Biology subjects

Lambert, B.

Publications and source records attributed to Lambert, B..

At least 19 recordsLinked to original sources

bioRxiv2026

Heatwaves and turbidity influxes have interacting and lasting effects on the foraging behaviour of three-spined stickleback Gasterosteus aculeatus

Freshwater ecosystems support a disproportionately high share of fish species and biodiversity yet are among the most threatened ecosystems globally and are increasingly being exposed to multiple, co-occurring environmental stressors. In addition, predicting the combined impacts of environmental stressors remains a major challenge, particularly when stressors differ in how quickly they arise and how long their effects persist. Understanding how these stressors interact across time is critical for predicting their ecological consequences. Here, we examine how warming and turbidity, two widespread and intensifying stressors, interact to influence predator-prey dynamics in the three-spined stickleback (\textit{Gasterosteus aculeatus}). Using a fully factorial experiment, fish were exposed to ambient, warmed, turbid, or combined conditions for short (one week) or prolonged (six weeks) periods, before feeding rates were quantified across prey densities using a novel stochastic Bayesian model. We found that foraging behaviour was shaped primarily by prior exposure rather than the immediate experimental conditions. Short-term exposure to both stressors increased feeding rates, whereas prolonged warming reduced them. In contrast, turbidity continued to increase feeding rates over both time periods and, when combined with warming, offset the negative effects of extended heat exposure. Changes in feeding performance at the individual level may propagate through food webs, potentially triggering trophic cascades that reduce biodiversity. Given the exceptional biodiversity of freshwater systems, such dynamics are of particular concern. We also found that behavioural changes persisted even after stressors were removed, indicating that exposure history can have lasting effects. These findings suggest that short-term experiments, such as the functional response, may actually be measuring subject experiential history rather than responses to specific experimental conditions.

animal behavior and cognition↗

bioRxiv2026

The PEARL toolkit: Using sand flies to identify leishmaniasis animal reservoirs

Animal reservoirs remain unknown in emerging and most endemic leishmaniasis foci hindering control efforts. Here, we developed a field-applicable PERL (Phlebotomines Establish Reservoirs of Leishmania) toolkit using individual blood fed sand flies (IBF) to identify leishmaniasis reservoirs. Using IBF, we optimized DNA and RNA co-extraction and parasite detection of [≥]1 parasite/s by kDNA qPCR and ssu rRNA RT-qPCR. We then screened IBF for expression of two parasite genes, sherp and HPB, identified by RNAseq as having low-to-absent expression in IBF given a first Leishmania donovani-infected blood meal (IBF-iBM1) and high expression in specimens provided subsequent uninfected blood meals (IBF-BMS+). Linear discriminant analysis of target gene expression classified iBM1 parasites with a predictive accuracy of ~87% and ~82% in membrane- or naturally-fed on hamsters sand flies, respectively. By determining the blood source in specimens determined as IBF-iBM1, the PERL toolkit provides an innovative and practical approach to identification of leishmaniasis reservoirs.

Molecular Biology↗

bioRxiv2026

A Bayesian modelling framework for inference of latent infection risk patterns from virus neutralisation assay titration data

Serological assays remain the standard approach for estimating the cumulative incidence of a pathogen and monitoring population immunity. Titration data from virus neutralisation assays are conventionally analysed using a nearly century-old interpolation-based method that neglects inherent imperfections in the assay and produces estimates with no measure of uncertainty. We introduce a two-part Bayesian framework for inferring latent infection risk directly from raw titration data. First, we develop a mechanistic model for serum antibody titration data that estimates antibody concentrations while quantifying uncertainty. Second, we propagate this uncertainty into an age-structured serocatalytic mixture model by integrating over posterior draws of individual antibody concentrations, allowing joint inference on latent serostate membership, force of infection, and serological waning rate. We applied the framework to three cross-sectional serosurveys of the population of England, for enteroviruses A71 (EV-A71) and D68 (EV-D68) and coxsackievirus A6 (CVA6), which are leading causes of severe respiratory illness and hand, foot, and mouth disease. We estimated consistently higher and more persistent lifetime antibody concentrations for EV-D68 than for EV-A71 and CVA6. The proportion of recently infected individuals peaks around 40% by age 5 years for CVA6 and around 32% by age 7 years for EV-A71, before declining with age. These estimates are conditional on a model structure excluding complete seroreversion, which, where identifiable, implied a half-life of several decades. For EV-D68, the inferred proportion previously infected exceeded 50% by age 13 years and grew, with the force of infection declining more gradually with age. These estimates differ from those previously obtained from binarised versions of the same data. Our framework uncovers the wide-ranging variation in antibody levels that are often obscured by conventional endpoint titre methods and infers infection rates directly from raw titration data, without dichotomising at predetermined seropositivity cut-offs, while making minimal assumptions about virus-specific infection mechanisms.

