Biology subjects

Beck, J. L.

Publications and source records attributed to Beck, J. L..

4 recordsLinked to original sources

bioRxiv2024

aniSNA : An R package to assess bias and uncertainty in social networks obtained from animals sampled via direct observations or satellite telemetry

Animal social network analysis using GPS telemetry datasets provides insights into group dynamics, social structure, and interactions of the animal communities. It aids conservation by characterizing key aspects of animal sociality - including spatially explicit information on where sociality occurs (e.g., habitats, migratory corridors), contributing to informed management strategies for wildlife populations. The aniSNA package provides functions to assess and leverage data collected by sampling a subset of an animal population to perform social network analysis. The methodologies offered in this package are compatible with a variety of location and grouping data, collected through various means (e.g., direct observations, biologgers), however, they are particularly well suited to autocorrelated data streams such as data collected through GPS telemetry radio collars. The techniques assess the datas suitability to extract reliable statistical inferences from social networks and compute uncertainty estimates around the network metrics in the scenario where a fraction of the population is monitored. The package functions are user-friendly and allow for the implementation of pre-network data permutations for auto-correlated data streams, sensitivity analysis under downsampling, bootstrapping to establish confidence intervals for global and node-level network metrics, and correlation and regression analysis to assess the robustness of node-level network metrics. Using this package, animal ecologists will be able to compute social network metrics, both at the population and individual level, assess their reliability, and use such metrics in further analyses, e.g., to study social network variation within and across populations or link individual sociality to life history. This software also has plotting features that allow for visual interpretation of the findings.

animal behavior and cognition↗

bioRxiv2023

Assessing bias and robustness of social network metrics using GPS based radio-telemetry data

O_LISocial network analysis of animal societies allows scientists to test hypotheses about social evolution, behaviour, dynamical processes, and transmission events such as the spread of disease. However, the accuracy of estimated social network metrics depends on the proportion of individuals sampled, actual sample size, and frequency of observations. Robustness of network metrics derived from a sample has thus far been examined through various simulation studies. However, simulated data do not necessarily reflect the nuances of real empirical data. C_LIO_LIWe used some of the largest available GPS telemetry relocation datasets from five species of ungulates characterised by different behavioural and ecological traits and living in distinct environmental contexts to study the bias and robustness of social network metrics. We introduced novel statistical methods to quantify the uncertainty in network metrics obtained from a partial population suited to autocorrelated data such as telemetry relocations. We analysed how social network metrics respond to down-sampling from the observed data and applied pre-network data permutation techniques, a bootstrapping approach, correlation, and regression analyses to assess the stability of network metrics when based on samples of a population. C_LIO_LIWe found that global network metrics like density remain robust when the sample size is lowered, whereas some local network metrics, such as eigenvector centrality, are entirely unreliable when a large proportion of the population is not monitored. We show how to construct confidence intervals around the point estimates of these metrics representing the uncertainty as a function of the number of nodes in the network. C_LIO_LIOur uncertainty estimates enable the statistical comparison of social network metrics under different conditions, such as analysing daily and seasonal changes in the density of a network. Despite the striking differences in the ecology and sociality among the five different ungulate species, the various social network metrics behave similarly under downsampling, suggesting that our approach can be applied to a wider range of species across vertebrates. Our methods can guide methodological decisions about animal social network research (e.g., sampling design and sample sizes) and allow more accurate ecological inferences from the available data. C_LI

animal behavior and cognition↗

bioRxiv2020

A mechanism for the extension and unfolding of parallel telomeric G-quadruplexes by human telomerase at single-molecule resolution

Telomeric G-quadruplexes (G4) were long believed to form a protective structure at telomeres, preventing their extension by the ribonucleoprotein telomerase. Contrary to this belief, we have previously demonstrated that parallel-stranded conformations of telomeric G4 can be extended by human and ciliate telomerase. However, a mechanistic understanding of the interaction of telomerase with structured DNA remained elusive. Here, we use single-molecule fluorescence resonance energy transfer (smFRET) microscopy and bulk-phase enzymology to propose a mechanism for the resolution and extension of parallel G4 by telomerase. Binding is initiated by the RNA template of telomerase interacting with the G-quadruplex; nucleotide addition then proceeds to the end of the RNA template. It is only through the large conformational change of translocation following synthesis that the G-quadruplex structure is completely unfolded to a linear product. Surprisingly, parallel G4 stabilization with either small molecule ligands or by chemical modification does not always inhibit G4 unfolding and extension by telomerase. These data reveal that telomerase is a parallel G-quadruplex resolvase.

biochemistry↗

bioRxiv2016

The Effect of Compositional Context on Synthetic Gene Net-works

It is well known that synthetic gene expression is highly sensitive to how comprising genetic elements (promoter structure, spacing regions between promoter and coding sequences, ribosome binding sites, etc.) are spatially configured. An important topic that has received far less attention is how the physical layout of entire genes within a synthetic gene network affects their individual expression levels. In this paper we show, both quantitatively and qualitatively, that compositional context can significantly alter expression levels in synthetic gene networks. We also show that these compositional context effects are pervasive both at the transcriptional and translational level. Further, we demonstrate that key characteristics of gene induction, such as ultra-sensitivity and dynamic range, are heavily dependent on compositional context. We postulate that supercoiling can be used to explain these interference effects and validate this hypothesis through modeling and a series of in vitro supercoiling relaxation experiments. On the whole, these results suggest that compositional context introduces feedback in synthetic gene networks. As an illustrative example, we show that a design strategy incorporating compositional context effects can improve threshold detection and memory properties of the toggle switch.

synthetic biology↗