Publications
211 results found
Altuncu MT, Mayer E, Yaliraki SN, et 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.
Altuncu T, 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.
Tomazou M, Barahona M, Polizzi K, et 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.
Vangelov B, Barahona M, 2017, Modelling the Dynamics of Biological Systems with the Geometric Hidden Markov Model
<jats:title>ABSTRACT</jats:title><jats:p>Many biological processes can be described geometrically in a simple way: stem cell differentiation can be represented as a branching tree and cell division can be depicted as a cycle. In this paper we introduce the geometric hidden Markov model (GHMM), a dynamical model whose goal is to capture the low-dimensional characteristics of biological processes from multivariate time series data. The framework integrates a graph-theoretical algorithm for dimensionality reduction with a latent variable model for sequential data. We analyzed time series data generated by an in silico model of a biomolecular circuit, the represillator. The trained model has a simple structure: the latent Markov chain corresponds to a two-dimensional lattice. We show that the short-term and long-term predictions of the GHMM reflect the oscillatory behaviour of the genetic circuit. Analysis of the inferred model with a community detection methods leads to a coarse-grained representation of the process.</jats:p>
Liu Z, Barahona M, 2017, Geometric multiscale community detection: Markov stability and vector partitioning, Journal of Complex Networks, Vol: 6, Pages: 157-172, 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.
Branch T, Barahona M, Dodson C, et al., 2017, Kinetic analysis reveals the identity of Aβ-metal complex responsible for the initial aggregation of Aβ in the synapse, ACS Chemical Neuroscience, Vol: 8, Pages: 1970-1979, ISSN: 1948-7193
The mechanism of Aβ aggregation in the absence of metal ions is well established, yet the role that Zn2+ and Cu2+, the two most studied metal ions, released during neurotransmission, paly in promoting Aβ aggregation in the vicinity of neuronal synapses remains elusive. Here we report the kinetics of Zn2+ binding to Aβ and Zn2+/Cu2+ binding to Aβ-Cu to form ternary complexes under near physiological conditions (nM Aβ, μM metal ions). We find that these reactions are several orders of magnitude slower than Cu2+ binding to Aβ. Coupled reaction-diffusion simulations of the interactions of synaptically released metal ions with Aβ show that up to a third of Aβ is Cu2+-bound under repetitive metal ion release, while any other Aβ-metal complexes (including Aβ-Zn) are insignificant. We therefore conclude that Zn2+ is unlikely to play an important role in the very early stages (i.e., dimer formation) of Aβ aggregation, contrary to a widely held view in the subject. We propose that targeting the specific interactions between Cu2+ and Aβ may be a viable option in drug development efforts for early stages of AD.
Kiselev V, Kirschner K, Schaub MT, et al., 2017, SC3: consensus clustering of single-cell RNA-seq data, Nature Methods, Vol: 14, Pages: 483-486, ISSN: 1548-7105
Single-cell RNA-seq enables the quantitative characterization of cell types based on global transcriptome profiles. We present single-cell consensus clustering (SC3), a user-friendly tool for unsupervised clustering, which achieves high accuracy and robustness by combining multiple clustering solutions through a consensus approach (http://bioconductor.org/packages/SC3). We demonstrate that SC3 is capable of identifying subclones from the transcriptomes of neoplastic cells collected from patients.
Gosztolai A, Schumacher J, Behrends V, et al., 2017, GlnK Facilitates the Dynamic Regulation of Bacterial Nitrogen Assimilation., Biophysical journal, Vol: 112, Pages: 2219-2230, ISSN: 0006-3495
Ammonium assimilation in Escherichia coli is regulated by two paralogous proteins (GlnB and GlnK), which orchestrate interactions with regulators of gene expression, transport proteins, and metabolic pathways. Yet how they conjointly modulate the activity of glutamine synthetase, the key enzyme for nitrogen assimilation, is poorly understood. We combine experiments and theory to study the dynamic roles of GlnB and GlnK during nitrogen starvation and upshift. We measure time-resolved in vivo concentrations of metabolites, total and posttranslationally modified proteins, and develop a concise biochemical model of GlnB and GlnK that incorporates competition for active and allosteric sites, as well as functional sequestration of GlnK. The model predicts the responses of glutamine synthetase, GlnB, and GlnK under time-varying external ammonium level in the wild-type and two genetic knock-outs. Our results show that GlnK is tightly regulated under nitrogen-rich conditions, yet it is expressed during ammonium run-out and starvation. This suggests a role for GlnK as a buffer of nitrogen shock after starvation, and provides a further functional link between nitrogen and carbon metabolisms.
