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  • Journal article
    Pardo O, Chrysostomou S, Roy R, Prischi F, Thamlikitkul L, Chapman KL, Mufti U, Peach R, Ding L, Hancock D, Moore C, Molina-Arcas M, Mauri F, Pinato DJ, Abrahams JM, Ottaviani S, Castellano L, Giamas G, Pascoe J, Moonamale D, Pirrie S, Gaunt C, Billingham L, Steven NM, Cullen M, Hrouda D, Winkler M, Post J, Cohen P, Salpeter SJ, Bar V, Zundelevich A, Golan S, Leibovici D, Lara R, Klug DR, Yaliraki SN, Barahona M, Wang Y, Downward J, Skehel JM, Ali MMU, Seckl MJet al., 2021,

    Repurposed floxacins targeting RSK4 prevent chemoresistance and metastasis in lung and bladder cancer

    , Science Translational Medicine, Vol: 13, ISSN: 1946-6234

    Lung and bladder cancers are mostly incurable because of the early development of drug resistance and metastatic dissemination. Hence, improved therapies that tackle these two processes are urgently needed to improve clinical outcome. We have identified RSK4 as a promoter of drug resistance and metastasis in lung and bladder cancer cells. Silencing this kinase, through either RNA interference or CRISPR, sensitized tumor cells to chemotherapy and hindered metastasis in vitro and in vivo in a tail vein injection model. Drug screening revealed several floxacin antibiotics as potent RSK4 activation inhibitors, and trovafloxacin reproduced all effects of RSK4 silencing in vitro and in/ex vivo using lung cancer xenograft and genetically engineered mouse models and bladder tumor explants. Through x-ray structure determination and Markov transient and Deuterium exchange analyses, we identified the allosteric binding site and revealed how this compound blocks RSK4 kinase activation through binding to an allosteric site and mimicking a kinase autoinhibitory mechanism involving the RSK4’s hydrophobic motif. Last, we show that patients undergoing chemotherapy and adhering to prophylactic levofloxacin in the large placebo-controlled randomized phase 3 SIGNIFICANT trial had significantly increased (P = 0.048) long-term overall survival times. Hence, we suggest that RSK4 inhibition may represent an effective therapeutic strategy for treating lung and bladder cancer.

  • Journal article
    Mersmann S, Stromich L, Song F, Wu N, Vianello F, Barahona M, Yaliraki Set al., 2021,

    ProteinLens: a web-based application for the analysis of allosteric signalling on atomistic graphs of biomolecules

    , Nucleic Acids Research, Vol: 49, Pages: W551-W558, ISSN: 0305-1048

    The investigation of allosteric effects in biomolecular structures is of great current interest in diverse areas, from fundamental biological enquiry to drug discovery. Here we present ProteinLens, a user-friendly and interactive web application for the investigation of allosteric signalling based on atomistic graph-theoretical methods. Starting from the PDB file of a biomolecule (or a biomolecular complex) ProteinLens obtains an atomistic, energy-weighted graph description of the structure of the biomolecule, and subsequently provides a systematic analysis of allosteric signalling and communication across the structure using two computationally efficient methods: Markov Transients and bond-to-bond propensities. ProteinLens scores and ranks every bond and residue according to the speed and magnitude of the propagation of fluctuations emanating from any site of choice (e.g. the active site). The results are presented through statistical quantile scores visualised with interactive plots and adjustable 3D structure viewers, which can also be downloaded. ProteinLens thus allows the investigation of signalling in biomolecular structures of interest to aid the detection of allosteric sites and pathways. ProteinLens is implemented in Python/SQL and freely available to use at:

  • Journal article
    Godoy-Lorite A, Jones N, 2021,

    Inference and influence of network structure using snapshot social behavior without network data

