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  • Conference paper
    Chrysostomou S, Roy R, Prischi F, Chapman K, Mufti U, Mauri F, Bellezza G, Abrahams J, Ottaviani S, Castellano L, Giamas G, Hrouda D, Winkler M, Klug D, Yaliraki S, Barahona M, Wang Y, Ali M, Seckl M, Pardo Oet al., 2019,

    Targeting RSK4 prevents both chemoresistance and metastasis in lung cancer

    , AACR Annual Meeting on Bioinformatics, Convergence Science, and Systems Biology, Publisher: AMER ASSOC CANCER RESEARCH, ISSN: 0008-5472
  • Conference paper
    Prischi F, Chrysostomou S, Roy R, Chapman K, Mufti U, Peach R, Ding L, Mauri F, Bellezza G, Cagini L, Barbareschi M, Ferrero S, Abrahams J, Ottaviani S, Castellano L, Giamas G, Pascoe J, Moonamale D, Billingham L, Cullen M, Hrouda D, Winkler M, Klug D, Yaliraki S, Barahona M, Wang Y, Ali M, Seckl M, Pardo Oet al., 2019,

    Targeting RSK4 prevents both chemoresistance and metastasis in lung and bladder cancer

    , FEBS Open Bio, Publisher: WILEY, Pages: 330-330, ISSN: 2211-5463
  • Journal article
    Burgstaller J, Kolbe T, Havlicek V, Hembach S, Poulton J, Piálek J, Steinborn R, Rulicke T, Brem G, Jones NS, Johnston Iet al., 2019,

    Large-scale genetic analysis reveals mammalian mtDNA heteroplasmy dynamics and variance increase through lifetimes and generations

    , Nature Communications, Vol: 9, ISSN: 2041-1723

    Vital mitochondrial DNA (mtDNA) populations exist in cells and may consist of heteroplasmic mixtures of mtDNA types. The evolution of these heteroplasmic populations through development, ageing, and generations is central to genetic diseases, but is poorly understood in mammals. Here we dissect these population dynamics using a dataset of unprecedented size and temporal span, comprising 1947 single-cell oocyte and 899 somatic measurements of heteroplasmy change throughout lifetimes and generations in two genetically distinct mouse models. We provide a novel and detailed quantitative characterisation of the linear increase in heteroplasmy variance throughout mammalian life courses in oocytes and pups. We find that differences in mean heteroplasmy are induced between generations, and the heteroplasmy of germline and somatic precursors diverge early in development, with a haplotype-specific direction of segregation. We develop stochastic theory predicting the implications of these dynamics for ageing and disease manifestation and discuss its application to human mtDNA dynamics.

  • Journal article
    Hoitzing H, Gammage PA, Haute LV, Minczuk M, Johnston IG, Jones NSet al., 2019,

    Energetic costs of cellular and therapeutic control of stochastic mitochondrial DNA populations

    , PLoS Computational Biology, Vol: 15, ISSN: 1553-734X

    The dynamics of the cellular proportion of mutant mtDNA molecules is crucial for mitochondrial diseases. Cellular populations of mitochondria are under homeostatic control, but the details of the control mechanisms involved remain elusive. Here, we use stochastic modelling to derive general results for the impact of cellular control on mtDNA populations, the cost to the cell of different mtDNA states, and the optimisation of therapeutic control of mtDNA populations. This formalism yields a wealth of biological results, including that an increasing mtDNA variance can increase the energetic cost of maintaining a tissue, that intermediate levels of heteroplasmy can be more detrimental than homoplasmy even for a dysfunctional mutant, that heteroplasmy distribution (not mean alone) is crucial for the success of gene therapies, and that long-term rather than short intense gene therapies are more likely to beneficially impact mtDNA populations.

  • Journal article
    Schaub MT, Delvenne JC, Lambiotte R, Barahona Met al., 2019,

    Multiscale dynamical embeddings of complex networks

    , Physical Review E, Vol: 99, Pages: 062308-1-062308-18, ISSN: 1539-3755

    Complex systems and relational data are often abstracted as dynamical processes on networks. To understand, predict, and control their behavior, a crucial step is to extract reduced descriptions of such networks. Inspired by notions from control theory, we propose a time-dependent dynamical similarity measure between nodes, which quantifies the effect a node-input has on the network. This dynamical similarity induces an embedding that can be employed for several analysis tasks. Here we focus on (i) dimensionality reduction, i.e., projecting nodes onto a low-dimensional space that captures dynamic similarity at different timescales, and (ii) how to exploit our embeddings to uncover functional modules. We exemplify our ideas through case studies focusing on directed networks without strong connectivity and signed networks. We further highlight how certain ideas from community detection can be generalized and linked to control theory, by using the here developed dynamical perspective.

