Biology subjects

Best, A.

Publications and source records attributed to Best, A..

8 recordsLinked to original sources

bioRxiv2026

Complex epidemiological dynamics driven by the combination of host spatial structure and seasonal forcing

Spatial population structure and seasonality are both central to the spread of many infectious diseases of plants, animals and humans. While seasonal forcing in transmission often plays an important role in epidemiological models of a wide range of infectious disease, and we now have some theoretical understanding of the dynamical impacts of spatial structure, the combined effects of these two ubiquitous processes has not been examined in detail. Here, we develop a novel model to explore the combined influence of spatial structure and temporal variability on disease dynamics. Spatial structure is represented using a lattice-based approach with near-neighbour interactions, while temporal variability is included through regular, seasonal, variation of the transmission rate. We use bifurcation analysis of a pair approximation of the full spatial model to identify the parameter regimes associated with qualitatively distinct dynamical behaviours. The model exhibits a remarkably wide range of complex dynamics, including limit cycles, quasi-periodic cycles, multi-year cycles, chaotic dynamics and bistability between these different states. In particular, complex dynamics occur when reproduction is predominantly local, with the dynamics depending critically on the amplitude of the seasonal transmission rate. We show how high transmission rates, high birth rates and in particular low recovery rates are requirements for complex dynamics. We predict that SI-type disease interactions in plant pathogen systems will show complex dynamics even with relatively global transmission dynamics.

ecology↗

bioRxiv2026

The effects of vertical transmission on a spatially-structured host-parasite model

Vertical transmission of an infectious disease from parent to offspring is a common transmission route in many systems. Here we investigate the dynamics of a pathogen with both horizontal and vertical transmission within a spatially structured population. We introduce a lattice model with a pair approximation that includes both local and global transmission and reproduction. We find that vertical transmission can determine pathogen invasion and reduce the horizontal transmission rate required for invasion. When the majority of transmission and reproduction is local, vertical transmission can destabilise a host population to cause limit cycles. Given the advantages of a pathogen having both horizontal and vertical transmission routes, we extend the model to investigate the likelihood a mutant strain with both transmission modes will outcompete a resident strain with only horizontal transmission. When there is no trade-off the mutant always invades and when there is a trade-off with horizontal transmission, the mutant emerges when the cost to the horizontal transmission rate is not too large. Depending on how the mutant appears within the host population, it may have an initial advantage over the resident strain even if it cannot outcompete in the long-term. Our work demonstrates the potential importance of vertical transmission within host-pathogen dynamics.

ecology↗

bioRxiv2025

Fitting a lattice model with local and global transmission to spread of a plant disease

Understanding, predicting and managing the spread of plant pathogens is crucial given the economic, societal and climatic benefits of plants, including crops and trees. Mathematical models have long been used to investigate disease dynamics in plants. An important component of such models is to account for spatial structure, since plant hosts are immobile and a majority of disease spread will often be localised. Here we apply a lattice-based mathematical modelling approach, a pair approximation, to model disease spread. While this method has previously been used to develop epidemiological theory, it has not been used to predict spread in a specific pathosystem. We fit our lattice-based epidemiological model to experimental data relating to Bahia bark scaling of citrus, an economically-important disease in north-eastern Brazil, and compare its performance to a more commonly used dispersal-kernel modelling approach. We show that the lattice-based model fits the data well, predicting a significant degree of near-neighbour infections, with similar estimated values of epidemiologically-meaningful parameters to the dispersal model. We highlight the pros and cons of the lattice-based approach and discuss how it may be used to predict disease spread and optimise control of plant diseases. Author SummaryPlant diseases can have significant impacts, including reducing crop yields, limiting the availability of natural spaces, and the knock-on effects on our wellbeing. Mathematical models have long been used to understand how disease spreads through plant populations. Here we apply a form of mathematical model that has not previously been specifically applied to a real disease system that emphasises neighbour-to-neighbour spread of infection. In particular, we use the model to explore the spread of Bahia bark scaling of citrus, for which we have excellent experimental data available of its spatial spread. We show that the model fits the data best when there is significant neighbour-to-neighbour spread with very rare long-range infections. We show that this approach agrees well with a more commonly-used mathematical framework and highlight how it might be used to test disease management strategies.

ecology↗

bioRxiv2022

A sterility-mortality tolerance trade-off leads to within-population variation in host tolerance.

While experimental studies have demonstrated within-population variation in host tolerance to parasitism, theoretical studies rarely predict for polymorphism to arise. However, most theoretical models do not consider the crucial distinction between tolerance to the effects of infection-induced deaths (mortality tolerance) and tolerance to the parasite-induced reduction in the reproduction of infected hosts (sterility tolerance). While some studies have examined trade-offs between host tolerance and resistance mechanisms, none has considered a correlation within different tolerance mechanisms. We assume that sterility tolerance and mortality tolerance are directly traded-off in a host population subjected to a pathogen and use adaptive dynamics to study their evolutionary behaviour. We find that such a trade-off between the two tolerance strategies can drive the host population to branch into dimorphic strains, leading to coexistence of strains with sterile hosts that have low mortality and fully fertile with high mortality rates. Further, we find a wider range of trade-off shapes allows branching at intermediate or high infected population size. Our other significant finding is that sterility tolerance is maximised (and mortality tolerance minimised) at an intermediate disease-induced mortality rate. Additionally, evolution entirely reverses the disease prevalence pattern corresponding to the recovery rate, compared to when no strategies evolve. We provide novel predictions on the evolutionary behaviour of two tolerance strategies concerning such a trade-off.

