Biology subjects

Lambert, B.

Publications and source records attributed to Lambert, B..

25 records · Page 2Linked to original sources

bioRxiv2020

Functional data analysis techniques to improve the generalizability of near-infrared spectral data for monitoring mosquito populations

Near infrared spectroscopy is increasingly being used as an economical method to monitor mosquito vector populations in support of disease control. Despite this rise in popularity, strong geographical variation in spectra has proven an issue for generalising predictions from one location to another. Here, we use a functional data analysis approach--which models spectra as smooth curves rather than as a discrete set of points--to develop a method that is robust to geographic heterogeneity. Specifically, we use a penalised generalised linear modelling framework which includes efficient functional representation of spectra, spectral smoothing and regularisation. To ensure better generalisation of model predictions from one training set to another, we use cross-validation procedures favouring smoother representation of spectra. To illustrate the performance of our approach, we collected spectra for field-caught specimens of Anopheles gambiae complex mosquitoes - the most epidemiologically important vector species on the planet - in two sites in Burkina Faso. Using these spectra, we show how models trained on data from one site can successfully classify morphologically identical sibling species in another site, over 250km away. Whilst we apply our framework to species prediction, our unified statistical framework can, alternatively, handle regression analysis (for example, to determine mosquito age) and other types of multinomial classification (for example, to determine infection status). To make our methods readily available for field entomologists, we have created an open-source R package mlevcm. All data used is publicly also available.

bioinformatics↗

bioRxiv2020

Endemic and epidemic human alphavirus infections in Eastern Panama, An Analysis of Population-Based Cross-Sectional Surveys

BackgroundMadariaga virus (MADV), has recently been associated with severe human disease in Panama, where the closely related Venezuelan equine encephalitis virus (VEEV) also circulates. In June, 2017, a fatal MADV infection was confirmed in a community of Darien province. MethodsWe conducted a cross-sectional outbreak investigation with human and mosquito collections in July 2017, where sera were tested for alphavirus antibodies and viral RNA. Additionally, by applying a catalytic, force-of-infection statistical model to two serosurveys from Darien province in 2012 and 2017, we investigated whether endemic or epidemic alphavirus transmission occurred historically. ResultsIn 2017, MADV and VEEV IgM seroprevalence was 1.6% and 4.4%, respectively; IgG antibody prevalences were MADV: 13.2%; VEEV: 16.8%; Una virus (UNAV): 16.0%; and Mayaro virus (MAYV): 1.1%. Active viral circulation was not detected. Evidence of MADV and UNAV infection was found near households -- raising questions about its vectors and enzootic transmission cycles. Insomnia was associated with MADV and VEEV infection, depression symptoms were associated with MADV, and dizziness with VEEV and UNAV. Force-of-infection analyses suggest endemic alphavirus transmission historically, with recent increased human exposure to MADV and VEEV in some regions. ConclusionsThe lack of additional neurological cases suggest that severe MADV and VEEV infections occur only rarely. Our results indicate that, over the past five decades, alphavirus infections have occurred at low levels in eastern Panama, but that MADV and VEEV infections have recently increased -- potentially during the past decade. Endemic infections and outbreaks of MADV and VEEV appear to differ spatially. Author summaryPrior to 2010, it was believed that the Madariaga virus (MADV) was primarily associated with equine disease. However, an outbreak reported in Panama, in an endemic area where Venezuelan equine encephalitis virus (VEEV) also circulates, suggested a change in its epidemiological profile. We aimed to reconstruct the epidemiological dynamics of MADV and VEEV, as well as additional alphaviruses known to circulate in the region in order to understand MADV emergence. For this, cross-sectional serosurveys were used to demonstrate that the Alphaviruses MADV, VEEV and Una virus have repeatedly infected humans in eastern Panama over the past five decades. Whilst their historical transmission has been low, we confirm that the transmission has recently increased for both MADV and VEEV.