  • Journal article
    Warren L, Clarke J, Arora S, Barahona M, Arebi N, Darzi Aet al., 2019,

    Transitions of care across hospital settings in patients with inflammatory bowel disease

    , World Journal of Gastroenterology, Vol: 25, Pages: 2122-2132, ISSN: 1007-9327

    BACKGROUNDInflammatory bowel disease (IBD) is a chronic, inflammatory disorder characterised by both intestinal and extra-intestinal pathology. Patients may receive both emergency and elective care from several providers, often in different hospital settings. Poorly managed transitions of care between providers can lead to inefficiencies in care and patient safety issues. To ensure that the sharing of patient information between providers is appropriate, timely, accurate and secure, effective data-sharing infrastructure needs to be developed. To optimise inter-hospital data-sharing for IBD patients, we need to better understand patterns of hospital encounters in this group.AIMTo determine the type and location of hospital services accessed by IBD patients in England.METHODSThis was a retrospective observational study using Hospital Episode Statistics, a large administrative patient data set from the National Health Service in England. Adult patients with a diagnosis of IBD following admission to hospital were followed over a 2-year period to determine the proportion of care accessed at the same hospital providing their outpatient IBD care, defined as their ‘home provider’. Secondary outcome measures included the geographic distribution of patient-sharing, regional and age-related differences in accessing services, and type and frequency of outpatient encounters.RESULTSOf 95055 patients accessed hospital services on 1760156 occasions over a 2-year follow-up period. The proportion of these encounters with their identified IBD ‘home provider’ was 73.3%, 87.8% and 83.1% for accident and emergency, inpatient and outpatient encounters respectively. Patients living in metropolitan centres and younger patients were less likely to attend their ‘home provider’ for hospital services. The most commonly attended specialty services were gastroenterology, general surgery and ophthalmology.CONCLUSIONTransitions of care between secondary care sett

  • Journal article
    Lubba CH, Le Guen Y, Jarvis S, Jones NS, Cork SC, Eftekhar A, Schultz SRet al., 2019,

    Correction to: PyPNS: Multiscale Simulation of a Peripheral Nerve in Python.

    , Neuroinformatics

    The original version of this article unfortunately contained a mistake. The following text: "This project has received funding from European Research Council (ERC) Synergy Grant no. 319818." is missing in the Acknowledgments.

  • Conference paper
    Insalata F, Hoitzing H, Jones N, 2019,

    A mathematical model of expansion of disadvantaged but altruistic mitochondrial mutants in skeletal muscle fibres

    , Publisher: WILEY, Pages: 61-61, ISSN: 0014-2972
  • Journal article
    Kuntz J, Thomas P, Stan G-B, Barahona Met al., 2019,

    The exit time finite state projection scheme: bounding exit distributions and occupation measures of continuous-time Markov chains

    , SIAM Journal on Scientific Computing, Vol: 41, Pages: A748-A769, ISSN: 1064-8275

    We introduce the exit time finite state projection (ETFSP) scheme, a truncation- based method that yields approximations to the exit distribution and occupation measure associated with the time of exit from a domain (i.e., the time of first passage to the complement of the domain) of time-homogeneous continuous-time Markov chains. We prove that: (i) the computed approximations bound the measures from below; (ii) the total variation distances between the approximations and the measures decrease monotonically as states are added to the truncation; and (iii) the scheme converges, in the sense that, as the truncation tends to the entire state space, the total variation distances tend to zero. Furthermore, we give a computable bound on the total variation distance between the exit distribution and its approximation, and we delineate the cases in which the bound is sharp. We also revisit the related finite state projection scheme and give a comprehensive account of its theoretical properties. We demonstrate the use of the ETFSP scheme by applying it to two biological examples: the computation of the first passage time associated with the expression of a gene, and the fixation times of competing species subject to demographic noise.

  • Journal article
    Tonn M, Thomas P, Barahona M, Oyarzun D, Tonn M, Thomas P, Barahona M, Oyarzun Det al., 2019,

    Stochastic modelling reveals mechanisms of metabolic heterogeneity

    , Communications Biology, Vol: 2, ISSN: 2399-3642

    Phenotypic variation is a hallmark of cellular physiology. Metabolic heterogeneity, in particular, underpins single-cell phenomena such as microbial drug tolerance and growth variability. Much research has focussed on transcriptomic and proteomic heterogeneity, yet it remains unclear if such variation permeates to the metabolic state of a cell. Here we propose a stochastic model to show that complex forms of metabolic heterogeneity emerge from fluctuations in enzyme expression and catalysis. The analysis predicts clonal populations to split into two or more metabolically distinct subpopulations. We reveal mechanisms not seen in deterministic models, in which enzymes with unimodal expression distributions lead to metabolites with a bimodal or multimodal distribution across the population. Based on published data, the results suggest that metabolite heterogeneity may be more pervasive than previously thought. Our work casts light on links between gene expression and metabolism, and provides a theory to probe the sources of metabolite heterogeneity.

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