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- Citations: 9
Colijn C, Jones N, Johnston I, et al., 2017, Towards precision healthcare: context and mathematical challenges, Frontiers in Physiology, Vol: 8, ISSN: 1664-042X
Precision medicine refers to the idea of delivering the right treatment to the right patient at the right time, usually with a focus on a data-centred approach to this task. In this perspective piece, we use the term "precision healthcare" to describe the development of precision approaches that bridge from the individual to the population, taking advantage of individual-level data, but also taking the social context into account. These problems give rise to a broad spectrum of technical, scientific, policy, ethical and social challenges, and new mathematical techniques will be required to meet them. To ensure that the science underpin-ning "precision" is robust, interpretable and well-suited to meet the policy, ethical and social questions that such approaches raise, the mathematical methods for data analysis should be transparent, robust and able to adapt to errors and uncertainties. In particular, precision methodologies should capture the complexity of data, yet produce tractable descriptions at the relevant resolution while preserving intelligibility and traceability, so that they can be used by practitioners to aid decision-making. Through several case studies in this domain of precision healthcare, we argue that this vision requires the development of new mathematical frameworks, both in modelling and in data analysis and interpretation.
Schaub M, Billeh YN, Anastassiou CA, et al., 2017, Emergence of Slow-Switching Assemblies in Structured Neuronal Networks, PLOS COMPUTATIONAL BIOLOGY, Vol: 13, ISSN: 1553-734X
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.
Beguerisse-Diaz M, McLennan AK, Garduño-Hernández G, et 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.
Schaub MT, O'Clery N, Billeh YN, et al., 2016, Graph partitions and cluster synchronization in networks of oscillators, Chaos: an interdisciplinary journal of nonlinear science, Vol: 26, ISSN: 1054-1500
Synchronization over networks depends strongly on the structure of the coupling between the oscillators. When the coupling presents certain regularities, the dynamics can be coarse-grained into clusters by means of External Equitable Partitions of the network graph and their associated quotient graphs. We exploit this graph-theoretical concept to study the phenomenon of cluster synchronization, in which different groups of nodes converge to distinct behaviors. We derive conditions and properties of networks in which such clustered behavior emerges and show that the ensuing dynamics is the result of the localization of the eigenvectors of the associated graph Laplacians linked to the existence of invariant subspaces. The framework is applied to both linear and non-linear models, first for the standard case of networks with positive edges, before being generalized to the case of signed networks with both positive and negative interactions. We illustrate our results with examples of both signed and unsigned graphs for consensus dynamics and for partial synchronization of oscillator networks under the master stability function as well as Kuramoto oscillators.
Beguerisse Diaz M, Desikan R, Barahona M, 2016, Linear models of activation cascades: analytical solutions and coarse-graining of delayed signal transduction, Journal of the Royal Society Interface, Vol: 13, ISSN: 1742-5689
Cellular signal transduction usually involves activation cascades, the sequential activation of a series of proteins following the reception of an input signal. Here we study the classic model of weakly activated cascades and obtain analytical solutions for a variety of inputs. We show that in the special but important case of optimal-gain cascades (i.e., when the deactivation rates are identical) the downstream output of the cascade can be represented exactly as a lumped nonlinear module containing an incomplete gamma function with real parameters that depend on the rates and length of the cascade, as well as parameters of the input signal. The expressions obtained can be applied to the non-identical case when the deactivation rates are random to capture the variability in the cascade outputs. We also show that cascades can be rearranged so that blocks with similar rates can be lumped and represented through our nonlinear modules. Our results can be used both to represent cascades in computational models of differential equations and to fit data efficiently, by reducing the number of equations and parameters involved. In particular, the length of the cascade appears as a real-valued parameter and can thus be fitted in the same manner as Hill coefficients. Finally, we show how the obtained nonlinear modules can be used instead of delay differential equations to model delays in signal transduction.
Amor BRC, Schaub MT, Yaliraki S, et al., 2016, Prediction of allosteric sites and mediating interactions through bond-to-bond propensities, Nature Communications, Vol: 7, Pages: 1-13, ISSN: 2041-1723
Allostery is a fundamental mechanism of biological regulation, in which binding of a molecule at a distant location affects the active site of a protein. Allosteric sites provide targets to fine-tune protein activity, yet we lack computational methodologies to predict them. Here we present an efficient graph-theoretical framework to reveal allosteric interactions (atoms and communication pathways strongly coupled to the active site) without a priori information of their location. Using an atomistic graph with energy-weighted covalent and weak bonds, we define a bond-to-bond propensity quantifying the non-local effect of instantaneous bond fluctuations propagating through the protein. Significant interactions are then identified using quantile regression. We exemplify our method with three biologically important proteins: caspase-1, CheY, and h-Ras, correctly predicting key allosteric interactions, whose significance is additionally confirmed against a reference set of 100 proteins. The almost-linear scaling of our method renders it suitable for high-throughput searches for candidate allosteric sites.
