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  • Journal article
    Martins B, Tooke AK, Thomas P, Locke JCWet al., 2018,

    Cell size control driven by the circadian clock and environment in cyanobacteria

    , Proceedings of the National Academy of Sciences, Vol: 115, Pages: E11415-E11424, ISSN: 0027-8424

    How cells maintain their size has been extensively studied under constant conditions. In the wild, however, cells rarely experience constant environments. Here, we examine how the 24-hour circadian clock and environmental cycles modulate cell size control and division timings in the cyanobacterium Synechococcus elongatus using single-cell time-lapse microscopy. Under constant light, wild type cells follow an apparent sizer-like principle. Closer inspection reveals that the clock generates two subpopulations, with cells born in the subjective day following different division rules from cells born in subjective night. A stochastic model explains how this behaviour emerges from the interaction of cell size control with the clock. We demonstrate that the clock continuously modulates the probability of cell division throughout day and night, rather than solely applying an on-off gate to division as previously proposed. Iterating between modelling and experiments, we go on to identify an effective coupling of the division rate to time of day through the combined effects of the environment and the clock on cell division. Under naturally graded light-dark cycles, this coupling narrows the time window of cell divisions and shifts divisions away from when light levels are low and cell growth is reduced. Our analysis allows us to disentangle, and predict the effects of, the complex interactions between the environment, clock, and cell size control.

  • Journal article
    Keogh M, Wei W, Aryaman J, Walker L, van den Ameele J, Coxhead J, Wilson I, Bashton M, Beck J, West J, Chen R, Haudenschild C, Bartha G, Luo S, Morris C, Jones N, Attems J, Chinnery Pet al., 2018,

    High prevalence of focal and multi-focal somatic genetic variants in the human brain

    , Nature Communications, Vol: 9, ISSN: 2041-1723

    Somatic mutations during stem cell division are responsible for several cancers. In principle, a similar process could occur during the intense cell proliferation accompanying human brain development, leading to the accumulation of regionally distributed foci of mutations. Using dual platform >5000-fold depth sequencing of 102 genes in 173 adult human brain samples, we detect and validate somatic mutations in 27 of 54 brains. Using a mathematical model of neurodevelopment and approximate Bayesian inference, we predict that macroscopic islands of pathologically mutated neurons are likely to be common in the general population. The detected mutation spectrum also includes DNMT3A and TET2 which are likely to have originated from blood cell lineages. Together, these findings establish developmental mutagenesis as a potential mechanism for neurodegenerative disorders, and provide a novel mechanism for the regional onset and focal pathology in sporadic cases.

  • Journal article
    Thomas P, Terradot G, Danos V, Weisse Aet al., 2018,

    Sources, propagation and consequences of stochasticity in cellular growth

    , Nature Communications, Vol: 9, ISSN: 2041-1723

    Growth impacts a range of phenotypic responses. Identifying the sources of growth variation and their propagation across the cellular machinery can thus unravel mechanisms that underpin cell decisions. We present a stochastic cell model linking gene expression, metabolism and replication to predict growth dynamics in single bacterial cells. Alongside we provide a theory to analyse stochastic chemical reactions coupled with cell divisions, enabling efficient parameter estimation, sensitivity analysis and hypothesis testing. The cell model recovers population-averaged data on growth-dependence of bacterial physiology and how growth variations in single cells change across conditions. We identify processes responsible for this variation and reconstruct the propagation of initial fluctuations to growth and other processes. Finally, we study drug-nutrient interactions and find that antibiotics can both enhance and suppress growth heterogeneity. Our results provide a predictive framework to integrate heterogeneous data and draw testable predictions with implications for antibiotic tolerance, evolutionary and synthetic biology.

  • Journal article
    Wei W, Keogh MJ, Aryaman J, Golder Z, Kullar PJ, Wilson I, Talbot K, Turner MR, McKenzie C-A, Troakes C, Attems J, Smith C, Sarraj SA, Morris CM, Ansorge O, Jones NS, Ironside JW, Chinnery PFet al., 2018,

    Frequency and signature of somatic variants in 1461 human brain exomes

    , Genetics in Medicine, Vol: 21, Pages: 904-912, ISSN: 1098-3600

    PURPOSE: To systematically study somatic variants arising during development in the human brain across a spectrum of neurodegenerative disorders. METHODS: In this study we developed a pipeline to identify somatic variants from exome sequencing data in 1461 diseased and control human brains. Eighty-eight percent of the DNA samples were extracted from the cerebellum. Identified somatic variants were validated by targeted amplicon sequencing and/or PyroMark® Q24. RESULTS: We observed somatic coding variants present in >10% of sampled cells in at least 1% of brains. The mutational signature of the detected variants showed a predominance of C>T variants most consistent with arising from DNA mismatch repair, occurred frequently in genes that are highly expressed within the central nervous system, and with a minimum somatic mutation rate of 4.25 × 10-10 per base pair per individual. CONCLUSION: These findings provide proof-of-principle that deleterious somatic variants can affect sizeable brain regions in at least 1% of the population, and thus have the potential to contribute to the pathogenesis of common neurodegenerative diseases.

