Timothy G. Vaughan

BEAST 2: A Software Platform for Bayesian Evolutionary Analysis

We present a new open source, extensible and flexible software platform for Bayesian evolutionary analysis called BEAST 2. This software platform is a re-design of the popular BEAST 1 platform to correct structural deficiencies that became evident as the BEAST 1 software evolved. Key among those deficiencies was the lack of post-deployment extensibility. BEAST 2 now has a fully developed package management system that allows third party developers to write additional functionality that can be directly installed to the BEAST 2 analysis platform via a package manager without requiring a new software release of the platform. This package architecture is showcased with a number of recently published new models encompassing birth-death-sampling tree priors, phylodynamics and model averaging for substitution models and site partitioning. A second major improvement is the ability to read/write the entire state of the MCMC chain to/from disk allowing it to be easily shared between multiple instances of the BEAST software. This facilitates checkpointing and better support for multi-processor and high-end computing extensions. Finally, the functionality in new packages can be easily added to the user interface (BEAUti 2) by a simple XML template-based mechanism because BEAST 2 has been re-designed to provide greater integration between the analysis engine and the user interface so that, for example BEAST and BEAUti use exactly the same XML file format.

Efficient Bayesian inference under the structured coalescent

Motivation: Population structure significantly affects evolutionary dynamics. Such structure may be due to spatial segregation, but may also reflect any other gene-flow-limiting aspect of a model. In combination with the structured coalescent, this fact can be used to inform phylogenetic tree reconstruction, as well as to infer parameters such as migration rates and subpopulation sizes from annotated sequence data. However, conducting Bayesian inference under the structured coalescent is impeded by the difficulty of constructing Markov Chain Monte Carlo (MCMC) sampling algorithms (samplers) capable of efficiently exploring the state space. Results: In this article, we present a new MCMC sampler capable of sampling from posterior distributions over structured trees: timed phylogenetic trees in which lineages are associated with the distinct subpopulation in which they lie. The sampler includes a set of MCMC proposal functions that offer significant mixing improvements over a previously published method. Furthermore, its implementation as a BEAST 2 package ensures maximum flexibility with respect to model and prior specification. We demonstrate the usefulness of this new sampler by using it to infer migration rates and effective population sizes of H3N2 influenza between New Zealand, New York and Hong Kong from publicly available hemagglutinin (HA) gene sequences under the structured coalescent. Availability and implementation: The sampler has been implemented as a publicly available BEAST 2 package that is distributed under version 3 of the GNU General Public License at http://compevol.github.io/MultiTypeTree. Contact: tgvaughan@gmail.com Supplementary information: Supplementary data are available at Bioinformatics online.

Einstein-Podolsky-Rosen entanglement strategies in two-well Bose-Einstein condensates.

Criteria suitable for measuring entanglement between two different potential wells in a BoseEinstein condensation (BEC) are evaluated. We show how to generate the required entanglement, utilizing either an adiabatic two-mode or dynamic four-mode interaction strategy, with techniques that take advantage of s-wave scattering interactions to provide the nonlinear coupling. The dynamic entanglement method results in an entanglement signature with spatially separated detectors, as in the Einstein-Podolsky-Rosen (EPR) paradox.

A Stochastic Simulator of Birth–Death Master Equations with Application to Phylodynamics

In this article, we present a versatile new software tool for the simulation and analysis of stochastic models of population phylodynamics and chemical kinetics. Models are specified via an expressive and human-readable XML format and can be used as the basis for generating either single population histories or large ensembles of such histories. Importantly, phylogenetic trees or networks can be generated alongside the histories they correspond to, enabling investigations into the interplay between genealogies and population dynamics. Summary statistics such as means and variances can be recorded in place of the full ensemble, allowing for a reduction in the amount of memory used—an important consideration for models including large numbers of individual subpopulations or demes. In the case of population size histories, the resulting simulation output is written to disk in the flexible JSON format, which is easily read into numerical analysis environments such as R for visualization or further processing. Simulated phylogenetic trees can be recorded using the standard Newick or NEXUS formats, with extensions to these formats used for non-tree-like inheritance relationships.

Phylodynamics with Migration: A Computational Framework to Quantify Population Structure from Genomic Data

When viruses spread, outbreaks can be spawned in previously unaffected regions. Depending on the time and mode of introduction, each regional outbreak can have its own epidemic dynamics. The migration and phylodynamic processes are often intertwined and need to be taken into account when analyzing temporally and spatially structured virus data. In this article, we present a fully probabilistic approach for the joint reconstruction of phylodynamic history in structured populations (such as geographic structure) based on a multitype birth–death process. This approach can be used to quantify the spread of a pathogen in a structured population. Changes in epidemic dynamics through time within subpopulations are incorporated through piecewise constant changes in transmission parameters. We analyze a global human influenza H3N2 virus data set from a geographically structured host population to demonstrate how seasonal dynamics can be inferred simultaneously with the phylogeny and migration process. Our results suggest that the main migration path among the northern, tropical, and southern region represented in the sample analyzed here is the one leading from the tropics to the northern region. Furthermore, the time-dependent transmission dynamics between and within two HIV risk groups, heterosexuals and injecting drug users, in the Latvian HIV epidemic are investigated. Our analyses confirm that the Latvian HIV epidemic peaking around 2001 was mainly driven by the injecting drug user risk group.

