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  • 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 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.

  • Journal article
    Aryaman J, Johnston I, Jones N, 2019,

    Mitochondrial heterogeneity

    , Frontiers in Genetics, Vol: 9, ISSN: 1664-8021

    Cell-to-cell heterogeneity drives a range of (patho)physiologically important phenomena, such as cell fate and chemotherapeutic resistance. The role of metabolism, and particularly of mitochondria, is increasingly being recognized as an important explanatory factor in cell-to-cell heterogeneity. Most eukaryotic cells possess a population of mitochondria, in the sense that mitochondrial DNA (mtDNA) is held in multiple copies per cell, where the sequence of each molecule can vary. Hence, intra-cellular mitochondrial heterogeneity is possible, which can induce inter-cellular mitochondrial heterogeneity, and may drive aspects of cellular noise. In this review, we discuss sources of mitochondrial heterogeneity (variations between mitochondria in the same cell, and mitochondrial variations between supposedly identical cells) from both genetic and non-genetic perspectives, and mitochondrial genotype-phenotype links. We discuss the apparent homeostasis of mtDNA copy number, the observation of pervasive intra-cellular mtDNA mutation (which is termed “microheteroplasmy”), and developments in the understanding of inter-cellular mtDNA mutation (“macroheteroplasmy”). We point to the relationship between mitochondrial supercomplexes, cristal structure, pH, and cardiolipin as a potential amplifier of the mitochondrial genotype-phenotype link. We also discuss mitochondrial membrane potential and networks as sources of mitochondrial heterogeneity, and their influence upon the mitochondrial genome. Finally, we revisit the idea of mitochondrial complementation as a means of dampening mitochondrial genotype-phenotype links in light of recent experimental developments. The diverse sources of mitochondrial heterogeneity, as well as their increasingly recognized role in contributing to cellular heterogeneity, highlights the need for future single-cell mitochondrial measurements in the context of cellular noise studies.

  • Journal article
    Thomas P, 2019,

    Intrinsic and extrinsic noise of gene expression in lineage trees

    , Scientific Reports, Vol: 9, ISSN: 2045-2322

    Cell-to-cell heterogeneity is driven by stochasticity in intracellular reactions and the population dynamics. While these sources are usually studied separately, we develop an agent-based framework that accounts for both factors while tracking every single cell of a growing population. Apart from the common intrinsic variability, the framework also predicts extrinsic noise without the need to introduce fluctuating rate constants. Instead, extrinsic fluctuations are explained by cell cycle fluctuations and differences in cell age. We provide explicit formulas to quantify mean molecule numbers, intrinsic and extrinsic noise statistics in two-colour experiments. We find that these statistics differ significantly depending on the experimental setup used to observe the cells. We illustrate this fact using (i) averages over an isolated cell lineage tracked over many generations as observed in the mother machine, (ii) population snapshots with known cell ages as recorded in time-lapse microscopy, and (iii) snapshots with unknown cell ages as measured from static images or flow cytometry. Applying the method to models of stochastic gene expression and feedback regulation elucidates that isolated lineages, as compared to snapshot data, can significantly overestimate the mean number of molecules, overestimate extrinsic noise but underestimate intrinsic noise and have qualitatively different sensitivities to cell cycle fluctuations.

  • Journal article
    Altuncu MT, Mayer E, Yaliraki SN, Barahona Met al., 2019,

    From free text to clusters of content in health records: An unsupervised graph partitioning approach

    , Applied Network Science, Vol: 4, ISSN: 2364-8228

    Electronic Healthcare records contain large volumes of unstructured data in different forms. Free text constitutes a large portion of such data, yet this source of richly detailed information often remains under-used in practice because of a lack of suitable methodologies to extract interpretable contentin a timely manner. Here we apply network-theoretical tools to the analysis of free text in Hospital Patient Incident reports in the English National Health Service, to find clusters of reports in an unsupervised manner and at different levels of resolution based directly on the free text descriptions contained within them. To do so, we combine recently developed deep neural network text-embedding methodologies based on paragraph vectors with multi-scale Markov Stability community detection applied to a similarity graph of documents obtained from sparsified text vector similarities. We showcase the approach with the analysis of incident reports submitted in Imperial College Healthcare NHS Trust, London. The multiscale community structure reveals levels of meaning with different resolution in the topics of the dataset, as shown by relevant descriptive terms extracted from thegroups of records, as well as by comparing a posteriori against hand-coded categories assigned by healthcare personnel. Our content communities exhibit good correspondence with well-defined hand-coded categories, yet our results also provide further medical detail in certain areas as well asrevealing complementary descriptors of incidents beyond the external classification. We also discuss how the method can be used to monitor reports over time and across different healthcare providers, and to detect emerging trends that fall outside of pre-existing categories.

  • Journal article
    Gosztolai A, Carrillo de la Plata JA, Barahona M, 2019,

    Optimal navigation (ON) model - numerical implementation

    , Frontiers in Physics, Vol: 6, ISSN: 2296-424X

    Implementation of the optimal navigation (ON) model accompanying the paper 'Collective search with finite perception: transient dynamics and search efficiency' by Gosztolai et al., Frontiers in Physics (2019) 10:153runON.m runs the ON model in Matlab in various configurations and can be used to reproduce the results in the paper. The code has been written and tested in Matlab_R2017b.ON1D.m and ON2D.m are functions containing the 1D and 2D implementation of the ON model, which are called by runON.mThe .avi files contain simulations of the evolution in 2D for time horizons 5, 10, 100, 1000, as described in the paper.

  • Journal article
    Lubba CT, Le Guen Y, Jarvis S, Jones N, Cork S, Eftekhar A, Schultz Set al., 2019,

    PyPNS: multiscale simulation of a peripheral nerve in Python

    , Neuroinformatics, Vol: 17, Pages: 63-81, ISSN: 1539-2791

    Bioelectronic Medicines that modulate the activity patterns on peripheral nerves have promise as a new way of treating diverse medical conditions from epilepsy to rheumatism. Progress in the field builds upon time consuming and expensive experiments in living organisms. To reduce experimentation load and allow for a faster, more detailed analysis of peripheral nerve stimulation and recording, computational models incorporating experimental insights will be of great help.We present a peripheral nerve simulator that combines biophysical axon models and numerically solved and idealised extracellular space models in one environment. We modeled the extracellular space as a three-dimensional resistive continuum governed by the electro-quasistatic approximation of the Maxwell equations. Potential distributions were precomputed in finite element models for different media (homogeneous, nerve in saline, nerve in cuff) and imported into our simulator. Axons, on the other hand, were modeled more abstractly as one-dimensional chains of compartments. Unmyelinated fibres were based on the Hodgkin- Huxley model; for myelinated fibres, we adapted the model proposed by McIntyre et al. in 2002 to smaller diameters. To obtain realistic axon shapes, an iterative algorithm positioned fibres along the nerve with a variable tortuosity fit to imaged trajectories. We validated our model with data from the stimulated rat vagus nerve. Simulation results predicted that tortuosity alters recorded signal shapes and increases stimulation thresholds. The model we developed can easily be adapted to different nerves, and may be of use for Bioelectronic Medicine research in the future.

  • Conference paper
    Lubba CH, Fulcher BD, Schultz SR, Jones NSet al., 2019,

    Efficient peripheral nerve firing characterisation through massive feature extraction

    , 9th IEEE/EMBS International Conference on Neural Engineering (NER), Publisher: IEEE, Pages: 179-182, ISSN: 1948-3546

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