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  • 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.
  • Journal article
    Schaub MT, Lehmann J, Yaliraki SN, Barahona Met al., 2014,

    Structure of complex networks: Quantifying edge-to-edge relations by failure-induced flow redistribution

    , Network Science, Vol: 2, Pages: 66-89

    The analysis of complex networks has so far revolved mainly around the role of nodes and communities of nodes. However, the dynamics of interconnected systems is often focalized on edge processes, and a dual edge-centric perspective can often prove more natural. Here we present graph-theoretical measures to quantify edge-to-edge relations inspired by the notion of flow redistribution induced by edge failures. Our measures, which are related to the pseudo-inverse of the Laplacian of the network, are global and reveal the dynamical interplay between the edges of a network, including potentially non-local interactions. Our framework also allows us to define the embeddedness of an edge, a measure of how strongly an edge features in the weighted cuts of the network. We showcase the general applicability of our edge-centric framework through analyses of the Iberian power grid, traffic flow in road networks, and the C. elegans neuronal network.
  • Conference paper
    Branch T, Barahona M, Ying L, 2014,

    Kinetics of the Interconversion Between Two Physiologically Important Copper-Bound Amyloid-Beta Species

    , 58th Annual Meeting of the Biophysical-Society, Publisher: CELL PRESS, Pages: 682A-682A, ISSN: 0006-3495
  • Conference paper
    Branch T, Evans M, Barahona M, Ying Let al., 2014,

    Kinetics of Metal Amyloid-Beta Binding and Efficacy of Ligands Targeting Metal Amyloid-Beta Interactions

    , 58th Annual Meeting of the Biophysical-Society, Publisher: CELL PRESS, Pages: 39A-39A, ISSN: 0006-3495
  • Journal article
    Amor B, Yaliraki SN, Woscholski R, Barahona Met al., 2014,

    Uncovering allosteric pathways in caspase-1 using Markov transient analysis and multiscale community detection

    , MOLECULAR BIOSYSTEMS, Vol: 10, Pages: 2247-2258, ISSN: 1742-206X
  • Journal article
    Schumacher J, Behrends V, Pan Z, Brown DR, Heydenreich F, Lewis MR, Bennett MH, Razzaghi B, Komorowski M, Barahona M, Stumpf MPH, Wigneshweraraj S, Bundy JG, Buck Met al., 2013,

    Nitrogen and Carbon Status Are Integrated at the Transcriptional Level by the Nitrogen Regulator NtrC <i>In Vivo</i>

    , MBIO, Vol: 4, ISSN: 2150-7511
  • Journal article
    Thomas P, Straube AV, Timmer J, Fleck C, Grima Ret al., 2013,

    Signatures of nonlinearity in single cell noise-induced oscillations

    , JOURNAL OF THEORETICAL BIOLOGY, Vol: 335, Pages: 222-234, ISSN: 0022-5193
  • Journal article
    O'Clery N, Yuan Y, Stan G-B, Barahona Met al., 2013,

    Observability and coarse graining of consensus dynamics through the external equitable partition

    , PHYSICAL REVIEW E, Vol: 88, ISSN: 1539-3755
  • Journal article
    Thomas P, Matuschek H, Grima R, 2013,

    How reliable is the linear noise approximation of gene regulatory networks?

    , BMC Genomics, Vol: 14, ISSN: 1471-2164

    BackgroundThe linear noise approximation (LNA) is commonly used to predict how noise is regulated and exploited at the cellular level. These predictions are exact for reaction networks composed exclusively of first order reactions or for networks involving bimolecular reactions and large numbers of molecules. It is however well known that gene regulation involves bimolecular interactions with molecule numbers as small as a single copy of a particular gene. It is therefore questionable how reliable are the LNA predictions for these systems.ResultsWe implement in the software package intrinsic Noise Analyzer (iNA), a system size expansion based method which calculates the mean concentrations and the variances of the fluctuations to an order of accuracy higher than the LNA. We then use iNA to explore the parametric dependence of the Fano factors and of the coefficients of variation of the mRNA and protein fluctuations in models of genetic networks involving nonlinear protein degradation, post-transcriptional, post-translational and negative feedback regulation. We find that the LNA can significantly underestimate the amplitude and period of noise-induced oscillations in genetic oscillators. We also identify cases where the LNA predicts that noise levels can be optimized by tuning a bimolecular rate constant whereas our method shows that no such regulation is possible. All our results are confirmed by stochastic simulations.ConclusionThe software iNA allows the investigation of parameter regimes where the LNA fares well and where it does not. We have shown that the parametric dependence of the coefficients of variation and Fano factors for common gene regulatory networks is better described by including terms of higher order than LNA in the system size expansion. This analysis is considerably faster than stochastic simulations due to the extensive ensemble averaging needed to obtain statistically meaningful results. Hence iNA is well suited for performing computationall

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