bioinformatics↗

bioRxiv2025

MetaBeeAI: an AI pipeline for full-text systematic reviews in biology

The volume and complexity of scientific literature are expanding rapidly, making it increasingly difficult to extract and synthesise information across studies. This challenge is particularly acute in the biological sciences, where evidence spans multiple levels of organisation and heterogeneous experimental designs. Large Language Model (LLM) pipelines offer a scalable route to evidence synthesis, but many existing approaches lack transparency, modularity, and effective mechanisms for human oversight. We present MetaBeeAI, an open-source, modular pipeline that integrates established LLM techniques into a coherent, auditable workflow for structured data extraction in biology. MetaBeeAI combines modular prompting, multi-pass extraction, and expert-in-the-loop validation within an interface that presents model outputs alongside source text, enabling inspection, correction, and iterative refinement. The pipeline produces machine-readable records of prompts, configurations, and expert annotations, supporting reproducibility and continuous improvement. We apply MetaBeeAI to 924 research papers on bees and pesticides, extracting structured information on species, compounds, exposure designs, and experimental context. Evaluation demonstrates improved consistency, convergence with expert judgement, and robustness across heterogeneous biological studies, highlighting the value of expert-guided refinement. MetaBeeAI provides a transparent and extensible framework for scalable evidence synthesis, supporting reliable integration of LLMs into biological research workflows.

ecology↗

bioRxiv2025

Modelling the effects of adult emergence on the surveillance and age distribution of medically important mosquitoes

Entomological surveillance is an important component of mosquito-borne disease control. Mosquito abundance, infection prevalence and the entomological inoculation rate are the most widely reported entomological metrics, although these data are notoriously noisy and difficult to interpret. For many infections, only older mosquitoes are infectious, which is why, in part, vector control tools that reduce mosquito life expectancy have been so successful. The age structure of wild mosquitoes has been proposed as a metric to assess the effectiveness of interventions that kill adult mosquitoes, and age grading tools are becoming increasingly advanced. Mosquito populations show seasonal dynamics with temporal fluctuations. How seasonal changes in adult mosquito emergence and vector control could affect the mosquito age distribution or other important metrics is unclear. We develop stochastic mathematical models of mosquito population dynamics to show how variability in mosquito emergence causes substantial heterogeneity in the mosquito age distribution, with low frequency, positively autocorrelated changes in emergence being the most important driver of this variability. Fitting a population model to mosquito abundance data collected in experimental hut trials indicates these dynamics are likely to exist in natural Anopheles gambiae populations. Incorporating age structuring into an established compartmental model of mosquito dynamics and vector control, indicates that the use of mosquito age as a metric to assess the efficacy of vector-control tools will require an understanding of underlying variability in mosquito ages, with estimates affected by short-term and seasonal fluctuations in mosquito emergence.

ecology↗

bioRxiv2024

Integrating data from across insecticide resistance bioassay types using a mechanistic model incorporating mosquito genetic variation and behaviour

