Research & Publications
Most of the members of this group are from the Statistics Section and Biomaths research group of the Department of Mathematics. Below you can find a list of research areas that members of this group are currently working on and/or would like to work on by applying their developed mathematical and statistical methods.
Research areas
Research areas
- Systems Biology
- Statistical genomics and Epidemiology
- Medical Imaging
- Precision and Stratified Medicine
- Analysis of clinical trials, observational and longitudinal studies
- Infectious Disease Epidemiology
Publications
Results
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Journal articleAryaman J, Johnston IG, Jones NS, 2017,
Mitochondrial DNA Density Homeostasis Accounts for a Threshold Effect in a Cybrid Model of a Human Mitochondrial Disease
, Biochemical Journal, Vol: 474, Pages: 4019-4034, ISSN: 1470-8728Mitochondrial dysfunction is involved in a wide array of devastating diseases, but the heterogeneity and complexity of the symptoms of these diseases challenges theoretical understanding of their causation. With the explosion of omics data, we have the unprecedented opportunity to gain deep understanding of the biochemical mechanisms of mitochondrial dysfunction. This goal raises the outstanding need to make these complex datasets interpretable. Quantitative modelling allows us to translate such datasets into intuition and suggest rational biomedical treatments. Taking an interdisciplinary approach, we use a recently published large-scale dataset and develop a descriptive and predictive mathematical model of progressive increase in mutant load of the MELAS 3243A>G mtDNA mutation. The experimentally observed behaviour is surprisingly rich, but we find that our simple, biophysically motivated model intuitively accounts for this heterogeneity and yields a wealth of biological predictions. Our findings suggest that cells attempt to maintain wild-type mtDNA density through cell volume reduction, and thus power demand reduction, until a minimum cell volume is reached. Thereafter, cells toggle from demand reduction to supply increase, up-regulating energy production pathways. Our analysis provides further evidence for the physiological significance of mtDNA density and emphasizes the need for performing single-cell volume measurements jointly with mtDNA quantification. We propose novel experiments to verify the hypotheses made here to further develop our understanding of the threshold effect and connect with rational choices for mtDNA disease therapies.
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Journal articleFulcher B, Jones NS, 2017,
hctsa: A computational framework for automated timeseriesphenotyping using massive feature extraction
, Cell Systems, Vol: 5, Pages: 527-531.e3, ISSN: 2405-4712Phenotype measurements frequently take the form of time series, but we currently lack a systematic method for relating these complex data streams to scientifically meaningful outcomes, such as relating the movement dynamics of organisms to their genotype or measurements of brain dynamics of a patient to their disease diagnosis. Previous work addressed this problem by comparing implementations of thousands of diverse scientific time-series analysis methods in an approach termed highly comparative time-series analysis. Here, we introduce hctsa, a software tool for applying this methodological approach to data. hctsa includes an architecture for computing over 7,700 time-series features and a suite of analysis and visualization algorithms to automatically select useful and interpretable time-series features for a given application. Using exemplar applications to high-throughput phenotyping experiments, we show how hctsa allows researchers to leverage decades of time-series research to quantify and understand informative structure in time-series data.
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Journal articleCox DR, Battey HS, 2017,
Large numbers of explanatory variables, a semi-descriptive analysis
, Proceedings of the National Academy of Sciences of USA, Vol: 114, Pages: 8592-8595, ISSN: 0027-8424Data with a relatively small number of study individuals and a very large number of potential explanatory features arise particularly, but by no means only, in genomics. A powerful method of analysis, the lasso [Tibshirani R (1996) J Roy Stat Soc B 58:267–288], takes account of an assumed sparsity of effects, that is, that most of the features are nugatory. Standard criteria for model fitting, such as the method of least squares, are modified by imposing a penalty for each explanatory variable used. There results a single model, leaving open the possibility that other sparse choices of explanatory features fit virtually equally well. The method suggested in this paper aims to specify simple models that are essentially equally effective, leaving detailed interpretation to the specifics of the particular study. The method hinges on the ability to make initially a very large number of separate analyses, allowing each explanatory feature to be assessed in combination with many other such features. Further stages allow the assessment of more complex patterns such as nonlinear and interactive dependences. The method has formal similarities to so-called partially balanced incomplete block designs introduced 80 years ago [Yates F (1936) J Agric Sci 26:424–455] for the study of large-scale plant breeding trials. The emphasis in this paper is strongly on exploratory analysis; the more formal statistical properties obtained under idealized assumptions will be reported separately.