    , Science Advances, Vol: 7, ISSN: 2375-2548

    Population behavior, like voting and vaccination, depends on the structure of social networks. This structure can differ depending on behavior type and is typically hidden. However, we do often have behavioral data, albeit only snapshots taken at one time point. We present a method jointly inferring a model for both network structure and human behavior using only snapshot population-level behavioral data. This exploits the simplicity of a few parameter model, geometric sociodemographic network model, and a spin-based model of behavior. We illustrate, for the European Union referendum and two London mayoral elections, how the model offers both prediction and the interpretation of the homophilic inclinations of the population. Beyond extracting behavior-specific network structure from behavioral datasets, our approach yields a framework linking inequalities and social preferences to behavioral outcomes. We illustrate potential network-sensitive policies: How changes to income inequality, social temperature, and homophilic preferences might have reduced polarization in a recent election.

  • Journal article
    Thomas P, Shahrezaei V, 2021,

    Coordination of gene expression noise with cell size: analytical results for agent-based models of growing cell populations

    , Journal of the Royal Society Interface, Vol: 18, Pages: 1-16, ISSN: 1742-5662

    The chemical master equation and the Gillespie algorithm are widely used to model the reaction kinetics inside living cells. It is thereby assumed that cell growth and division can be modelled through effective dilution reactions and extrinsic noise sources. We here re-examine these paradigms through developing an analytical agent-based framework of growing and dividing cells accompanied by an exact simulation algorithm, which allows us to quantify the dynamics of virtually any intracellular reaction network affected by stochastic cell size control and division noise. We find that the solution of the chemical master equation—including static extrinsic noise—exactly agrees with the agent-based formulation when the network under study exhibits stochastic concentration homeostasis, a novel condition that generalizes concentration homeostasis in deterministic systems to higher order moments and distributions. We illustrate stochastic concentration homeostasis for a range of common gene expression networks. When this condition is not met, we demonstrate by extending the linear noise approximation to agent-based models that the dependence of gene expression noise on cell size can qualitatively deviate from the chemical master equation. Surprisingly, the total noise of the agent-based approach can still be well approximated by extrinsic noise models.

  • Journal article
    Myall AC, Peach RL, Weiße AY, Davies F, Mookerjee S, Holmes A, Barahona Met al., 2021,

    Network memory in the movement of hospital patients carrying drug-resistant bacteria

    , Applied Network Science, Vol: 6, ISSN: 2364-8228

    Hospitals constitute highly interconnected systems that bring into contact anabundance of infectious pathogens and susceptible individuals, thus makinginfection outbreaks both common and challenging. In recent years, there hasbeen a sharp incidence of antimicrobial-resistance amongsthealthcare-associated infections, a situation now considered endemic in manycountries. Here we present network-based analyses of a data set capturing themovement of patients harbouring drug-resistant bacteria across three largeLondon hospitals. We show that there are substantial memory effects in themovement of hospital patients colonised with drug-resistant bacteria. Suchmemory effects break first-order Markovian transitive assumptions andsubstantially alter the conclusions from the analysis, specifically on noderankings and the evolution of diffusive processes. We capture variable lengthmemory effects by constructing a lumped-state memory network, which we then useto identify overlapping communities of wards. We find that these communities ofwards display a quasi-hierarchical structure at different levels of granularitywhich is consistent with different aspects of patient flows related to hospitallocations and medical specialties.

  • Journal article
    Saavedra-Garcia P, Roman-Trufero M, Al-Sadah HA, Blighe K, Lopez-Jimenez E, Christoforou M, Penfold L, Capece D, Xiong X, Miao Y, Parzych K, Caputo V, Siskos AP, Encheva V, Liu Z, Thiel D, Kaiser MF, Piazza P, Chaidos A, Karadimitris A, Franzoso G, Snijder AP, Keun HC, Oyarzún DA, Barahona M, Auner Het al., 2021,

    Systems level profiling of chemotherapy-induced stress resolution in cancer cells reveals druggable trade-offs

    , Proceedings of the National Academy of Sciences of USA, Vol: 118, ISSN: 0027-8424