microbiology↗

bioRxiv2019

A Monte Carlo method to estimate cell population heterogeneity

1Variation is characteristic of all living systems. Laboratory techniques such as flow cytometry can probe individual cells, and, after decades of experimentation, it is clear that even members of genetically identical cell populations can exhibit differences. To understand whether variation is biologically meaningful, it is essential to discern its source. Mathematical models of biological systems are tools that can be used to investigate causes of cell-to-cell variation. From mathematical analysis and simulation of these models, biological hypotheses can be posed and investigated, then parameter inference can determine which of these is compatible with experimental data. Data from laboratory experiments often consist of \"snapshots\" representing distributions of cellular properties at different points in time, rather than individual cell trajectories. These data are not straightforward to fit using hierarchical Bayesian methods, which require the number of cell population clusters to be chosen a priori. Here, we introduce a computational sampling method named \"Contour Monte Carlo\" for estimating mathematical model parameters from snapshot distributions, which is straightforward to implement and does not require cells be assigned to predefined categories. Our method is appropriate for systems where observed variation is mostly due to variability in cellular processes rather than experimental measurement error, which may be the case for many systems due to continued improvements in resolution of laboratory techniques. In this paper, we apply our method to quantify cellular variation for three biological systems of interest and provide Julia code enabling others to use this method.

systems biology↗

bioRxiv2018

Birds invest wingbeats to keep a steady head and reap the ultimate benefits of flocking

Flapping flight is the most energetically demanding form of sustained forwards locomotion that vertebrates perform. Flock dynamics therefore have significant implications for energy expenditure. Despite this, no studies have quantified the biomechanical consequences of flying in a cluster flock relative to flying solo. Here, we compared the flight characteristics of homing pigeons (Columba livia) flying solo and in pairs, using high-precision 5 Hz GPS and 200 Hz tri-axial accelerometer biologgers. Paired flight increased route accuracy by ~7%, but, was accompanied by an increase in wingbeat frequency of ~18%. As expected, paired individuals benefitted from improved homing route accuracy, which reduced flight distance by ~7% and time by ~9%. However, realising these navigational gains involved substantial changes in flight kinematics and energetics. Both individuals in a pair increased their wingbeat frequency by c.18%, by decreasing the duration of their upstroke. This sharp increase in wingbeat frequency caused just a 3% increase in airspeed, but reduced the oscillatory displacement of the body by ~22%, which we hypothesise relates to an increased requirement for visual stability and manoeuvrability when flocking. Overall, the shorter flight distances and increased wingbeat frequency in a pair resulted in a net increase in the aerodynamic cost of returning home, which we estimate was ~14%. Our results demonstrate that flocking costs have been underestimated by an order of magnitude and force reinterpretation of their mechanistic origin. We show that, for pigeons, two heads are better than one, but keeping a steady head necessitates energetically costly kinematics.

animal behavior and cognition↗

bioRxiv2018

Inverse sensitivity analysis of mathematical models avoiding the curse of dimensionality

1Biological systems have evolved a degree of robustness with respect to perturbations in their environment and this capability is essential for their survival. In applications ranging from therapeutics to conservation, it is important to understand not only the sensitivity of biological systems to changes in their environments, but which features of these systems are necessary to achieve a given outcome. Mathematical models are increasingly employed to understand these mechanisms. Sensitivity analyses of such mathematical models provide insight into the responsiveness of the system when experimental manipulation is difficult. One common approach is to seek the probability distribution of the outputs of the system corresponding to a known distribution of inputs. By contrast, inverse sensitivity analysis determines the probability distribution of model inputs which produces a known distribution of outputs. The computational complexity of the methods used to conduct inverse sensitivity analyses for deterministic systems has limited their application to models with relatively few parameters. Here we describe a novel Markov Chain Monte Carlo method we call \"Contour Monte Carlo\", which can be used to invert systems with a large number of parameters. We demonstrate the utility of this method by inverting a range of frequently-used deterministic models of biological systems, including the logistic growth equation, the Michaelis-Menten equation, and an SIR model of disease transmission with nine input parameters. We argue that the simplicity of our approach means it is amenable to a large class of problems of practical significance and, more generally, provides a probabilistic framework for understanding the inversion of deterministic models.\n\n2 Author summaryMathematical models of complex systems are constructed to provide insight into their underlying functioning. Statistical inversion can probe the often unobserved processes underlying biological systems, by proceeding from a given distribution of a models outputs (the aggregate \"effects\") to a distribution over input parameters (the constituent \"causes\"). The process of inversion is well-defined for systems involving randomness and can be described by Bayesian inference. The inversion of a deterministic system, however, cannot be performed by the standard Bayesian approach. We develop a conceptual framework that describes the inversion of deterministic systems with fewer outputs than input parameters. Like Bayesian inference, our approach uses probability distributions to describe the uncertainty over inputs and outputs, and requires a prior input distribution to ensure a unique \"posterior\" probability distribution over inputs. We describe a computational Monte Carlo method that allows efficient sampling from the posterior distribution even as the dimension of the input parameter space grows. This is a two-step process where we first estimate a \"contour volume density\" associated with each output value which is then used to define a sampling algorithm that yields the requisite input distribution asymptotically. Our approach is simple, broadly applicable and could be widely adopted.