Bacik KA, Schaub MT, Beguerisse-Diaz M, et 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.
Phoka E, Berditchevskaia A, Barahona M, et al., 2016, Long-term, layer-specific reverberant activity in the mouse somatosensory cortex following sensory stimulation
<jats:p>Neocortical circuits exhibit spontaneous neuronal activity whose functional relevance remains enigmatic. Several proposed functions assume that sensory experience can influence subsequent spontaneous activity. However, long-term alterations in spontaneous firing rates following sensory stimulation have not been reported until now. Here we show that multi-whisker, spatiotemporally rich stimulation of mouse vibrissae induces a laminar-specific, long-term increase of spontaneous activity in the somatosensory cortex. Such stimulation additionally produces stereotypical neural ensemble firing patterns from simultaneously recorded single neurons, which are maintained during spontaneous activity following stimulus offset. The increased neural activity and concomitant ensemble firing patterns are sustained for at least 25 minutes after stimulation, and specific to layers IV and Vb. In contrast, the same stimulation protocol applied to a single whisker fails to elicit this effect. Since layer Vb has the largest receptive fields and, together with layer IV, receives direct thalamic and lateral drive, the increase in firing activity could be the result of mechanisms involving the integration of spatiotemporal patterns across multiple whiskers. Our results provide direct evidence of modification of spontaneous cortical activity by sensory stimulation and could offer insight into the role of spatiotemporal integration in memory storage mechanisms for complex stimuli.</jats:p>
Amor B, Vuik S, Callahan R, et al., 2016, Community detection and role identification in directed networks: understanding the Twitter network of the care.data debate, Dynamic Networks and Cyber-Security, Editors: Adams, Heard, Publisher: World Scientific, Pages: 111-136, ISBN: 978-1-60558752-3
With the rise of social media as an important channel for the debate anddiscussion of public affairs, online social networks such as Twitter havebecome important platforms for public information and engagement by policymakers. To communicate effectively through Twitter, policy makers need tounderstand how influence and interest propagate within its network of users. Inthis chapter we use graph-theoretic methods to analyse the Twitter debatesurrounding NHS England's controversial care.data scheme. Directionality is acrucial feature of the Twitter social graph - information flows from thefollowed to the followers - but is often ignored in social network analyses;our methods are based on the behaviour of dynamic processes on the network andcan be applied naturally to directed networks. We uncover robust communities ofusers and show that these communities reflect how information flows through theTwitter network. We are also able to classify users by their differing roles indirecting the flow of information through the network. Our methods and resultswill be useful to policy makers who would like to use Twitter effectively as acommunication medium.
Georgiou PS, Yaliraki SN, Drakakis EM, et al., 2016, Window functions and sigmoidal behaviour of memristive systems, International Journal of Circuit Theory and Applications, Vol: 44, Pages: 1685-1696, ISSN: 0098-9886
Summary: A common approach to model memristive systems is to include empirical window functions to describe edge effects and nonlinearities in the change of the memristance. We demonstrate that under quite general conditions, each window function can be associated with a sigmoidal curve relating the normalised time-dependent memristance to the time integral of the input. Conversely, this explicit relation allows us to derive window functions suitable for the mesoscopic modelling of memristive systems from a variety of well-known sigmoidals. Such sigmoidal curves are defined in terms of measured variables and can thus be extracted from input and output signals of a device and then transformed to its corresponding window. We also introduce a new generalised window function that allows the flexible modelling of asymmetric edge effects in a simple manner.