Malaria claims approximately 500,000 lives each year, and insecticide-treated nets (ITNs), which kill mosquitoes that transmit the disease, remain the most effective intervention. However, resistance to pyrethroids, the primary insecticide class used in ITNs, has risen dramatically in Africa, making it difficult to assess the current public health impact of pyrethroid-ITNs. Past work has modelled the relation between pyrethroid susceptibility measured in discriminating-dose susceptibility bioassays and ITN effectiveness in experimental hut trials. Here, we introduce a new predictive approach that accounts for heterogeneity in insecticide resistance within wild mosquito populations, for example, due to genetic variability, by incorporating data from newly recommended intensity-dose susceptibility bioassays. We fit our mathematical model to a comprehensive data set that combines discriminating dose bioassays from all over Africa, intensity dose bioassays from Burkina Faso, and concurrent experimental hut trials. Our analysis estimates location- and insecticide-specific variation in resistance heterogeneity in Burkina Faso and quantifies differences in insecticide exposure in bioassays and experimental huts. By providing a mechanistic understanding of these experimental data, our approach could be integrated into malaria transmission models to account for the public health impact of insecticide resistance detected by surveillance programmes. Author summaryBednets treated with insecticides that kill mosquitoes have been responsible for major reductions in malaria burden over recent decades. However, the spread of insecticide resistance in mosquito populations threatens to slow or reverse these gains. It is therefore important to be able to gauge insecticide resistance in local mosquito populations and the remaining effectiveness of bednets. Important tools for quantifying insecticide resistance include susceptibility bioassays, which expose mosquitoes to specific insecticide doses and measure mortality, and experimental hut trials, which aim to mimic how mosquitoes interact with insecticides on bednets in the field. Here, we develop a mathematical model that incorporates mechanistic features of how mosquitoes interact with insecticides in both types of experiments. We show that this model can accurately predict mosquito mortality in experimental hut trials using data from intensity-dose susceptibility bioassays. These bioassays are more sensitive than traditional discriminating-dose assays while being less costly and logistically demanding than experimental hut trials. Our model provides a more granular understanding of insecticide resistance measurements and could be embedded into malaria transmission models to predict the public health impact of insecticide resistance measured by surveillance programmes.

bioinformatics↗

bioRxiv2024

Identification of early-stage liver fibrosis by modifications in the interstitial space diffusive microenvironment using fluorescent single-walled carbon nanotubes

During liver fibrosis, recurrent hepatic injuries lead to accumulation of collagen and other extracellular matrix components in the interstitial space, ultimately disrupting liver functions. Early stages of liver fibrosis may be reversible, but opportunities for diagnosis at these stages are currently limited. Here, we show that the alterations of the interstitial space associated with fibrosis can be probed by tracking individual fluorescent single-walled carbon nanotubes (SWCNTs) diffusing in that space. In a mouse model of early liver fibrosis, we find that nanotubes generally explore elongated areas, whose length decrease as the disease progresses even in regions where histopathological examination does not reveal fibrosis yet. Furthermore, this decrease in nanotube mobility is a purely geometrical effect, as the instantaneous nanotube diffusivity stays unmodified. This work establishes the promise of SWCNTs both for the diagnosis of liver fibrosis at an early stage, and for more in-depth studies of the biophysical effects of the disease.

cancer biology↗

bioRxiv2023

Investigating the dose-dependency of the midgut escape barrier using a mechanistic model of within-mosquito dengue virus population dynamics

