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    Kendall ML, Ayabina P, Xu Y, Stimson J, Colijn Cet al., 2018,

    Estimating Transmission from Genetic and Epidemiological Data: A Metric to Compare Transmission Trees

    , Statistical Science, Vol: 33, Pages: 70-85, ISSN: 0883-4237

    Reconstructing who infected whom is a central challenge in analysing epidemiological data. Recently, advances in sequencing technology have led to increasing interest in Bayesian approaches to inferring who infected whom using genetic data from pathogens. The logic behind such approaches is that isolates that are nearly genetically identical are more likely to have been recently transmitted than those that are very different. A number of methods have been developed to perform this inference. However, testing their convergence, examining posterior sets of transmission trees and comparing methods’ performance are challenged by the fact that the object of inference—the transmission tree—is a complicated discrete structure. We introduce a metric on transmission trees to quantify distances between them. The metric can accommodate trees with unsampled individuals, and highlights differences in the source case and in the number of infections per infector. We illustrate its performance on simple simulated scenarios and on posterior transmission trees from a TB outbreak. We find that the metric reveals where the posterior is sensitive to the priors, and where collections of trees are composed of distinct clusters. We use the metric to define median trees summarising these clusters. Quantitative tools to compare transmission trees to each other will be required for assessing MCMC convergence, exploring posterior trees and benchmarking diverse methods as this field continues to mature.

    McGrath T, Murphy KG, Jones NS, 2018,

    Quantitative approaches to energy and glucose homeostasis: machine learning and modelling for precision understanding and prediction

    Aryaman J, Hoitzing H, Burgstaller JP, Johnston IG, Jones NSet al., 2017,

    Mitochondrial heterogeneity, metabolic scaling and cell death

    , BIOESSAYS, Vol: 39, ISSN: 0265-9247
    Aryaman J, Johnston IG, Jones NS, 2017,

    Mitochondrial DNA density homeostasis accounts for a threshold effect in a cybrid model of a human mitochondrial disease

    , BIOCHEMICAL JOURNAL, Vol: 474, Pages: 4019-4034, ISSN: 0264-6021
    Beguerisse-Diaz M, McLennan AK, Garduño-Hernández G, Barahona M, Ulijaszek SJet al., 2017,

    The 'who' and 'what' of #diabetes on Twitter

    , Digital Health, Vol: 3, Pages: 1-29, ISSN: 2055-2076

    Social media are being increasingly used for health promotion, yet thelandscape of users, messages and interactions in such fora is poorlyunderstood. Studies of social media and diabetes have focused mostly onpatients, or public agencies addressing it, but have not looked broadly at allthe participants or the diversity of content they contribute. We study Twitterconversations about diabetes through the systematic analysis of 2.5 milliontweets collected over 8 months and the interactions between their authors. Weaddress three questions: (1) what themes arise in these tweets?; (2) who arethe most influential users?; (3) which type of users contribute to whichthemes? We answer these questions using a mixed-methods approach, integratingtechniques from anthropology, network science and information retrieval such asthematic coding, temporal network analysis, and community and topic detection.Diabetes-related tweets fall within broad thematic groups: health information,news, social interaction, and commercial. At the same time, humorous messagesand references to popular culture appear consistently, more than any other typeof tweet. We classify authors according to their temporal 'hub' and 'authority'scores. Whereas the hub landscape is diffuse and fluid over time, topauthorities are highly persistent across time and comprise bloggers, advocacygroups and NGOs related to diabetes, as well as for-profit entities withoutspecific diabetes expertise. Top authorities fall into seven interestcommunities as derived from their Twitter follower network. Our findings haveimplications for public health professionals and policy makers who seek to usesocial media as an engagement tool and to inform policy design.

    Colijn C, Jones N, Johnston IG, Yaliraki S, Barahona Met al., 2017,

    Toward Precision Healthcare: Context and Mathematical Challenges

    , FRONTIERS IN PHYSIOLOGY, Vol: 8, ISSN: 1664-042X
    Dattani J, Barahona M, 2017,

    Stochastic models of gene transcription with upstream drives: Exact solution and sample path characterisation

    , Journal of the Royal Society Interface, Vol: 14, ISSN: 1742-5689

    Gene transcription is a highly stochastic and dynamic process. As a result, the mRNA copynumber of a given gene is heterogeneous both between cells and across time. We present a frameworkto model gene transcription in populations of cells with time-varying (stochastic or deterministic)transcription and degradation rates. Such rates can be understood as upstream cellular drivesrepresenting the effect of different aspects of the cellular environment. We show that the full solutionof the master equation contains two components: a model-specific, upstream effective drive, whichencapsulates the effect of cellular drives (e.g., entrainment, periodicity or promoter randomness),and a downstream transcriptional Poissonian part, which is common to all models. Our analyticalframework treats cell-to-cell and dynamic variability consistently, unifying several approaches in theliterature. We apply the obtained solution to characterise different models of experimental relevance,and to explain the influence on gene transcription of synchrony, stationarity, ergodicity, as well asthe effect of time-scales and other dynamic characteristics of drives. We also show how the solutioncan be applied to the analysis of noise sources in single-cell data, and to reduce the computationalcost of stochastic simulations.

    Didelot X, Fraser C, Gardy J, Colijn Cet al., 2017,

    Genomic Infectious Disease Epidemiology in Partially Sampled and Ongoing Outbreaks

    , MOLECULAR BIOLOGY AND EVOLUTION, Vol: 34, Pages: 997-1007, ISSN: 0737-4038
    Fulcher BD, Jones NS, 2017,

    hctsa: A Computational Framework for Automated Time-Series Phenotyping Using Massive Feature Extraction

    , CELL SYSTEMS, Vol: 5, Pages: 527-+, ISSN: 2405-4712
    Gosztolai A, Schumacher J, Behrends V, Bundy JG, Heydenreich F, Bennett MH, Buck M, Barahona Met al., 2017,

    GlnK Facilitates the Dynamic Regulation of Bacterial Nitrogen AssimilationS

    , BIOPHYSICAL JOURNAL, Vol: 112, Pages: 2219-2230, ISSN: 0006-3495

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