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Journal articleGriffie J, Shlomovich L, Williamson D, et al., 2017,
3D Bayesian cluster analysis of super-resolution data reveals LAT recruitment to the T cell synapse
, Scientific Reports, Vol: 7, ISSN: 2045-2322Single-molecule localisation microscopy (SMLM) allows the localisation of fluorophores with a precision of 10–30 nm, revealing the cell’s nanoscale architecture at the molecular level. Recently, SMLM has been extended to 3D, providing a unique insight into cellular machinery. Although cluster analysis techniques have been developed for 2D SMLM data sets, few have been applied to 3D. This lack of quantification tools can be explained by the relative novelty of imaging techniques such as interferometric photo-activated localisation microscopy (iPALM). Also, existing methods that could be extended to 3D SMLM are usually subject to user defined analysis parameters, which remains a major drawback. Here, we present a new open source cluster analysis method for 3D SMLM data, free of user definable parameters, relying on a model-based Bayesian approach which takes full account of the individual localisation precisions in all three dimensions. The accuracy and reliability of the method is validated using simulated data sets. This tool is then deployed on novel experimental data as a proof of concept, illustrating the recruitment of LAT to the T-cell immunological synapse in data acquired by iPALM providing ~10 nm isotropic resolution.Introduction.
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Journal articleAryaman J, hoitzing H, burgstaller J, et al., 2017,
Mitochondrial heterogeneity, metabolic scaling and cell death
, Bioessays, Vol: 39, ISSN: 1521-1878Heterogeneity in mitochondrial content has been previously suggested as a major contributor to cellular noise, with multiple studies indicating its direct involvement in biomedically important cellular phenomena. A recently published dataset explored the connection between mitochondrial functionality and cell physiology, where a non-linearity between mitochondrial functionality and cell size was found. Using mathematical models, we suggest that a combination of metabolic scaling and a simple model of cell death may account for these observations. However, our findings also suggest the existence of alternative competing hypotheses, such as a non-linearity between cell death and cell size. While we find that the proposed non-linear coupling between mitochondrial functionality and cell size provides a compelling alternative to previous attempts to link mitochondrial heterogeneity and cell physiology, we emphasise the need to account for alternative causal variables, including cell cycle, size, mitochondrial density and death, in future studies of mitochondrial physiology.
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Journal articleJohnson S, Jones NS, 2017,
Looplessness in networks is linked to trophic coherence
, Proceedings of the National Academy of Sciences of USA, Vol: 114, Pages: 5618-5623, ISSN: 0027-8424Many natural, complex systems are remarkably stable thanks to anabsence of feedback acting on their elements. When described as net-works, these exhibit few or no cycles, and associated matrices have smallleading eigenvalues. It has been suggested that this architecture can con-fer advantages to the system as a whole, such as ‘qualitative stability’,but this observation does not in itself explain how a loopless structuremight arise. We show here that the number of feedback loops in a net-work, as well as the eigenvalues of associated matrices, are determined bya structural property called trophic coherence, a measure of how neatlynodes fall into distinct levels. Our theory correctly classifies a variety ofnetworks – including those derived from genes, metabolites, species, neu-rons, words, computers and trading nations – into two distinct regimesof high and low feedback, and provides a null model to gauge the signifi-cance of related magnitudes. Since trophic coherence suppresses feedback,whereas an absence of feedback alone does not lead to coherence, our worksuggests that the reasons for ‘looplessness’ in nature should be sought incoherence-inducing mechanisms.
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Journal articleBattey HS, 2017,
Eigen structure of a new class of structured covariance and inverse covariance matrices
, Bernoulli, Vol: 23, Pages: 3166-3177There is a one to one mapping between a p dimensional strictly positive definite covariancematrix Σ and its matrix logarithm L. We exploit this relationship to study thestructure induced on Σ through a sparsity constraint on L. Consider L as a randommatrix generated through a basis expansion, with the support of the basis coefficientstaken as a simple random sample of size s = s∗from the index set [p(p + 1)/2] ={1, . . . , p(p + 1)/2}. We find that the expected number of non-unit eigenvalues of Σ, denotedE[|A|], is approximated with near perfect accuracy by the solution of the equation4p + p(p − 1)2(p + 1)hlog pp − d −d2p(p − d)i− s∗ = 0.Furthermore, the corresponding eigenvectors are shown to possess only p − |Ac| nonzeroentries. We use this result to elucidate the precise structure induced on Σ and Σ−1.We demonstrate that a positive definite symmetric matrix whose matrix logarithm issparse is significantly less sparse in the original domain. This finding has importantimplications in high dimensional statistics where it is important to exploit structure inorder to construct consistent estimators in non-trivial norms. An estimator exploitingthe structure of the proposed class is presented.