Flaviviruses are arthropod-borne (arbo)viruses which can emerge rapidly and cause explosive epidemics of severe disease. Some of the most epidemiologically important flaviviruses, including dengue virus (DENV), Zika virus (ZIKV) and yellow fever virus (YFV), are transmitted by Aedes mosquitoes, most notably Aedes aegypti and Aedes albopictus. After a mosquito blood feeds on an infected host, virus enters the midgut and infects the midgut epithelium. The virus must then overcome a series of barriers before reaching the mosquito saliva and being transmitted to a new host. The virus must escape from the midgut (known as the midgut escape barrier; MEB), which is thought to be mediated by transient changes in the permeability of the midgut-surrounding basal lamina layer (BL) following blood feeding. Here, we present a mathematical model of the within-mosquito population dynamics of flaviviruses that includes the interaction of the midgut and BL which can account for the MEB. Our results indicate a dose-dependency of midgut establishment of infection as well as rate of escape from the midgut: collectively, these suggest that the extrinsic incubation period (EIP) - the time taken for DENV virus to be transmissible after infection - is shortened when mosquitoes imbibe more virus. Additionally, our experimental data indicates that multiple blood feeding events, which more closely mimic mosquito-feeding behavior in the wild, can hasten the course of infections, and our model predicts that this effect is sensitive to the amount of virus imbibed. Our model indicates that mutations to the virus which impact its replication rate in the midgut could lead to even shorter EIPs when double-feeding occurs. Mechanistic models of within-vector viral infection dynamics provide a quantitative understanding of infection dynamics and could be used to evaluate novel interventions that target the mosquito stages of the infection. Author summaryAedes mosquitoes are the main vectors of dengue virus (DENV), Zika virus (ZIKV) and yellow fever virus (YFV), all of which can cause severe disease in humans with dengue alone infecting an estimated 100-400 million people each year. Understanding the processes that affect whether, and at which rate, mosquitoes may transmit such viruses is, hence, paramount. Here, we present a mathematical model of virus dynamics within infected mosquitoes. By combining the model with novel experimental data, we show that the course of infection is sensitive to the initial dose of virus ingested by the mosquito. The data also indicates that mosquitoes which blood feed subsequent to becoming infected may be able to transmit infection earlier, which is reproduced in the model. This is important as many mosquito species feed multiple times during their lifespan and, any reduction in time to dissemination will increase the number of days that a mosquito is infectious and so enhance the risk of transmission. Our study highlights the key and complementary roles played by mathematical models and experimental data for understanding within-mosquito virus dynamics.

systems biology↗

bioRxiv2023

Prediction of mycotoxin response of DNA-wrapped nanotube sensor with machine learning

DNA-wrapped single-walled carbon nanotubes (DNA-SWCNTs) have demonstrated great versatility as optical sensors. SWCNTs emit a near-infrared fluorescence that is responsive to the slightest changes in their environment, enabling the creation of sensors that can respond to single-molecule fluctuations within the vicinity of their surfaces. The fluorescence response and surface interactions of these sensors are determined by the DNA wrapping sequence. However, the lack of information on the relationship between the DNA sequence and its effect on the SWCNT fluorescence remains a bottleneck for designing sensors specific to analytes of interest. The use of directed evolution was recently demonstrated in order to evolve SWCNT sensors towards mycotoxins through iterative cycles of DNA mutation, screening and selection. In the current work, we use the data acquired during the directed evolution of DNA-SWCNT sensors to train machine learning (ML) algorithms. Artificial neural network (ANN) and support vector machine (SVM) methods were used to predict the response of DNA-SWCNT sensors to the mycotoxin. The reliability of the models was assessed through cross-validation. The cross-validated ANN and SVM models were able to accurately classify the various DNA-SWCNTs as yielding either a high or low fluorescence response with an accuracy of 73 and 81%, respectively. The models were further tested on alternative similar and dissimilar DNA sequences outside of the initial training dataset. The ANN model showed a better ability to predict dissimilar DNA sequences resulting in a high sensor response in comparison with the SVM model. In addition, the possibility to combine the two SVM and ANN models with directed evolution methods was investigated. The experimental results showed that the SVM model was able to predict the response of DNA-SWCNT sensors with 95% accuracy. Finally, the Hierarchy and k-means++ clustering methods were used to examine the similarity and dissimilarity of each DNA sequence at every stage of our investigation. In this work, we show that the application of ML algorithms to directed evolution libraries of DNA allows one to accurately map the performances of DNA-SWCNT sensors within a particular DNA sequence space. Moreover, the computational success of this mapping provides a framework for replacing current empirical approaches with the rational design of DNA sequences for SWCNT sensing.

bioengineering↗

bioRxiv2023

Temperature and transmission: novel estimates of the effects of temperature on the human malaria parasite, Plasmodium falciparum

