Biology subjects

Flegg, J. A.

Publications and source records attributed to Flegg, J. A..

6 recordsLinked to original sources

bioRxiv2026

Bayesian Parameter Balancing Enables Robust and Consistent Estimation of Kinetic Parameter Uncertainty

MotivationKinetic models are central to systems biology, but enzyme-kinetic parameters compiled from the literature and databases are often incomplete, inconsistent, and measured under heterogeneous conditions. Classical parameter balancing helps infer missing parameters, yet it often lacks calibrated uncertainty, robustness to misspecification, and explicit treatment of source-level heterogeneity. ResultsWe develop a formal Bayesian parameter balancing framework that enforces thermodynamic constraints, estimates full posterior uncertainty, and validates calibration using leave-one-out cross-validation and posterior-predictive coverage. Beyond the classical Gaussian formulation, we introduce robust Student-t and skewed error models to improve reliability under outliers and model misspecification, and incorporate random effects to account for source-level or group-level variability across studies. The resulting approach yields thermodynamically consistent parameter sets with well-calibrated credible intervals on held-out data, offering a Bayesian parameter balancing approach useful to systems biology researchers. Availability and implementationSource code, data, workflows, a Julia package and command-line usage are available at the project GitHub repository. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=112 SRC="FIGDIR/small/726043v2_ufig1.gif" ALT="Figure 1"> View larger version (52K): org.highwire.dtl.DTLVardef@182b3c2org.highwire.dtl.DTLVardef@1e79bdforg.highwire.dtl.DTLVardef@aa56a7org.highwire.dtl.DTLVardef@11f2de2_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2026

REDUCED-PRECISION STOCHASTIC SIMULATION FOR MATHEMATICAL BIOLOGY

AO_SCPLOWBSTRACTC_SCPLOWThe stochastic simulation algorithm (SSA) is widely used to perform exact forward simulation of discrete stochastic processes in biology. However, the computational cost, driven by sequential event-by-event sampling across large ensembles, remains a computational barrier. We investigate whether reduced-precision floating-point arithmetic can accelerate SSA without degrading statistical fidelity, drawing on the success of reduced-precision methods in weather and climate modelling. We evaluate two strategies across five canonical models (birth-death, Schlogl, Telegraph, dimerisation, repressilator): (i) mixed precision, computing propensities in 16-bit while maintaining accumulators in 32-bit; and (ii) uniform precision, performing all arithmetic in 16-bit. Mixed-precision SSA produces ensemble statistics that closely match the 64-bit reference for all models, as measured by Kolmogorov-Smirnov tests and Wasserstein distances. Under uniform precision, deterministic rounding introduces systematic biases across several models, with catastrophic failures in some cases. Stochastic rounding (SR) and propensity normalisation eliminate these biases, restoring distributional fidelity across all models tested (KS p > 0.05). Our results establish mixed-precision SSA with SR as a viable acceleration strategy for mathematical biology: 16-bit formats shrink per-variable data size by 2-4x relative to fp32/fp64, yielding comparable reductions in memory footprint and up to ~ 1.5x wall-clock speedup on CPU hardware that lacks native 16-bit arithmetic. As a hardware-level acceleration, mixed-precision SSA complements algorithmic methods such as tau-leaping and maps naturally onto modern GPU and TPU architectures with native 16-bit arithmetic.

systems biology↗

bioRxiv2025

A mathematical model of metacarpal subchondral bone adaptation, microdamage, and repair in racehorses

Fractures of the distal limb in Thoroughbred racehorses primarily occur because of accumulation of bone microdamage from high-intensity training. Mathematical models of subchondral bone adaptation of the third metacarpal lateral condyles are capable of approximating existing data for Thoroughbred racehorses in training or at rest. To improve upon previous models, we added a dynamic resorption rate and microdamage accumulation and repair processes. Our ordinary differential equation model simulates the coupled processes of bone adaptation and microdamage accumulation, and is calibrated to data on racehorses in training and rest. Sensitivity analyses of our model suggest that joint loads and distances covered per day are among the most significant parameters for predicting microdamage accumulated during training. We also use the model to compare the impact of incremental increasing training programs as horses enter training from a period of rest and maintenance workloads of horses that are race fit on bone adaptation. We find that high-speed training accounts for the majority of damage to the bone. Furthermore, for horses in race training, the estimated rates of bone repair are unable to offset the rate of damage accumulation under a typical Australian racing campaign, highlighting the need for regular rest from training. Nomenclature O_TBL View this table: org.highwire.dtl.DTLVardef@1d155cborg.highwire.dtl.DTLVardef@57a950org.highwire.dtl.DTLVardef@12cd1b9org.highwire.dtl.DTLVardef@17ce916org.highwire.dtl.DTLVardef@1aaaf66_HPS_FORMAT_FIGEXP M_TBL C_TBL

animal behavior and cognition↗

bioRxiv2024

Is testing the bee-all and end-all of varroa eradication?

