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• Journal article
  Wang B, Barahona M, Buck M, 2015,
  , NUCLEIC ACIDS RESEARCH, Vol: 43, Pages: 1955-1964, ISSN: 0305-1048
  • Cite
• Conference paper
  Branch T, Barahona M, Ying L, 2015,
  , 59th Annual Meeting of the Biophysical-Society, Publisher: CELL PRESS, Pages: 385A-385A, ISSN: 0006-3495
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• Journal article
  Branch T, Girvan P, Barahona M, Ying Let al., 2015,
  , ANGEWANDTE CHEMIE-INTERNATIONAL EDITION, Vol: 54, Pages: 1227-1230, ISSN: 1433-7851
  • Cite
  • Citations: 46
• Journal article
  Beguerisse-Díaz M, Garduño-Hernández G, Vangelov B, Yaliraki SN, Barahona Met al., 2014,
  , 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.

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• Journal article
  Thomas P, Fleck C, Grima R, Popovic Net al., 2014,
  , 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.

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• Journal article
  Georgiou PS, Barahona M, Yaliraki SN, Drakakis EMet al., 2014,
  , Microelectronics Journal, Vol: 45, Pages: 1363-1371, ISSN: 0026-2692
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• Journal article
  Billeh YN, Schaub MT, Anastassiou CA, Barahona M, Koch Cet al., 2014,
  , JOURNAL OF NEUROSCIENCE METHODS, Vol: 236, Pages: 92-106, ISSN: 0165-0270
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  • Citations: 20
• Journal article
  Wang B, Barahona M, Buck M, 2014,
  , 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.

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• Conference paper
  Noseda M, Harada M, Mcsweeney S, Leja T, Belian E, Macaulay I, Paiva MA, Jacobsen SE, Barahona M, Schneider MDet al., 2014,
  , 3rd Congress of the ESC-Council-on-Basic-Cardiovascular-Science on Frontiers in Cardio Vascular Biology, Publisher: OXFORD UNIV PRESS, ISSN: 0008-6363
  • Cite
  • Citations: 2
• Journal article
  Lambiotte R, Delvenne JC, Barahona M, 2014,
  , 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.

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  • Citations: 305

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