Malaria transmission is strongly influenced by traits such as mosquito life expectancy, vector competence, and the Extrinsic Incubation Period (EIP, which is the development time of the parasite inside the mosquito). Even though these traits are known to be shaped by environmental temperature and there is considerable concern that climate change could increase disease risk in certain areas, surprisingly, these temperature dependencies are poorly characterized. Here, we use a mechanistic model fitted to novel experimental data to describe how Plasmodium falciparum infection of the African malaria vector, Anopheles gambiae, is modulated by temperature, including its influences on parasite establishment, conversion efficiency through parasite developmental stages, EIP and overall competence. We use these data, together with novel estimates of the survival of infected blood-fed mosquitoes, to explore the potential influence of temperature on transmission in four locations in Kenya, considering recent conditions and future climate change. Results provide insights into factors limiting transmission in cooler environments and indicate that increases in malaria transmission efficiency due to climate warming in areas like the Kenyan Highlands, might be less than previously predicted.

microbiology↗

bioRxiv2023

Simulating clinical trials for model-informed precision dosing: Using warfarin treatment as a use case

Treatment response variability across patients is a common phenomenon in clinical practice. For many drugs this inter-individual variability does not require much (if any) individualisation of dosing strategies. However, for some drugs, including chemotherapies and some monoclonal antibody treatments, individualisation of dosages are needed to avoid harmful adverse events. Model-informed precision dosing (MIPD) is an emerging approach to guide the individualisation of dosing regimens of otherwise difficult-to-administer drugs. Several MIPD approaches have been suggested to predict dosing strategies, including regression, reinforcement learning (RL) and pharmacokinetic and pharmacodynamic (PKPD) modelling. A unified framework to study the strengths and limitations of these approaches is missing. We develop a framework to simulate clinical MIPD trials, providing a cost and time efficient way to test different MIPD approaches. Central for our framework is a clinical trial model that emulates the complexities in clinical practice that challenge successful treatment individualisation. We demonstrate this framework using warfarin treatment as a use case and investigate three popular MIPD methods: 1. neural network regression; 2. deep RL; and 3. PKPD modelling. We find that the PKPD model individualises warfarin dosing regimens with the highest success rate and the highest efficiency: 75.1% of the individuals display INRs inside the therapeutic range at the end of the simulated trial; and the median time in the therapeutic range (TTR) is 74 %. In comparison, the regression model and the deep RL model have success rates of 47.9% and 65.8 %, and median TTRs of 45 % and 68 %. We also find that the MIPD models can attain different degrees of individualisation: the Regression model individualises dosing regimens up to variability explained by covariates; the Deep RL model and the PKPD model individualise dosing regimens accounting also for additional variation using monitoring data. However, the Deep RL model focusses on control of the treatment response, while the PKPD model uses the data also to further the individualisation of predictions.

pharmacology and toxicology↗

bioRxiv2022

Filter inference: A scalable nonlinear mixed effects inference approach for snapshot time series data

Variability is an intrinsic property of biological systems and is often at the heart of their complex behaviour. Examples range from cell-to-cell variability in cell signalling pathways to variability in the response to treatment across patients. A popular approach to model and understand this variability is nonlinear mixed effects (NLME) modelling. However, estimating the parameters of NLME models from measurements quickly becomes computationally expensive as the number of measured individuals grows, making NLME inference intractable for datasets with thousands of measured individuals. This shortcoming is particularly limiting for snapshot datasets, common e.g. in cell biology, where high-throughput measurement techniques provide large numbers of single cell measurements. We extend earlier work by Hasenauer et al (2011) to introduce a novel approach for the estimation of NLME model parameters from snapshot measurements, which we call filter inference. Filter inference is a new variant of approximate Bayesian computation, with dominant computational costs that do not increase with the number of measured individuals, making efficient inferences from snapshot measurements possible. Filter inference also scales well with the number of model parameters, using state-of-the-art gradient-based MCMC algorithms, such as the No-U-Turn Sampler (NUTS). We demonstrate the properties of filter inference using examples from early cancer growth modelling and from epidermal growth factor signalling pathway modelling. Author summaryNonlinear mixed effects (NLME) models are widely used to model differences between individuals in a population. In pharmacology, for example, they are used to model the treatment response variability across patients, and in cell biology they are used to model the cell-to-cell variability in cell signalling pathways. However, NLME models introduce parameters, which typically need to be estimated from data. This estimation becomes computationally intractable when the number of measured individuals - be they patients or cells - is too large. But, the more individuals are measured in a population, the better the variability can be understood. This is especially true when individuals are measured only once. Such snapshot measurements are particularly common in cell biology, where high-throughput measurement techniques provide large numbers of single cell measurements. In clinical pharmacology, datasets consisting of many snapshot measurements are less common but are easier and cheaper to obtain than detailed time series measurements across patients. Our approach can be used to estimate the parameters of NLME models from snapshot time series data with thousands of measured individuals.