Varroa destructor is a significant European honeybee pest, impacting agricultural industries globally. Since arriving in 2022, Australia faces the possibility that varroa will become established in European honeybee colonies nationally. Australia initially pursued a strategy of testing and subsequently eliminating hives infested with varroa. These management efforts raise interesting questions about the interplay between hive testing and elimination, and the spread of varroa between hives. This study uses mathematical modelling to investigate how combined hive testing and elimination strategies impact the spread of varroa through a network of European honeybee hives. We develop a model of both within-hive reproduction of varroa and hive testing, and between-hive movement of varroa on a network of hives. This model is used to assess the impact of various testing and hive elimination strategies on the total number of hives eliminated on the network of hives. Each model simulation starts with a single infested hive, and from this we observed one of two dynamics: either the infestation is caught before spreading, or varroa spreads extensively through the network before being caught by testing. Within our model we implement two common hive testing methods - sugar shake and alcohol testing. A shared limitation of these testing methods is that they can only detect mites in a specific stage of their lifecycle. As such, testing is not only dependent on how many varroa mites are in a hive, but what lifecycle stage the mites are in at the time of testing. By varying testing and movement parameters, we see that this testing limitation greatly impacts the number of hives eliminated in various scenarios. We find that there are largely two invasion possibilities: either there is only a small incursion, or that varroa achieves complete spread on the network. Furthermore, testing earlier, or testing more frequently, does not guarantee a smaller invasion. Our model results suggest irregular testing schedules, e.g. testing multiple times in close succession rather than just once in a given timeframe, may help overcome the limitations of common hive testing strategies.

ecology↗

bioRxiv2022

Free and interfacial boundaries in individual-based models of multicellular biological systems

Coordination of cell behaviour is key to a myriad of biological processes including tissue morphogenesis, wound healing, and tumour growth. As such, individual-based computational models, which explicitly describe inter-cellular interactions, are commonly used to model collective cell dynamics. However, when using individual-based models, it is unclear how descriptions of cell boundaries affect overall population dynamics. In order to investigate this we define three cell boundary descriptions of varying complexities for each of three widely used off-lattice individual-based models: overlapping spheres, Voronoi tessellation, and vertex models. We apply our models to multiple biological scenarios to investigate how cell boundary description can influence tissue-scale behaviour. We find that the Voronoi tessellation model is most sensitive to changes in the cell boundary description with basic models being inappropriate in many cases. The timescale of tissue evolution when using an overlapping spheres model is coupled to the boundary description. The vertex model is demonstrated to be the most stable to changes in boundary description, though still exhibits timescale sensitivity. When using individual-based computational models one should carefully consider how cell boundaries are defined. To inform future work, we provide an exploration of common individual-based models and cell boundary descriptions in frequently studied biological scenarios and discuss their benefits and disadvantages.

biophysics↗

bioRxiv2019

Revisiting the Natural History of Pulmonary Tuberculosis: a Bayesian Estimation of Natural Recovery and Mortality rates

BackgroundTuberculosis (TB) natural history remains poorly characterised and new investigations are impossible as it would be unethical to follow up TB patients without treating them. Estimates of TB burden and mortality rely heavily on TB self-recovery and mortality rates, as around 40% of individuals with TB are never detected, making their prognosis entirely dependent on the disease natural history.\n\nMethodsWe considered the reports identified in a previous systematic review of studies from the prechemotherapy era, and extracted detailed data on mortality over time. We used a continuous-time Markov model in a Bayesian framework to estimate the rates of TB-induced mortality and self-cure. A hierarchical model was employed to allow estimates to vary by cohort. Inference was performed separately for smear-positive TB (SP-TB) and smear-negative TB (SN-TB).\n\nResultsWe included 41 cohorts of SP-TB patients and 19 cohorts of pulmonary SN-TB patients in the analysis. No data were available on extrapulmonary TB. The posterior median estimates of the TB-specific mortality rates were 0.390 year-1 (0.329-0.452, 95% credible interval) and 0.025 year-1 (0.016-0.036) for SP-TB and SN-TB patients, respectively. The estimates for self-recovery rates were 0.233 year-1 (0.179-0.293) and 0.147 year-1 (0.087-0.248) for SP-TB and SN-TB patients, respectively. These rates correspond to average durations of untreated TB of 1.57 years (1.37-1.81) and 5.35 years (3.42-8.23) for SP-TB and SN-TB, respectively, when assuming a natural mortality rate of 0.014 year-1 (i.e. a 70-year life expectancy).\n\nConclusionsTB-specific mortality rates are around 15 times higher for SP-TB than for SN-TB patients. This difference was underestimated dramatically in previous TB modelling studies that parameterised models based on the ratio of 3.3 between the 10-year case fatality of SP-TB and SN-TB. Our findings raise important concerns about the accuracy of past and current estimates of TB mortality and predicted impact of control interventions on TB mortality.

bioinformatics↗