Genetic diversity loss in a biodiversity hotspot: Ancient DNA quantifies genetic decline and former connectivity in a critically endangered marsupial

The extent of genetic diversity loss and former connectivity between fragmented populations are often unknown factors when studying endangered species. While genetic techniques are commonly applied in extant populations to assess temporal and spatial demographic changes, it is no substitute for directly measuring past diversity using ancient DNA (aDNA). We analysed both mitochondrial DNA (mtDNA) and nuclear microsatellite loci from 64 historical fossil and skin samples of the critically endangered Western Australian woylie (Bettongia penicillata ogilbyi), and compared them with 231 (n = 152 for mtDNA) modern samples. In modern woylie populations 15 mitochondrial control region (CR) haplotypes were identified. Interestingly, mtDNA CR data from only 29 historical samples demonstrated 15 previously unknown haplotypes and detected an extinct divergent clade. Through modelling, we estimated the loss of CR mtDNA diversity to be between 46% and 91% and estimated this to have occurred in the past 2000–4000 years in association with a dramatic population decline. In addition, we obtained near‐complete 11‐loci microsatellite profiles from 21 historical samples. In agreement with the mtDNA data, a number of ‘new’ microsatellite alleles was only detected in the historical populations despite extensive modern sampling, indicating a nuclear genetic diversity loss >20%. Calculations of genetic diversity (heterozygosity and allelic rarefaction) showed that these were significantly higher in the past and that there was a high degree of gene flow across the woylies historical range. These findings have an immediate impact on how the extant populations are managed and we recommend the implementation of an assisted migration programme to prevent further loss of genetic diversity. Our study demonstrates the value of integrating aDNA data into current‐day conservation strategies.

Entanglement, number fluctuations and optimized interferometric phase measurement

We derive a phase-entanglement criterion for two bosonic modes that is immune to number fluctuations, using the generalized Moore–Penrose inverse to normalize the phase-quadrature operator. We also obtain a phase-squeezing criterion that is immune to number fluctuations using similar techniques. These are used to obtain an operational definition of relative phase-measurement sensitivity via the analysis of phase measurement in interferometry. We show that these criteria are proportional to the enhanced phase-measurement sensitivity. The phase-entanglement criterion is the hallmark of a new type of quantum-squeezing, namely planar quantum-squeezing. This has the property that it squeezes simultaneously two orthogonal spin directions, which is possible owing to the fact that the SU(2) group that describes spin symmetry has a three-dimensional parameter space of higher dimension than the group for photonic quadratures. A practical advantage of planar quantum-squeezing is that, unlike conventional spin-squeezing, it allows noise reduction over all phase angles simultaneously. The application of this type of squeezing is to the quantum measurement of an unknown phase. We show that a completely unknown phase requires two orthogonal measurements and that with planar quantum-squeezing it is possible to reduce the measurement uncertainty independently of the unknown phase value. This is a different type of squeezing compared to the usual spin-squeezing interferometric criterion, which is applicable only when the measured phase is already known to a good approximation or can be measured iteratively. As an example, we calculate the phase entanglement of the ground state of a two-well, coupled Bose–Einstein condensate, similarly to recent experiments. This system demonstrates planar squeezing in both the attractive and the repulsive interaction regime.

How well can the exponential-growth coalescent approximate constant-rate birth-death population dynamics?

One of the central objectives in the field of phylodynamics is the quantification of population dynamic processes using genetic sequence data or in some cases phenotypic data. Phylodynamics has been successfully applied to many different processes, such as the spread of infectious diseases, within-host evolution of a pathogen, macroevolution and even language evolution. Phylodynamic analysis requires a probability distribution on phylogenetic trees spanned by the genetic data. Because such a probability distribution is not available for many common stochastic population dynamic processes, coalescent-based approximations assuming deterministic population size changes are widely employed. Key to many population dynamic models, in particular epidemiological models, is a period of exponential population growth during the initial phase. Here, we show that the coalescent does not well approximate stochastic exponential population growth, which is typically modelled by a birth–death process. We demonstrate that introducing demographic stochasticity into the population size function of the coalescent improves the approximation for values of R0 close to 1, but substantial differences remain for large R0. In addition, the computational advantage of using an approximation over exact models vanishes when introducing such demographic stochasticity. These results highlight that we need to increase efforts to develop phylodynamic tools that correctly account for the stochasticity of population dynamic models for inference.

Molecular Approaches to Understanding Transmission and Source Attribution in Nontyphoidal Salmonella and Their Application in Africa

Nontyphoidal Salmonella (NTS) is a frequent cause of diarrhea around the world, yet in many African countries it is more commonly associated with invasive bacterial disease. Various source attribution models have been developed that utilize microbial subtyping data to assign cases of human NTS infection to different animal populations and foods of animal origin. Advances in molecular microbial subtyping approaches, in particular whole-genome sequencing, provide higher resolution data with which to investigate these sources. In this review, we provide updates on the source attribution models developed for Salmonella, and examine the application of whole-genome sequencing data combined with evolutionary modeling to investigate the putative sources and transmission pathways of NTS, with a focus on the epidemiology of NTS in Africa. This is essential information to decide where, what, and how control strategies might be applied most effectively.

Within-host demographic fluctuations and correlations in early retroviral infection

In this paper we analyze the demographic fluctuations and correlations present in within-host populations of viruses and their target cells during the early stages of infection. In particular, we present an exact treatment of a discrete-population, stochastic, continuous-time master equation description of HIV or similar retroviral infection dynamics, employing Monte Carlo simulations. The results of calculations employing Gillespies direct method clearly demonstrate the importance of considering the microscopic details of the interactions which constitute the macroscopic dynamics. We then employ the τ-leaping approach to study the statistical characteristics of infections involving realistic absolute numbers of within-host viral and cellular populations, before going on to investigate the effect that initial viral population size plays on these characteristics. Our main conclusion is that cross-correlations between infected cell and virion populations alter dramatically over the course of the infection. We suggest that these statistical correlations offer a novel and robust signature for the acute phase of retroviral infection.