arXiv subjects

Cody E. FitzGerald

Publications and source records attributed to Cody E. FitzGerald.

3 recordsLinked to original sources

arXiv2026

The effect of predation on the dynamics of Chronic Wasting Disease in deer

Chronic Wasting Disease (CWD) is a neurological disease impacting deer, elk, moose, and other cervid populations and is caused by a misfolded protein known as a prion. CWD is difficult to control due to the persistence of prions in the environment. Prions can remain infectious for more than a decade and have been found in soil as well as other environmental vectors, such as ticks and plants. Here, we provide a bifurcation analysis of a simple mathematical model of CWD spread in a cervid population and use a modification of the Gillespie algorithm to explore if predators can be used as an ecological control strategy to limit the spread of the disease in several relevant scenarios. We then use several analytical probabilistic arguments to show that lowering the susceptible population is also beneficial to controlling CWD outbreaks. Finally, we consider a more complex model of CWD spread in a cervid population in which predators are assumed to be dynamic. Here, we find that, again, predators may be used to control CWD outbreaks, assuming they selectively prey upon infected cervids at a sufficiently high rate. From our analysis, we conclude that wolves may be used as an effective control strategy to limit the spread of CWD in cervid populations, and hunting or other means of lowering the susceptible population are beneficial to controlling CWD.

q-bio.PE↗

arXiv2026

Discovering a low-dimensional temperature control architecture across animals

Hibernation is an adaptation to extreme environmental seasonality that has been studied for almost 200 years, but our understanding of the underlying physiological system remains lacking due to the partially observed nature of the system. During hibernation, small mammals, such as the Arctic ground squirrel, exhibit dramatic oscillations in body temperature, typically one of the only physiological states measured, of up to 40 $^{\circ}$C. These spikes are known as interbout arousals and typically occur 10-20 times throughout hibernation. The physiological process that drives interbout arousals is unknown, but two distinct macro-scale mechanisms have been hypothesized. Using model selection for partially observed systems and classical dynamical systems theory, we are able to differentiate between these two hypotheses using only body temperature data recorded from a free-ranging Arctic ground squirrel, and show that our model can capture the broad features of the observed seasonal physiological transitions. We then modify our discovered physiological model of Arctic ground squirrel to include internally-encoded environmental information and find that we can qualitatively match body temperature data recorded from a wide range of species, including a bird, a shrew, and a bear, which also dynamically modulate body temperature. Our results suggest that a low-dimensional, environmentally sensitive core regulator could control body temperature across a diverse range of species -- a new understanding of the physiological organization across species. While the findings presented here are applicable to thermophysiology, the general modeling procedure is applicable to time series data collected from partially observed biological, chemical, physical, mechanical, and cosmic systems for which the goal is to elucidate the underlying mechanism or control structure.

physics.bio-ph↗

arXiv2025

Practical indistinguishability in a gene regulatory network inference problem, a case study

Determining mechanistic models of gene regulation, especially underlying phenotypic variation, is a central goal of both mathematical biology and modern evolutionary biology. However, several challenges, involving both common characteristics of experimental data and the model development process, remain that limit the discovery of general principles. Even the highest-quality experimental data come with challenges. There are always sources of noise, a limit to how often we can measure the system in time, and it is impossible to measure all the relevant states that participate in the full underlying complexity. Additionally, there are usually sources of uncertainty in the underlying biological mechanisms, which give rise to multiple competing model structures. We walk through a case study involving inference of a regulatory network structure involved in a developmental decision in the nematode, \textit{Pristonchus pacificus}. In this study, we fit 13,824 distinct regulatory network models to gene expression data from three experimental conditions to determine which regulatory features are supported by the data. We discover \textit{model sets}, or collections of models with shared regulatory network features that best fit the data, for each of the three experiments we considered, and identify a regulatory network in the intersection of the three model sets. This model describes the data across the experimental conditions and exhibits a high degree of positive regulation and interconnectivity between the key regulators, \textit{eud-1}, \textit{sult-1}, and \textit{nhr-40}. While the biological results are specific to the molecular biology of development in \textit{Pristonchus pacificus}, the comparative modeling framework introduced here can be applied to other systems of gene regulation in an evolutionary developmental context.

q-bio.MN↗