bioinformatics↗

bioRxiv2022

The influence of selection, drift and immigration on the diversity of a tropical tree community

Ecology is rich in theories that aim to explain why natural communities have as many species as they do. Neutral theory, for example, supposes that a communitys diversity depends on the rate at which it gains species by immigration or speciation and loses them to ecological drift [1-5]. Classical niche theory, by contrast, supposes that diversity is regulated by the complexity of the environment: how many dimensions of resources it has and how finely species can subdivide them [6-10]. These theories are about levels of diversity at equilibrium. But non-equilibrium theory supposes that communities are perpetually buffeted by environmental change so that communities rarely contain all the individuals and species they might [11, 12]. When that happens, some species may profit from their immediate circumstances, but their gains are short lived as the environment changes again, favouring others. Such theories are often seen as competing visions of nature (e.g., [1, 2, 13-20]), but they can also be viewed as collectively describing a set of forces, any of which may be at work at a given time and place (cf. [21]). The relative importance of these forces in shaping the evolution of a communitys diversity can be captured by a small set of parameters: the communitys effective size, Ne, the rate at which it gains new species, , and the magnitude and form of species-specific selection coefficients, s [22]. Here we present a way of estimating these parameters using time series data and apply it to the famous Barro Colorado Island Neotropical forest dataset. We show that, for the last thirty years, this community has been dominated by directional selection. We then simulate the evolution of this community in order to disentangle how these forces have shaped the species diversity that we see today. We show that, while species richness can be maintained by a neutral force, immigration, species evenness cannot and argue that it is likely maintained by temporally varying selection driven by environmental change [23-25].

ecology↗

bioRxiv2022

A Meta-analysis of Longevity Estimates of Mosquito Vectors of Disease

Mosquitoes are responsible for more human deaths than any other animal, yet we still know relatively little about their ecology. Mosquito lifespan is a key determinant of the force of transmission for the diseases they vector, but the field experiments and dissection methods used to determine this quantity produce estimates with high uncertainty. In this paper, we use Bayesian hierarchical models to analyse a previously published database of 232 mark-release-recapture (MRR) experiments and two databases of different types of mosquito dissection experiments. One, compiled by us, consisted of 131 detailed estimates of the number of egg-laying (gonotrophic) cycles, the other, a recently published dataset of 1490 studies of parity (whether a mosquito has laid eggs or not) in anopheline malaria vectors. We analysed all studies with the same methodology and used Bayesian hierarchical statistics to obtain estimate at the species and genus level. For the major African malaria vector Anopheles gambiae s.l., we estimate lifespans ranging from 4.4 days (from MRR analysis) to 10.3 days (from parity analysis). For the predominantly East-African vector An. funestus s.l., our lifespan estimates range from 4.2 days (MRR) to 7.1 days (dichotomous parity analysis). We estimate lifespans ranging from 4.7 days (physiological age analysis) to 6.2 days (MRR) for Aedes aegypti, and a lifespan of 11.6 days for Ae. Albopictus (only present in MRR data) - the predominant vectors of key arboviruses. Additionally, we estimate that female mosquitoes outlive males by 1.2 days on average (mean estimate; 25%-75% CI: 0.3-1.6 days). By fitting a range of survival models to the data, we found relatively little evidence for senescence in the field. Our analyses, supplemented by power analyses, indicate the considerable uncertainty that remains about mosquito lifespan in the wild. We conclude further progress will require larger and longer experiments or the development of novel new methodologies. Author summaryMosquitoes transmit some of the most important diseases afflicting humans, with malaria alone killing between 390,000 and 460,000 people in 2019, chiefly children in low-income countries. The force of transmission of these diseases depends critically on the duration of mosquito lifespans, and some of the most successful disease control interventions, including insecticide-treated bednets, work because they reduce mosquito longevity. In this study, we conduct meta-analyses of two important classes of field experiments used to estimate wild mosquito lifespan: mark-release-recapture studies, where mosquitoes are marked with dye then released with the number of marked mosquitoes caught monitored over time; and experiments involving dissection of wild-caught females, whose reproductive anatomy is used as a biological clock to determine physiological age. In all analyses, we estimate that most mosquito species live less than 10 days on average, which suggests that relatively few mosquitoes live sufficiently long to transmit disease. The estimates obtained across the two field experiment types were largely discordant and indicated conflicting sources of heterogeneity in lifespan, likely due to the weak power of small-scale experiments. Finally, by fitting a range of survival models to the data, we conclude that, for most species, mosquitoes do not experience strong age-related increases in mortality. We critique the quality of the existing evidence base about mosquito lifespans in the field and suggest how it may be improved. Author contributionsBL, AN and HCJG designed this study. BL was responsible for data curation, developing the statistical methodology and conducting the investigation. All authors were involved in drafting the original manuscript and revising it.