Kuntz J, Ottobre M, Stan G-B, et al., 2016, BOUNDING STATIONARY AVERAGES OF POLYNOMIAL DIFFUSIONS VIA SEMIDEFINITE PROGRAMMING, SIAM JOURNAL ON SCIENTIFIC COMPUTING, Vol: 38, Pages: A3891-A3920, ISSN: 1064-8275
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- Citations: 17
Branch T, Girvan P, Barahona M, et al., 2015, Kinetics of amyloid-beta/metal ions interactions in the synaptic cleft: experiment and simulation, 10th EBSA European Biophysics Congress, Publisher: Springer Verlag, Pages: S230-S230, ISSN: 0175-7571
Sim A, Yaliraki SN, Barahona M, et al., 2015, Great cities look small., Journal of the Royal Society Interface, Vol: 12, ISSN: 1742-5689
Great cities connect people; failed cities isolate people. Despite the fundamental importance of physical, face-to-face social ties in the functioning of cities, these connectivity networks are not explicitly observed in their entirety. Attempts at estimating them often rely on unrealistic over-simplifications such as the assumption of spatial homogeneity. Here we propose a mathematical model of human interactions in terms of a local strategy of maximizing the number of beneficial connections attainable under the constraint of limited individual travelling-time budgets. By incorporating census and openly available online multi-modal transport data, we are able to characterize the connectivity of geometrically and topologically complex cities. Beyond providing a candidate measure of greatness, this model allows one to quantify and assess the impact of transport developments, population growth, and other infrastructure and demographic changes on a city. Supported by validations of gross domestic product and human immunodeficiency virus infection rates across US metropolitan areas, we illustrate the effect of changes in local and city-wide connectivities by considering the economic impact of two contemporary inter- and intra-city transport developments in the UK: High Speed 2 and London Crossrail. This derivation of the model suggests that the scaling of different urban indicators with population size has an explicitly mechanistic origin.
Noseda M, Harada M, McSweeney S, et al., 2015, PDGFRα demarcates the cardiogenic clonogenic Sca1(+) stem/progenitor cell in adult murine myocardium, Nature Communications, Vol: 6, ISSN: 2041-1723
Cardiac progenitor/stem cells in adult hearts represent an attractive therapeutic target for heart regeneration, though (inter)-relationships among reported cells remain obscure. Using single-cell qRT-PCR and clonal analyses, here we define four subpopulations of cardiac progenitor/stem cells in adult mouse myocardium all sharing stem cell antigen-1 (Sca1), based on side population (SP) phenotype, PECAM-1 (CD31) and platelet-derived growth factor receptor-α (PDGFRα) expression. SP status predicts clonogenicity and cardiogenic gene expression (Gata4/6, Hand2 and Tbx5/20), properties segregating more specifically to PDGFRα(+) cells. Clonal progeny of single Sca1(+) SP cells show cardiomyocyte, endothelial and smooth muscle lineage potential after cardiac grafting, augmenting cardiac function although durable engraftment is rare. PDGFRα(-) cells are characterized by Kdr/Flk1, Cdh5, CD31 and lack of clonogenicity. PDGFRα(+)/CD31(-) cells derive from cells formerly expressing Mesp1, Nkx2-5, Isl1, Gata5 and Wt1, distinct from PDGFRα(-)/CD31(+) cells (Gata5 low; Flk1 and Tie2 high). Thus, PDGFRα demarcates the clonogenic cardiogenic Sca1(+) stem/progenitor cell.
Stroud J, Barahona M, Pereira T, 2015, Dynamics of cluster synchronisation in modular networks: implications for structural and functional networks, Applications of Chaos and Nonlinear Dynamics in Science and Engineering - Vol. 4, Editors: Banerjee, Rondoni, Publisher: Springer International Publishing, Pages: 107-130, ISBN: 978-3-319-17036-7
Experimental results often do not assess network structure; rather, thenetwork structure is inferred by the dynamics of the nodes. From the dynamics of the nodes one then constructs a network of functional relations, termed the functional network. A fundamental question in the analysis of complex systems concerns the relation between functional and structural networks. Using synchronisation as a paradigm for network functionality, we study the dynamics of cluster formation in functional networks. We show that the functional network can drastically differ from the structural network. We uncover the mechanism driving these bifurcations by obtaining necessary conditions for modular synchronisation.
Wang B, Barahona M, Buck M, 2015, Amplification of small molecule-inducible gene expression via tuning of intracellular receptor densities, NUCLEIC ACIDS RESEARCH, Vol: 43, Pages: 1955-1964, ISSN: 0305-1048
Branch T, Barahona M, Ying L, 2015, Secondary Metal Binding to Amyloid-Beta Monomer is Insignificant under Synaptic Conditions, 59th Annual Meeting of the Biophysical-Society, Publisher: CELL PRESS, Pages: 385A-385A, ISSN: 0006-3495
Branch T, Girvan P, Barahona M, et 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
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- Citations: 40
Beguerisse-Díaz M, Garduño-Hernández G, Vangelov B, et 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.
Georgiou PS, Barahona M, Yaliraki SN, et al., 2014, On memristor ideality and reciprocity, Microelectronics Journal, Vol: 45, Pages: 1363-1371, ISSN: 0026-2692
Billeh YN, Schaub MT, Anastassiou CA, et al., 2014, Revealing cell assemblies at multiple levels of granularity, JOURNAL OF NEUROSCIENCE METHODS, Vol: 236, Pages: 92-106, ISSN: 0165-0270
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- Citations: 20
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