathogen interactions and explore the impact of micro-level co-evolutionary outcomes on macro-level epidemics, we develop a co-evolutionary model with a mixed host-defence strategy. Our results illustrate that host–pathogen co-evolution may induce infection cycling and lead to the vanishing of the disease-induced hydra effect, whereas pathogen mono-evolution strengthens the hydra effect in both range and magnitude. As the recovery rate increases, we find a counter-intuitive effect of increased disease prevalence due to host–pathogen co-evolution: the disease is first highly infectious and lethal, then highly infectious but with low lethality. Such diverse outcomes suggest that this combined co-evolutionary and epidemiological framework holds great promise for a better understanding of disease infection.

Morgan Craig

Université de Montréal

"Age-related variability in antibody responses to the mRNA COVID-19 vaccine primary series"

Immunological heterogeneity, driven by a variety of factors including e.g., age and sex, heavily influences vaccine outcomes. To better understand this variability, we recently developed a mechanistic mathematical model describing the generation and maintenance of humoral immunity after the mRNA COVID-19 vaccine primary series. By fitting our model to a clinical cohort of younger health care workers and seniors, we disentangled the mechanisms driving weaker antibody responses and faster antibody waning in older adults. Based on these results, we outlined vaccine strategies tailored to key characteristics driving outcomes using an approach rooted in computational immunology.

Chris Soteros

University of Saskatchewan

"Lattice polygon models of DNA topology"

The field of DNA Topology includes the study of DNA geometry (supercoiling) and topology (knots and links) and their effects on DNA in vitro and in vivo. Statistical physics-based lattice models of DNA have proved useful for addressing many questions arising from DNA topology experiments. In this talk I will review recent advances we have made using lattice polygon models to address questions related to the knot and link statistics of DNA in vitro either subject to varying salt conditions or under nanochannel-like confinement.

Margherita Maria Ferrari

University of Manitoba

"Discrete models for DNA-RNA complexes"

R-loops are three-stranded structures formed by a DNA-RNA hybrid and a single strand of DNA, often appearing during transcription. Experimental works show that R-loops can threaten genome integrity, while also playing regulatory roles in biological processes. In this talk, we introduce a model for R-loops based on formal grammars, that are systems to generate words widely applied in molecular biology. The model is trained on experimental data and, despite not including topological information, it accurately predicts R-loop formation on plasmids with varying starting topologies.

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Annual Meeting for the Society for Mathematical Biology, 2025.