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  • JOURNAL ARTICLE
    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.

  • JOURNAL ARTICLE
    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
  • JOURNAL ARTICLE
    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.

  • JOURNAL ARTICLE
    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
  • JOURNAL ARTICLE
    Kiselev VY, Kirschner K, Schaub MT, Andrews T, Yiu A, Chandra T, Natarajan KN, Reik W, Barahona M, Green AR, Hemberg Met al., 2017,

    SC3: consensus clustering of single-cell RNA-seq data

    , NATURE METHODS, Vol: 14, Pages: 483-+, ISSN: 1548-7091
  • JOURNAL ARTICLE
    Kuntz J, Thomas P, Stan G-B, Barahona Met al., 2017,

    Rigorous bounds on the stationary distributions of the chemical master equation via mathematical programming

    The stochastic dynamics of networks of biochemical reactions in living cellsare typically modelled using chemical master equations (CMEs). The stationarydistributions of CMEs are seldom solvable analytically, and few methods existthat yield numerical estimates with computable error bounds. Here, we presenttwo such methods based on mathematical programming techniques. First, we usesemidefinite programming to obtain increasingly tighter upper and lower boundson the moments of the stationary distribution for networks with rationalpropensities. Second, we employ linear programming to compute convergent upperand lower bounds on the stationary distributions themselves. The boundsobtained provide a computational test for the uniqueness of the stationarydistribution. In the unique case, the bounds collectively form an approximationof the stationary distribution accompanied with a computable $\ell^1$-errorbound. In the non-unique case, we explain how to adapt our approach so that ityields approximations of the ergodic distributions, also accompanied withcomputable error bounds. We illustrate our methodology through two biologicalexamples: Schl\"ogl's model and a toggle switch model.

  • JOURNAL ARTICLE
    Liu Z, Barahona M, 2017,

    Geometric multiscale community detection: Markov stability and vector partitioning

    , Journal of Complex Networks, ISSN: 2051-1329

    Multiscale community detection can be viewed from a dynamical perspective within the Markov stability framework, which uses the diffusion of a Markov process on the graph to uncover intrinsic network substructures across all scales. Here we reformulate multiscale community detection as a max-sum length vector partitioning problem with respect to the set of time-dependent node vectors expressed in terms of eigenvectors of the transition matrix. This formulation provides a geometric interpretation of Markov stability in terms of a time-dependent spectral embedding, where the Markov time acts as an inhomogeneous geometric resolution factor that zooms the components of the node vectors at different rates. Our geometric formulation encompasses both modularity and the multi-resolution Potts model, which are shown to correspond to vector partitioning in a pseudo-Euclidean space, and is also linked to spectral partitioning methods, where the number of eigenvectors used corresponds to the dimensionality of the underlying embedding vector space. Inspired by the Louvain optimization for community detection, we then propose an algorithm based on a graph-theoretical heuristic for the vector partitioning problem. We apply the algorithm to the spectral optimization of modularity and Markov stability community detection. The spectral embedding based on the transition matrix eigenvectors leads to improved partitions with higher information content and higher modularity than the eigen-decomposition of the modularity matrix. We illustrate the results with random network benchmarks.

  • JOURNAL ARTICLE
    Amor BRC, Schaub MT, Yaliraki SN, Barahona Met al., 2016,

    Prediction of allosteric sites and mediating interactions through bond-to-bond propensities

    , NATURE COMMUNICATIONS, Vol: 7, ISSN: 2041-1723
  • BOOK CHAPTER
    Amor BRC, Vuik SI, Callahan R, Darzi A, Yaliraki SN, Barahona Met al., 2016,

    Community detection and role identification in directed networks: Understanding the twitter network of the care.data debate

    , Dynamic Networks and Cyber-Security, Pages: 111-136, ISBN: 9781786340757

    © 2016 by World Scientific Publishing Europe Ltd. All rights reserved. With the rise of social media as an important channel for the debate and discussion of public affairs, online social networks such as Twitter have become important platforms for public information and engagement by policy makers. To communicate effectively through Twitter, policy makers need to understand how influence and interest propagate within its network of users. In this chapter, we use graph-theoretic methods to analyse the Twitter debate surrounding NHS England's controversial care.data scheme. Directionality is a crucial feature of the Twitter social graph - information flows from the followed to the followers - but is often ignored in social network analyses; our methods are based on the behaviour of dynamic processes on the network and can be applied naturally to directed networks. We uncover robust communities of users and show that these communities reflect how information flows through the Twitter network. We are also able to classify users by their differing roles in directing the flow of information through the network. Our methods and results will be useful to policy makers who would like to use Twitter effectively as a communication medium.

  • JOURNAL ARTICLE
    Bacik KA, Schaub MT, Beguerisse-Diaz M, Billeh YN, Barahona Met al., 2016,

    Flow-Based Network Analysis of the Caenorhabditis elegans Connectome

    , PLOS Computational Biology, Vol: 12, ISSN: 1553-734X

    We exploit flow propagation on the directed neuronal network of the nematode C. elegans to reveal dynamically relevant features of its connectome. We find flow-based groupings of neurons at different levels of granularity, which we relate to functional and anatomical constituents of its nervous system. A systematic in silico evaluation of the full set of single and double neuron ablations is used to identify deletions that induce the most severe disruptions of the multi-resolution flow structure. Such ablations are linked to functionally relevant neurons, and suggest potential candidates for further in vivo investigation. In addition, we use the directional patterns of incoming and outgoing network flows at all scales to identify flow profiles for the neurons in the connectome, without pre-imposing a priori categories. The four flow roles identified are linked to signal propagation motivated by biological input-response scenarios.

This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.

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