    Cancer cells can survive chemotherapy-induced stress, but how they recover from it is not known.Using a temporal multiomics approach, we delineate the global mechanisms of proteotoxic stressresolution in multiple myeloma cells recovering from proteasome inhibition. Our observations definelayered and protracted programmes for stress resolution that encompass extensive changes acrossthe transcriptome, proteome, and metabolome. Cellular recovery from proteasome inhibitioninvolved protracted and dynamic changes of glucose and lipid metabolism and suppression ofmitochondrial function. We demonstrate that recovering cells are more vulnerable to specific insultsthan acutely stressed cells and identify the general control nonderepressable 2 (GCN2)-driven cellularresponse to amino acid scarcity as a key recovery-associated vulnerability. Using a transcriptomeanalysis pipeline, we further show that GCN2 is also a stress-independent bona fide target intranscriptional signature-defined subsets of solid cancers that share molecular characteristics. Thus,identifying cellular trade-offs tied to the resolution of chemotherapy-induced stress in tumour cellsmay reveal new therapeutic targets and routes for cancer therapy optimisation.

  • Journal article
    Peach RL, Arnaudon A, Schmidt JA, Palasciano HA, Bernier NR, Jelfs KE, Yaliraki SN, Barahona Met al., 2021,

    HCGA: Highly comparative graph analysis for network phenotyping

    , Patterns, Vol: 2, Pages: 100227-100227, ISSN: 2666-3899

    <jats:title>A<jats:sc>bstract</jats:sc></jats:title><jats:p>Networks are widely used as mathematical models of complex systems across many scientific disciplines, not only in biology and medicine but also in the social sciences, physics, computing and engineering. Decades of work have produced a vast corpus of research characterising the topological, combinatorial, statistical and spectral properties of graphs. Each graph property can be thought of as a feature that captures important (and some times overlapping) characteristics of a network. In the analysis of real-world graphs, it is crucial to integrate systematically a large number of diverse graph features in order to characterise and classify networks, as well as to aid network-based scientific discovery. In this paper, we introduce HCGA, a framework for highly comparative analysis of graph data sets that computes several thousands of graph features from any given network. HCGA also offers a suite of statistical learning and data analysis tools for automated identification and selection of important and interpretable features underpinning the characterisation of graph data sets. We show that HCGA outperforms other methodologies on supervised classification tasks on benchmark data sets whilst retaining the interpretability of network features. We also illustrate how HCGA can be used for network-based discovery through two examples where data is naturally represented as graphs: the clustering of a data set of images of neuronal morphologies, and a regression problem to predict charge transfer in organic semiconductors based on their structure. HCGA is an open platform that can be expanded to include further graph properties and statistical learning tools to allow researchers to leverage the wide breadth of graph-theoretical research to quantitatively analyse and draw insights from network data.</jats:p>

  • Journal article
    Qian Y, Expert P, Panzarasa P, Barahona Met al., 2021,

    Geometric graphs from data to aid classification tasks with Graph Convolutional Networks

    , Patterns, Vol: 2, Pages: 100237-100237, ISSN: 2666-3899
  • Journal article
    Maes A, Barahona M, Clopath C, 2021,

    Learning compositional sequences with multiple time scales through a hierarchical network of spiking neurons

  • Journal article
    Kuntz Nussio J, Thomas P, Stan G, Barahona Met al., 2021,

    Approximations of countably-infinite linear programs over bounded measure spaces

    , SIAM Journal on Optimization, Vol: 31, Pages: 604-625, ISSN: 1052-6234

    We study a class of countably-infinite-dimensional linear programs (CILPs)whose feasible sets are bounded subsets of appropriately defined spaces ofmeasures. The optimal value, optimal points, and minimal points of these CILPscan be approximated by solving finite-dimensional linear programs. We show howto construct finite-dimensional programs that lead to approximations witheasy-to-evaluate error bounds, and we prove that the errors converge to zero asthe size of the finite-dimensional programs approaches that of the originalproblem. We discuss the use of our methods in the computation of the stationarydistributions, occupation measures, and exit distributions of Markov~chains.

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