Biology subjects

O'Donovan, S.

Publications and source records attributed to O'Donovan, S..

3 recordsLinked to original sources

bioRxiv2026

Uncovering disease progression trajectories across multiple timescales with hybrid ODE-mixture models

Biological systems are capable of evolving across multiple timescales, yet most modeling approaches capture either short-term physiological responses or long-term disease progression. Additionally, methods that combine these time scales either struggle with inter-individual variation, or require large volumes of data to work. We present Latent Class Parameter Trajectory Analysis (LCPTA), a hybrid framework that combines mechanistic ordinary differential equation (ODE) modeling of short-term dynamics with latent class trajectory modeling of long-term parameter evolution. To support this approach, we developed TemporalMixtureModels.jl, a Julia package for univariate and multivariate temporal mixture modeling. We evaluated LCPTA in two synthetic longitudinal studies, covering a simulation of a heterogeneous population developing hypertension, as well as a synthetic longitudinal study of 75 virtual individuals followed over 30 years, representing muscle insulin resistance, liver insulin resistance, and healthy trajectories. We compared LCPTA with a conditional neural ODE and a conditional neural network for reconstruction of long-term parameter trajectories and subgroup separation. In both studies LCPTA recovered relevant subgroup structure and trajectory patterns. In the cardiovascular study, the neural approaches were able to distinguish inter-individual differences, but in the insulin sensitivity study, neural approaches failed to represent parameter trajectories faithfully, primarily separating individuals along a single dominant parameter and showing weaker multivariate trajectory reconstruction. In additional experiments with sparse and irregularly sampled unseen individuals, LCPTA maintained strong classification performance. We show that LCPTA is an efficient and interpretable strategy for multiscale modeling of heterogeneous metabolic disease progression, with potential utility for data-efficient digital twin applications.

systems biology↗

bioRxiv2024

Physiology-informed regularization enables training of universal differential equation systems for biological applications

Systems biology tackles the challenge of understanding the high complexity in the internal regulation of homeostasis in the human body through mathematical modelling. These models can aid in the discovery of disease mechanisms and potential drug targets. However, on one hand the development and validation of knowledge-based mechanistic models is time-consuming and does not scale well with increasing features in medical data. On the other hand, more data-driven approaches such as machine learning models require large volumes of data to produce generalizable models. The integration of neural networks and mechanistic models, forming universal differential equation (UDE) models, enables the automated learning of unknown model terms with less data than the neural network alone. Nevertheless, estimating parameters for these hybrid models remains difficult with sparse data and limited sampling durations that are common in biological applications. In this work, we propose the use of physiology-informed regularization, penalizing biologically implausible model behavior to guide the UDE towards more physiologically plausible regions of the solution space. In a simulation study we show that physiology-informed regularization not only results in a more accurate forecasting of model behaviour, but also supports training with less data. We also applied this technique to learn a representation of the rate of glucose appearance in the glucose minimal model using meal response data measured in healthy people. In that case, the inclusion of regularization reduces variability between UDE-embedded neural networks that were trained from different initial parameter guesses. Author summarySystems biology concerns the modelling and analysis of biological processes, by viewing these as interconnected systems. Modelling is typically done either using mechanistic differential equations that are derived from experiments and known biology, or using machine learning on large biological datasets. While mathematical modelling from biological experiments can provide useful insights with limited data, building and validating these models takes a long time and often requires highly invasive measurements in humans. Efforts to combine this classical technique with machine learning have resulted in a framework termed universal differential equations, where the model equations contain a neural network to describe unknown biological interactions. While these methods have shown success in numerous fields, applications in biology are more challenging due to limited data-availability, high data sparsity. In this work, we have introduced physiology-informed regularization to overcome these instabilities and to constrain the model to biologically plausible behavior. Our results show that by using physiology-informed regularization, we can accurately predict future unseen observations in a simulated example, with much more limited data than a similar model without regularization. Additionally, we show an application of this technique on human data, applying a neural network to learn the appearance of glucose in the blood plasma after a meal.

systems biology↗

bioRxiv2021

Alpha-synuclein alters the faecal viromes of rats in a gut-initiated model of Parkinson's disease

Parkinsons disease (PD) is a chronic neurological disorder associated with the misfolding of alpha-synuclein (-syn) into Lewy body aggregates within nerve cells that contribute to their neurodegeneration. Recent evidence suggests -syn aggregation may begin in the gut and travel to the brain along the vagus nerve, with microbes a potential trigger initiating the misfolding of -syn. However, changes in the gut virome in response to -syn alterations have not been investigated. In this study, we show longitudinal changes in the faecal virome of rats administered either monomeric or preformed fibrils (PFF) of -syn directly into their enteric nervous system. Differential changes in rat viromes were observed when comparing monomeric and PFF -syn. The virome {beta}-diversity changes after -syn treatment were compounded by the addition of LPS as an adjunct. Changes in the diversity of rat faecal viromes were observed after one month and did not resolve within the studys five month observational period. Overall, these results suggest that microbiome alterations associated with PD may, partially, be reactive to host -syn associated changes.

microbiology↗