Publications

Publications

You can also access our individual websites (via the Members page) for further information about our research and lists of our publications.

Search or filter publications

Filter by type:

Filter by publication type

Filter by year:

to

Results

  • Showing results for:
  • Reset all filters

Search results

  • Journal article
    Branch T, Girvan P, Barahona M, Ying Let al., 2015,

    Introduction of a Fluorescent Probe to Amyloid-β to Reveal Kinetic Insights into Its Interactions with Copper(II)

    , ANGEWANDTE CHEMIE-INTERNATIONAL EDITION, Vol: 54, Pages: 1227-1230, ISSN: 1433-7851
  • Journal article
    Beguerisse-Díaz M, Garduño-Hernández G, Vangelov B, Yaliraki SN, Barahona Met al., 2014,

    Interest communities and flow roles in directed networks: the Twitter network of the UK riots

    , J. R. Soc. Interface 6 December 2014, Vol: 11

    Directionality is a crucial ingredient in many complex networks in whichinformation, energy or influence are transmitted. In such directed networks,analysing flows (and not only the strength of connections) is crucial to revealimportant features of the network that might go undetected if the orientationof connections is ignored. We showcase here a flow-based approach for communitydetection in networks through the study of the network of the most influentialTwitter users during the 2011 riots in England. Firstly, we use directed MarkovStability to extract descriptions of the network at different levels ofcoarseness in terms of interest communities, i.e., groups of nodes within whichflows of information are contained and reinforced. Such interest communitiesreveal user groupings according to location, profession, employer, and topic.The study of flows also allows us to generate an interest distance, whichaffords a personalised view of the attention in the network as viewed from thevantage point of any given user. Secondly, we analyse the profiles of incomingand outgoing long-range flows with a combined approach of role-based similarityand the novel relaxed minimum spanning tree algorithm to reveal that the usersin the network can be classified into five roles. These flow roles go beyondthe standard leader/follower dichotomy and differ from classifications based onregular/structural equivalence. We then show that the interest communities fallinto distinct informational organigrams characterised by a different mix ofuser roles reflecting the quality of dialogue within them. Our genericframework can be used to provide insight into how flows are generated,distributed, preserved and consumed in directed networks.
  • Journal article
    Thomas P, Fleck C, Grima R, Popovic Net al., 2014,

    System size expansion using Feynman rules and diagrams

    , JOURNAL OF PHYSICS A-MATHEMATICAL AND THEORETICAL, Vol: 47, ISSN: 1751-8113

    Few analytical methods exist for quantitative studies of large fluctuations in stochastic systems. In this article, we develop a simple diagrammatic approach to the chemical master equation that allows us to calculate multi-time correlation functions which are accurate to any desired order in van Kampenʼs system size expansion. Specifically, we present a set of Feynman rules from which this diagrammatic perturbation expansion can be constructed algorithmically. We then apply the methodology to derive in closed form the leading order corrections to the linear noise approximation of the intrinsic noise power spectrum for general biochemical reaction networks. Finally, we illustrate our results by describing noise-induced oscillations in the Brusselator reaction scheme which are not captured by the common linear noise approximation.
  • Journal article
    Georgiou PS, Barahona M, Yaliraki SN, Drakakis EMet al., 2014,

    On memristor ideality and reciprocity

    , Microelectronics Journal, Vol: 45, Pages: 1363-1371, ISSN: 0026-2692
  • Journal article
    Billeh YN, Schaub MT, Anastassiou CA, Barahona M, Koch Cet al., 2014,

    Revealing cell assemblies at multiple levels of granularity

    , JOURNAL OF NEUROSCIENCE METHODS, Vol: 236, Pages: 92-106, ISSN: 0165-0270
  • Journal article
    Wang B, Barahona M, Buck M, 2014,

    Engineering modular and tunable genetic amplifiers for scaling transcriptional signals in cascaded gene networks