ecology↗

bioRxiv2022

Treatment response prediction: Is model selection unreliable?

Quantitative modelling has become an essential part of the drug development pipeline. In particular, pharmacokinetic and pharmacodynamic models are used to predict treatment responses in order to optimise clinical trials and assess the safety and efficacy of dosing regimens across patients. It is therefore crucial that treatment response predictions are reliable. However, the data available to fit models are often limited, which can leave considerable uncertainty about the best model to use. Common practice is to select the model that is most consistent with the observed data based on the Akaike information criterion (AIC). Another popular approach is to average the predictions across the subset of models consistent with the data. In this article, we argue that both approaches can lead to unreliable predictions, as treatment responses typically display nonlinear dynamics, so models can be consistent with the observed dynamics, whilst predicting incorrect treatment responses. This is especially the case when predicting treatment responses for either times or dosing regimens that go beyond the observed dynamics. Across a range of experiments on both real laboratory data and synthetically derived data on Neisseria gonorrhoeae response to ciprofloxacin, we show that probabilistic averaging of models results in more reliable treatment response predictions.

systems biology↗

bioRxiv2021

Tuning spherical cells into kinking helices in wall-less bacteria

In bacteria, cell shape is determined and maintained through a complex interplay between the peptidoglycan cell wall and cytoplasmic filaments made of polymerized MreB. Spiroplasma species, members of the Mollicutes class, challenge this general understanding because they are characterized by a helical cell shape and motility without a cell wall. This specificity is thought to rely on five MreB isoforms and a specific fibril protein. In this study, combinations of these five MreBs and of the fibril from Spiroplasma citri were expressed in another Mollicutes, Mycoplasma capricolum. Mycoplasma cells that were initially pleomorphic, mostly spherical, turned into helices when MreBs and fibrils were expressed in this heterologous host. The fibril protein was essential neither for helicity nor for cell movements. The isoform MreB5 had a special role as it was sufficient to confer helicity and motility to the mycoplasma cells. Cryo-electron microscopy confirmed the association of MreBs and fibril-based cytoskeleton with the plasma membrane, suggesting a direct effect on the membrane curvature. Finally, the heterologous expression of these proteins, MreBs and fibril, made it possible to reproduce the kink-like motility of spiroplasmas without providing the ability of cell movement in liquid broth. We suggest that other Spiroplasma components, not yet identified, are required for swimming, a hypothesis that could be evaluated in future studies using the same model.

microbiology↗

bioRxiv2021

The dynamic trophic architecture of open-ocean protist communities revealed through machine- guided metatranscriptomics

