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  • 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.
  • Journal article
    Thomas P, 2018,

    Analysis of cell size homeostasis at the single-cell and population level

    , Frontiers in Physics, Vol: 6, ISSN: 2296-424X

    Growth pervades all areas of life from single cells to cell populations to tissues. Cell size often fluctuates significantly from cell to cell and from generation to generation. Here we present a unified framework to predict the statistics of cell size variations within a lineage tree of a proliferating population. We analytically characterize (i) the distributions of cell size snapshots, (ii) the distribution within a population tree, and (iii) the distribution of lineages across the tree. Surprisingly, these size distributions differ significantly from observing single cells in isolation. In populations, cells seemingly grow to different sizes, typically exhibit less cell-to-cell variability and often display qualitatively different sensitivities to cell cycle noise and division errors. We demonstrate the key findings using recent single-cell data and elaborate on the implications for the ability of cells to maintain a narrow size distribution and the emergence of different power laws in these distributions.
  • Journal article
    Tomazou M, Barahona M, Polizzi K, Stan Get al., 2018,

    Computational re-design of synthetic genetic oscillators for independent amplitude and frequency modulation

    , Cell Systems, Vol: 6, Pages: 508-520.e5, ISSN: 2405-4712

    To perform well in biotechnology applications, synthetic genetic oscillators must be engineered to allow independent modulation of amplitude and period. This need is currently unmet. Here, we demonstrate computationally how two classic genetic oscillators, the dual-feedback oscillator and the repressilator, can be re-designed to provide independent control of amplitude and period and improve tunability—that is, a broad dynamic range of periods and amplitudes accessible through the input “dials.” Our approach decouples frequency and amplitude modulation by incorporating an orthogonal “sink module” where the key molecular species are channeled for enzymatic degradation. This sink module maintains fast oscillation cycles while alleviating the translational coupling between the oscillator's transcription factors and output. We characterize the behavior of our re-designed oscillators over a broad range of physiologically reasonable parameters, explain why this facilitates broader function and control, and provide general design principles for building synthetic genetic oscillators that are more precisely controllable.
  • Conference paper
    Pezet M, Gomez-Duran A, Aryaman J, Jones NS, Chinnery PFet al., 2018,

    Understanding the mechanism underpinning the transmission of mtDNA mutations

    , 11th UK Neuromuscular Translational Research Conference, Publisher: PERGAMON-ELSEVIER SCIENCE LTD, Pages: S35-S35, ISSN: 0960-8966
  • Journal article
    McGrath TM, Murphy KG, Jones NS, 2018,

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

    , Journal of the Royal Society Interface, Vol: 15, ISSN: 1742-5662

    Obesity is a major global public health problem. Understanding how energy homeostasis is regulated, and can become dysregulated, is crucial for developing new treatments for obesity. Detailed recording of individual behaviour and new imaging modalities offer the prospect of medically relevant models of energy homeostasis that are both understandable and individually predictive. The profusion of data from these sources has led to an interest in applying machine learning techniques to gain insight from these large, relatively unstructured datasets. We review both physiological models and machine learning results across a diverse range of applications in energy homeostasis, and highlight how modelling and machine learning can work together to improve predictive ability. We collect quantitative details in a comprehensive mathematical supplement. We also discuss the prospects of forecasting homeostatic behaviour and stress the importance of characterizing stochasticity within and between individuals in order to provide practical, tailored forecasts and guidance to combat the spread of obesity.
  • Journal article
    Fricker M, Heaton L, Jones N, Obara B, Müller SJ, Meyer AJet al., 2018,

    Quantitation of ER Structure and Function.

    , Methods Mol Biol, Vol: 1691, Pages: 43-66

    The plant endoplasmic reticulum forms a network of tubules connected by three-way junctions or sheet-like cisternae. Although the network is three-dimensional, in many plant cells, it is constrained to a thin volume sandwiched between the vacuole and plasma membrane, effectively restricting it to a 2-D planar network. The structure of the network, and the morphology of the tubules and cisternae can be automatically extracted following intensity-independent edge-enhancement and various segmentation techniques to give an initial pixel-based skeleton, which is then converted to a graph representation. Collectively, this approach yields a wealth of quantitative metrics for ER structure and can be used to describe the effects of pharmacological treatments or genetic manipulation. The software is publicly available.
  • Journal article
    Salnikov V, Cassese D, Lambiotte R, Jones NSet al., 2018,

    Co-occurrence simplicial complexes in mathematics: identifying the holes of knowledge.

    , Appl Netw Sci, Vol: 3

    In the last years complex networks tools contributed to provide insights on the structure of research, through the study of collaboration, citation and co-occurrence networks. The network approach focuses on pairwise relationships, often compressing multidimensional data structures and inevitably losing information. In this paper we propose for the first time a simplicial complex approach to word co-occurrences, providing a natural framework for the study of higher-order relations in the space of scientific knowledge. Using topological methods we explore the conceptual landscape of mathematical research, focusing on homological holes, regions with low connectivity in the simplicial structure. We find that homological holes are ubiquitous, which suggests that they capture some essential feature of research practice in mathematics. k-dimensional holes die when every concept in the hole appears in an article together with other k+1 concepts in the hole, hence their death may be a sign of the creation of new knowledge, as we show with some examples. We find a positive relation between the size of a hole and the time it takes to be closed: larger holes may represent potential for important advances in the field because they separate conceptually distant areas. We provide further description of the conceptual space by looking for the simplicial analogs of stars and explore the likelihood of edges in a star to be also part of a homological cycle. We also show that authors' conceptual entropy is positively related with their contribution to homological holes, suggesting that polymaths tend to be on the frontier of research.
  • Journal article
    Thomas P, 2017,

    Making sense of snapshot data: ergodic principle for clonal cell populations

    , Journal of the Royal Society Interface, Vol: 14, ISSN: 1742-5662

    Population growth is often ignored when quantifying gene expression levels across clonal cell populations. We develop a framework for obtaining the molecule number distributions in an exponentially growing cell population taking into account its age structure. In the presence of generation time variability, the average acquired across a population snapshot does not obey the average of a dividing cell over time, apparently contradicting ergodicity between single cells and the population. Instead, we show that the variation observed across snapshots with known cell age is captured by cell histories, a single-cell measure obtained from tracking an arbitrary cell of the population back to the ancestor from which it originated. The correspondence between cells of known age in a population with their histories represents an ergodic principle that provides a new interpretation of population snapshot data. We illustrate the principle using analytical solutions of stochastic gene expression models in cell populations with arbitrary generation time distributions. We further elucidate that the principle breaks down for biochemical reactions that are under selection, such as the expression of genes conveying antibiotic resistance, which gives rise to an experimental criterion with which to probe selection on gene expression fluctuations.

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