  • Book chapter
    Voliotis M, Thomas P, Bowsher CG, Grima Ret al., 2018,

    The Extra Reaction Algorithm for Stochastic Simulation of Biochemical Reaction Systems in Fluctuating Environments

    , Quantitative Biology: Theory, Computational Methods, and Models, Editors: Munsky, Hlavacek, Tsimring
  • Journal article
    Beguerisse M, Bosque G, Oyarzun DA, Pico J, Barahona M, Beguerisse-Díaz M, Bosque G, Oyarzún D, Picó J, Barahona Met al., 2018,

    Flux-dependent graphs for metabolic networks

    , npj Systems Biology and Applications, Vol: 4, ISSN: 2056-7189

    Cells adapt their metabolic fluxes in response to changes in the environment. We present a framework for the systematic construction of flux-based graphs derived from organism-wide metabolic networks. Our graphs encode the directionality of metabolic flows via edges that represent the flow of metabolites from source to target reactions. The methodology can be applied in the absence of a specific biological context by modelling fluxes probabilistically, or can be tailored to different environmental conditions by incorporating flux distributions computed through constraint-based approaches such as Flux Balance Analysis. We illustrate our approach on the central carbon metabolism of Escherichia coli and on a metabolic model of human hepatocytes. The flux-dependent graphs under various environmental conditions and genetic perturbations exhibit systemic changes in their topological and community structure, which capture the re-routing of metabolic flows and the varying importance of specific reactions and pathways. By integrating constraint-based models and tools from network science, our framework allows the study of context-specific metabolic responses at a system level beyond standard pathway descriptions.

  • Journal article
    Keogh MJ, Wei W, Aryaman J, Wilson I, Talbot K, Turner MR, McKenzie C-A, Troakes C, Attems J, Smith C, Al Sarraj S, Morris CM, Ansorge O, Pickering-Brown S, Jones N, Ironside JW, Chinnery PFet al., 2018,

    Oligogenic genetic variation of neurodegenerative disease genes in 980 postmortem human brains

    , JOURNAL OF NEUROLOGY NEUROSURGERY AND PSYCHIATRY, Vol: 89, Pages: 813-816, ISSN: 0022-3050
  • Journal article
    Hodges M, Barahona M, Yaliraki SN, 2018,

    Allostery and cooperativity in multimeric proteins: bond-to-bond propensities in ATCase

    , SCIENTIFIC REPORTS, Vol: 8, ISSN: 2045-2322
  • Conference paper
    Altuncu MT, Mayer E, Yaliraki SN, Barahona Met al., 2018,

    From Text to Topics in Healthcare Records: An Unsupervised Graph Partitioning Methodology

    , 2018 KDD Conference Proceedings - MLMH: Machine Learning for Medicine and Healthcare

    Electronic Healthcare Records contain large volumes of unstructured data,including extensive free text. Yet this source of detailed information oftenremains under-used because of a lack of methodologies to extract interpretablecontent in a timely manner. Here we apply network-theoretical tools to analysefree text in Hospital Patient Incident reports from the National HealthService, to find clusters of documents with similar content in an unsupervisedmanner at different levels of resolution. We combine deep neural networkparagraph vector text-embedding with multiscale Markov Stability communitydetection applied to a sparsified similarity graph of document vectors, andshowcase the approach on incident reports from Imperial College Healthcare NHSTrust, London. The multiscale community structure reveals different levels ofmeaning in the topics of the dataset, as shown by descriptive terms extractedfrom the clusters of records. We also compare a posteriori against hand-codedcategories assigned by healthcare personnel, and show that our approachoutperforms LDA-based models. Our content clusters exhibit good correspondencewith two levels of hand-coded categories, yet they also provide further medicaldetail in certain areas and reveal complementary descriptors of incidentsbeyond the external classification taxonomy.

  • Conference paper
    Altuncu MT, Yaliraki SN, Barahona M, 2018,

    Content-driven, unsupervised clustering of news articles through multiscale graph partitioning

    , KDD 2018 - Workshop on Data Science Journalism and Media (DSJM)

    The explosion in the amount of news and journalistic content being generatedacross the globe, coupled with extended and instantaneous access to informationthrough online media, makes it difficult and time-consuming to monitor newsdevelopments and opinion formation in real time. There is an increasing needfor tools that can pre-process, analyse and classify raw text to extractinterpretable content; specifically, identifying topics and content-drivengroupings of articles. We present here such a methodology that brings togetherpowerful vector embeddings from Natural Language Processing with tools fromGraph Theory that exploit diffusive dynamics on graphs to reveal naturalpartitions across scales. Our framework uses a recent deep neural network textanalysis methodology (Doc2vec) to represent text in vector form and thenapplies a multi-scale community detection method (Markov Stability) topartition a similarity graph of document vectors. The method allows us toobtain clusters of documents with similar content, at different levels ofresolution, in an unsupervised manner. We showcase our approach with theanalysis of a corpus of 9,000 news articles published by Vox Media over oneyear. Our results show consistent groupings of documents according to contentwithout a priori assumptions about the number or type of clusters to be found.The multilevel clustering reveals a quasi-hierarchy of topics and subtopicswith increased intelligibility and improved topic coherence as compared toexternal taxonomy services and standard topic detection methods.

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