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Journal articleWills QF, Mellado-Gomez E, Nolan R, et al., 2017,
The nature and nurture of cell heterogeneity: accounting for macrophage gene-environment interactions with single-cell RNA-Seq.
, BMC Genomics, Vol: 18, ISSN: 1471-2164BACKGROUND: Single-cell RNA-Seq can be a valuable and unbiased tool to dissect cellular heterogeneity, despite the transcriptome's limitations in describing higher functional phenotypes and protein events. Perhaps the most important shortfall with transcriptomic 'snapshots' of cell populations is that they risk being descriptive, only cataloging heterogeneity at one point in time, and without microenvironmental context. Studying the genetic ('nature') and environmental ('nurture') modifiers of heterogeneity, and how cell population dynamics unfold over time in response to these modifiers is key when studying highly plastic cells such as macrophages. RESULTS: We introduce the programmable Polaris™ microfluidic lab-on-chip for single-cell sequencing, which performs live-cell imaging while controlling for the culture microenvironment of each cell. Using gene-edited macrophages we demonstrate how previously unappreciated knockout effects of SAMHD1, such as an altered oxidative stress response, have a large paracrine signaling component. Furthermore, we demonstrate single-cell pathway enrichments for cell cycle arrest and APOBEC3G degradation, both associated with the oxidative stress response and altered proteostasis. Interestingly, SAMHD1 and APOBEC3G are both HIV-1 inhibitors ('restriction factors'), with no known co-regulation. CONCLUSION: As single-cell methods continue to mature, so will the ability to move beyond simple 'snapshots' of cell populations towards studying the determinants of population dynamics. By combining single-cell culture, live-cell imaging, and single-cell sequencing, we have demonstrated the ability to study cell phenotypes and microenvironmental influences. It's these microenvironmental components - ignored by standard single-cell workflows - that likely determine how macrophages, for example, react to inflammation and form treatment resistant HIV reservoirs.
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Journal articleTodd J, Evangelou M, Cutler AJ, et al., 2016,
Regulatory T Cell Responses in Participants with Type 1 Diabetes after a Single Dose of Interleukin-2: A Non-Randomised, Open Label, Adaptive Dose-Finding Trial
, PLOS Medicine, Vol: 13, ISSN: 1549-1277BackgroundInterleukin-2 (IL-2) has an essential role in the expansion and function of CD4+ regulatory Tcells (Tregs). Tregs reduce tissue damage by limiting the immune response following infectionand regulate autoreactive CD4+ effector T cells (Teffs) to prevent autoimmune diseases,such as type 1 diabetes (T1D). Genetic susceptibility to T1D causes alterations inthe IL-2 pathway, a finding that supports Tregs as a cellular therapeutic target. Aldesleukin(Proleukin; recombinant human IL-2), which is administered at high doses to activate the immune system in cancer immunotherapy, is now being repositioned to treat inflammatoryand autoimmune disorders at lower doses by targeting Tregs.Methods and FindingsTo define the aldesleukin dose response for Tregs and to find doses that increase Tregsphysiologically for treatment of T1D, a statistical and systematic approach was taken byanalysing the pharmacokinetics and pharmacodynamics of single doses of subcutaneousaldesleukin in the Adaptive Study of IL-2 Dose on Regulatory T Cells in Type 1 Diabetes(DILT1D), a single centre, non-randomised, open label, adaptive dose-finding trial with 40adult participants with recently diagnosed T1D. The primary endpoint was the maximumpercentage increase in Tregs (defined as CD3+CD4+CD25highCD127low) from the baselinefrequency in each participant measured over the 7 d following treatment. There was an initiallearning phase with five pairs of participants, each pair receiving one of five preassignedsingle doses from 0.04 × 106 to 1.5 × 106 IU/m2, in order to model the doseresponsecurve. Results from each participant were then incorporated into interim statisticalmodelling to target the two doses most likely to induce 10% and 20% increases in Treg frequencies.Primary analysis of the evaluable population (n = 39) found that the optimaldoses of aldesleukin to induce 10% and 20% increases in Tregs were 0.101 × 106 IU/m2(standard error [SE] = 0.078, 95% CI = −0.052, 0.254
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Conference paperLarsen E, Truong T, Evangelou M, 2016,
Exploring GenexEnvironment interactions through pathway analysis
, Annual Meeting of the International-Genetic-Epidemiology-Society, Publisher: Wiley, Pages: 648-649, ISSN: 1098-2272
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