systems biology↗

bioRxiv2017

Epidemiological and ecological determinants of Zika virus transmission in an urban setting.

Zika has emerged as a global public health concern. Although its rapid geographic expansion can be attributed to the success of its Aedes mosquito vectors, local epidemiological drivers are still poorly understood. The city of Feira de Santana played a pivotal role in the early phases of the Chikungunya and Zika epidemics in Brazil. Here, using a climate-driven transmission model, we show that low Zika observation rates and a high vectorial capacity in this region were responsible for a high attack rate during the 2015 outbreak and the subsequent decline in cases in 2016, when the epidemic was peaking in the rest of the country. Our projections indicate that the balance between the loss of herd-immunity and the frequency of viral re-importation will dictate the transmission potential of Zika in this region in the near future. Sporadic outbreaks are expected but unlikely to be detected under current surveillance systems.

epidemiology↗

bioRxiv2017

Bayesian inference of agent-based models: a tool for studying kidney branching morphogenesis

The adult mammalian kidney has a complex, highly-branched collecting duct epithelium that arises as a ureteric bud sidebranch from an epithelial tube known as the nephric duct. Subsequent branching of the ureteric bud to form the collecting duct tree is regulated by subcellular interactions between the epithelium and a population of mesenchymal cells that surround the tips of outgrowing branches. The mesenchymal cells produce glial cell-line derived neurotrophic factor (GDNF), that binds with RET receptors on the surface of the epithelial cells to stimulate several subcellular pathways in the epithelium. Such interactions are known to be a prerequisite for normal branching development, although competing theories exist for their role in morphogenesis. Here we introduce the first agent-based model of ex vivo kidney uretic branching. Through comparison with experimental data, we show that growth factor-regulated growth mechanisms can explain early epithelial cell branching, but only if epithelial cell division depends in a switch-like way on the local growth factor concentration; cell division occurring only if the driving growth factor level exceeds a threshold. We also show how a recently-developed method, \"Approximate Approximate Bayesian Computation\", can be used to infer key model parameters, and reveal the dependency between the parameters controlling a growth factor-dependent growth switch. These results are consistent with a requirement for signals controlling proliferation and chemotaxis, both of which are previously identified roles for GDNF.\n\nAuthor SummaryA number of important congenital disorders arise due to incomplete development of the mammalian kidney. Elucidating the cause of these conditions requires an understanding of the mechanisms that contribute to kidney morphogenesis. Whilst experimental work has suggested several candidate mechanisms, their importance is still not well understood. Here we develop a computational model of kidney morphogenesis at the individual cell level to compare these different hypotheses. Guided by existing experimental evidence we propose that a generic growth factor, that we term \"GDNF\", produced from the mesenchyme surrounding the epithelium, can drive a number of cellular responses. Simulations of our agent-based model reveal that diffusion of GDNF, coupled with GDNF-stimulated epithelial cell division, can generate the branching patterns seen in ex vivo kidney explant experiments. We also find that branching depends on the sensitivity of cell proliferation to changes in GDNF levels. In particular our model only generates realistic branching when there is significant variation in GDNF levels along the boundary of the epithelium, and most cells divide only if the local concentration of GDNF exceeds a threshold value. We conclude that feedback between mesenchymal cells that produce GDNF, and epithelial cells that consume it, is vital for normal kidney organogenesis.

developmental biology↗