evolutionary biology↗

bioRxiv2022

{Pi}-{Pi} Interactions Stabilize PeptoMicelle-Based Formulations of Pretomanid Derivatives Leading to Promising Therapy Against Tuberculosis in Zebrafish and Mouse Models

Tuberculosis is the deadliest bacterial disease globally, threatening the lives of millions every year. New antibiotic therapies that can shorten the duration of treatment, improve cure rates, and impede the development of drug resistance are desperately needed. Here, we used polymeric micelles to encapsulate four second-generation derivatives of the antitubercular drug pretomanid that had previously displayed much better in vivo activity against Mycobacterium tuberculosis than pretomanid itself. Because these compounds were relatively hydrophobic, we expected that such micellar formulations would increase drug bioavailability, reduce toxicities, and improve therapeutic outcomes. The polymeric micelles were based on polypept(o)ides (PeptoMicelles) and were stabilized in their hydrophobic core by {pi}-{pi} interactions, allowing the efficient encapsulation of aromatic pretomanid derivatives. The stability of these {pi}-{pi}-stabilized PeptoMicelles was demonstrated in water, blood plasma, and lung surfactant by fluorescence cross-correlation spectroscopy and was further supported by prolonged circulation times of several days in the vasculature of zebrafish larvae. The pretomanid derivative with the best in vitro potency against Mycobacterium marinum ("drug D") was also the most efficacious PeptoMicelle formulation tested in the zebrafish larvae infection model, almost completely eradicating the bacteria at non-toxic doses. This lead formulation was further assessed against Mycobacterium tuberculosis in the susceptible C3HeB/FeJ mouse model, which develops human-like necrotic granulomas. Following intravenous administration, the drug D micellar formulation significantly reduced bacterial burden and inflammatory responses in the lungs and spleens of infected mice.

bioengineering↗

bioRxiv2019

High Throughput Evolution of Near Infrared Serotonin Nanosensors

Release and reuptake of neuromodulator serotonin, 5-HT, is central to mood regulation and neuropsychiatric disorders, whereby imaging serotonin is of fundamental importance to study the brains serotonin signaling system. We introduce a reversible near-infrared nanosensor for serotonin (nIRHT), for which synthetic molecular recognition toward serotonin is systematically evolved from ssDNA-carbon nanotube constructs generated from large libraries of 6.9 x 1010 unique ssDNA sequences. nIRHT produces a [~]200% fluorescence enhancement upon exposure to serotonin with a Kd = 6.3 {micro}M affinity. nIRHT shows selective responsivity towards serotonin over serotonin analogs, metabolites, and receptor-targeting drugs, and a 5-fold increased affinity for serotonin over dopamine. Further, nIRHT can be introduced into the brain extracellular space in acute slice, and can be used to image exogenous serotonin reversibly. Our results suggest evolution of nanosensors could be generically implemented to rapidly develop other neuromodulator probes, and that these probes can image neuromodulator dynamics at spatiotemporal scales compatible with endogenous neuromodulation.

neuroscience↗

bioRxiv2017

IMPROVEMENTS TO BAYESIAN GENE ACTIVITY STATE ESTIMATION FROM GENOME-WIDE TRANSCRIPTOMICS DATA

An important question in many biological applications, is to estimate or classify gene activity states (active or inactive) based on genome-wide transcriptomics data. Recently, we proposed a Bayesian method, titled MultiMM, which showed superior results compared to existing methods. In short, MultiMM performed better than existing methods on both simulated and real gene expression data, confirming well-known biological results and yielding better agreement with fluxomics data. Despite these promising results, MultiMM has numerous limitations. First, MultiMM leverages co-regulatory models to improve activity state estimates, but information about co-regulation is incorporated in a manner that assumes that networks are known with certainty. Second, MultiMM assumes that genes that change states in the dataset can be distinguished with certainty from those that remain in one state. Third, the model can be sensitive to extreme measures (outliers) of gene expression. In this manuscript, we propose a modified Bayesian approach, which addresses these three limitations by improving outlier handling and by explicitly modeling network and other uncertainty yielding improved gene activity state estimates when compared to MultiMM.

genomics↗

bioRxiv2017

Whippet: an efficient method for the detection and quantification of alternative splicing reveals extensive transcriptomic complexity

Alternative splicing (AS) is a widespread process underlying the generation of transcriptomic and proteomic diversity in metazoans. Major challenges in comprehensively detecting and quantifying patterns of AS are that RNA-seq datasets are expanding near exponentially, while existing analysis tools are computationally inefficient and ineffective at handling complex splicing patterns. Here, we describe Whippet, a method that rapidly, and with minimal hardware requirements, models and quantifies splicing events of any complexity without significant loss of accuracy. Using an entropic measure of splicing complexity, Whippet reveals that approximately 33% of human protein coding genes contain complex AS events that result in substantial expression of multiple splice isoforms. These events frequently affect tandem arrays of folded protein domains. Remarkably, high-entropy AS events are more prevalent in tumour relative to matched normal tissues, and these differences correlate with increased expression of proto-oncogenic splicing factors. Whippet thus affords the rapid and accurate analysis of AS events of any complexity, and as such will facilitate biomedical research.

genomics↗