    , Nucleic Acids Research

    Synthetic biology aims to control and reprogram signal processing pathways within living cells so as to realize repurposed, beneficial applications. Here we report the design and construction of a set of modular and gain-tunable genetic amplifiers in Escherichia coli capable of amplifying a transcriptional signal with wide tunable-gain control in cascaded gene networks. The devices are engineered using orthogonal genetic components (hrpRS, hrpV and PhrpL) from the hrp (hypersensitive response and pathogenicity) gene regulatory network in Pseudomonas syringae. The amplifiers can linearly scale up to 21-fold the transcriptional input with a large output dynamic range, yet not introducing significant time delay or significant noise during signal amplification. The set of genetic amplifiers achieves different gains and input dynamic ranges by varying the expression levels of the underlying ligand-free activator proteins in the device. As their electronic counterparts, these engineered transcriptional amplifiers can act as fundamental building blocks in the design of biological systems by predictably and dynamically modulating transcriptional signal flows to implement advanced intra- and extra-cellular control functions.
  • Conference paper
    Noseda M, Harada M, Mcsweeney S, Leja T, Belian E, Macaulay I, Paiva MA, Jacobsen SE, Barahona M, Schneider MDet al., 2014,

    PDGFRalpha demarcates the cardiogenic and clonogenic Sca-1+stem cell

    , 3rd Congress of the ESC-Council-on-Basic-Cardiovascular-Science on Frontiers in Cardio Vascular Biology, Publisher: OXFORD UNIV PRESS, ISSN: 0008-6363
  • Journal article
    Lambiotte R, Delvenne JC, Barahona M, 2014,

    Random walks, Markov processes and the multiscale modular organization of complex networks

    , IEEE Transactions on Network Science and Engineering, Vol: 1, Pages: 76-90

    Most methods proposed to uncover communities in complex networks rely on combinatorial graph properties. Usually an edge-counting quality function, such as modularity, is optimized over all partitions of the graph compared against a null random graph model. Here we introduce a systematic dynamical framework to design and analyze a wide variety of quality functions for community detection. The quality of a partition is measured by its Markov Stability, a time-parametrized function defined in terms of the statistical properties of a Markov process taking place on the graph. The Markov process provides a dynamical sweeping across all scales in the graph, and the time scale is an intrinsic parameter that uncovers communities at different resolutions. This dynamic-based community detection leads to a compound optimization, which favours communities of comparable centrality (as defined by the stationary distribution), and provides a unifying framework for spectral algorithms, as well as different heuristics for community detection, including versions of modularity and Potts model. Our dynamic framework creates a systematic link between different stochastic dynamics and their corresponding notions of optimal communities under distinct (node and edge) centralities. We show that the Markov Stability can be computed efficiently to find multi-scale community structure in large networks.
  • Journal article
    Liu Y, Kaneda R, Leja TW, Subkhankulova T, Tolmachov O, Minchiotti G, Schwartz RJ, Barahona M, Schneider MDet al., 2014,

    <i>Hhex</i> and <i>C</i>er1 Mediate the Sox17 Pathway for Cardiac Mesoderm Formation in Embryonic Stem Cells

    , STEM CELLS, Vol: 32, Pages: 1515-1526, ISSN: 1066-5099
  • Journal article
    Thomas P, Popovic N, Grima R, 2014,

    Phenotypic switching in gene regulatory networks

    , Proceedings of the National Academy of Sciences of the United States of America, Vol: 111, Pages: 6994-6999, ISSN: 0027-8424

    Noise in gene expression can lead to reversible phenotypic switching. Several experimental studies have shown that the abundance distributions of proteins in a population of isogenic cells may display multiple distinct maxima. Each of these maxima may be associated with a subpopulation of a particular phenotype, the quantification of which is important for understanding cellular decision-making. Here, we devise a methodology which allows us to quantify multimodal gene expression distributions and single-cell power spectra in gene regulatory networks. Extending the commonly used linear noise approximation, we rigorously show that, in the limit of slow promoter dynamics, these distributions can be systematically approximated as a mixture of Gaussian components in a wide class of networks. The resulting closed-form approximation provides a practical tool for studying complex nonlinear gene regulatory networks that have thus far been amenable only to stochastic simulation. We demonstrate the applicability of our approach in a number of genetic networks, uncovering previously unidentified dynamical characteristics associated with phenotypic switching. Specifically, we elucidate how the interplay of transcriptional and translational regulation can be exploited to control the multimodality of gene expression distributions in two-promoter networks. We demonstrate how phenotypic switching leads to birhythmical expression in a genetic oscillator, and to hysteresis in phenotypic induction, thus highlighting the ability of regulatory networks to retain memory.

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.

Request URL: http://www.imperial.ac.uk:80/respub/WEB-INF/jsp/search-t4-html.jsp Request URI: /respub/WEB-INF/jsp/search-t4-html.jsp Query String: id=219&limit=10&page=13&respub-action=search.html Current Millis: 1713540532732 Current Time: Fri Apr 19 16:28:52 BST 2024