Intricate networks of single-celled eukaryotes (protists) dominate carbon flow in the ocean. Their growth, demise, and interactions with other microorganisms drive the fluxes of biogeochemical elements through marine ecosystems. Mixotrophic protists are capable of both photosynthesis and ingestion of prey and are dominant components of open-ocean planktonic communities. Yet, the role of mixotrophs in elemental cycling is obscured by their capacity to act as primary producers or heterotrophic consumers depending on factors that remain largely uncharacterized. Here we introduce a machine learning model that can predict the in situ nutritional mode of aquatic protists based on their patterns of gene expression. This approach leverages a public collection of protist transcriptomes as a training set to identify a subset of gene families whose transcriptional profiles predict trophic status. We applied our model to nearly 100 metatranscriptomes obtained during two oceanographic cruises in the North Pacific and found community-level and population-specific evidence that abundant open-ocean mixotrophic populations shift their predominant mode of nutrient and carbon acquisition in response to natural gradients in nutrient supply and sea surface temperature. In addition, metatranscriptomic data from ship-board incubation experiments revealed that abundant mixotrophic prymnesiophytes from the oligotrophic North Pacific subtropical gyre rapidly remodelled their transcriptome to enhance photosynthesis when supplied with limiting nutrients. Coupling the technique introduced here with experiments designed to reveal the mechanisms driving mixotroph physiology is a promising approach for understanding the ecology of mixotrophic populations in the natural environment. Significance statementMixotrophy is a ubiquitous nutritional strategy in marine ecosystems. Although our understanding of the distribution and abundance of mixotrophic plankton has improved significantly, the functional roles of mixotrophs are difficult to pinpoint, as mixotroph nutritional strategies are flexible and form a continuum between heterotrophy and phototrophy. We employ a machine learning-driven metatranscriptomic technique to assess the nutritional strategies of abundant planktonic populations in situ and demonstrate that mixotrophic populations play varying functional roles along physico-chemical gradients in the North Pacific Ocean, revealing a degree of physiological plasticity unique to aquatic mixotrophs. Our results highlight mechanisms that may dictate the flow of biogeochemical elements and the ecology of the North Pacific Ocean, one of the largest biogeographical provinces on Earth.

ecology↗

bioRxiv2020

Estimating the extrinsic incubation period of malaria using a mechanistic model of sporogony

During sporogony, malaria-causing parasites infect a mosquito, reproduce and migrate to the mosquito salivary glands where they can be transmitted the next time blood-feeding occurs. The time required for sporogony, or extrinsic incubation period (EIP), is a crucial determinant of malaria transmission intensity. The EIP is typically estimated as the time for a given percentile of infected mosquitoes to have salivary gland sporozoites (the infectious parasite life stage). Many mechanisms, however, affect the observed sporozoite prevalence including the human-to-mosquito transmission probability and possibly differences in mosquito mortality according to infection status. To account for these various mechanisms, we present a mechanistic mathematical model ("mSOS"), which explicitly models key processes at the parasite, mosquito and observational scales. Fitting this model to experimental data, we find greater variation in EIP than previously thought: we estimated the range between two percentiles of the distribution, EIP10-EIP90 (at 27{degrees}C), as 4.5 days, compared to 0.9 days using existing methods. This pattern holds over the range of study temperatures included in the dataset. Increasing temperature from 21{degrees}C to 34{degrees}C decreased the EIP50 from 16.1 to 8.8 days and the human-to-mosquito transmission probability from 84% to 42%. Our work highlights the importance of mechanistic modelling of sporogony to (1) improve estimates of malaria transmission under different environmental conditions or disease control programs and (2) evaluate novel interventions that target the mosquito life stages of the parasite. Author summaryAnopheles mosquitoes become infected with malaria-causing parasites when blood feeding on an infectious human host. The parasites then process through a number of life stages, which begin in the mosquito gut and end in the salivary glands, where the newly formed infectious parasites can be transmitted to another host the next time a mosquito blood-feeds. The large variability in parasite numbers and development times that exists between mosquitoes, environments and parasites, mean that understanding parasite population dynamics from individual mosquito dissections is difficult. Here, we introduce a mathematical model of the mosquito life stages of parasites that mimics key characteristics of the biology. We show that the models parameters can be chosen so that its predictions correspond with experimental observations. In doing so, we estimate key system characteristics that are crucial determinants of malaria transmission intensity. Our work is a step towards a realistic model of within-mosquito parasite dynamics, which is increasingly important given that many recently proposed disease interventions specifically target mosquito life stages of the parasite.

microbiology↗