Imperial College London

DrThomasThorne

Faculty of MedicineDepartment of Brain Sciences

Honorary Research Fellow
 
 
 
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t.thorne

 
 
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Burlington DanesHammersmith Campus

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Summary

 

Publications

Publication Type
Year
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27 results found

Babtie AC, Stumpf MPH, Thorne T, 2021, Gene Regulatory Network Inference, Systems Medicine, Publisher: Elsevier, Pages: 86-95

Book chapter

Liang H, Ganeshbabu U, Thorne T, 2020, A Dynamic Bayesian Network Approach for Analysing Topic-Sentiment Evolution, IEEE ACCESS, Vol: 8, Pages: 54164-54174, ISSN: 2169-3536

Journal article

Gafson AR, Savva C, Thorne T, David MJ, Gomez-Romero B, Lewis M, Nicholas R, Heslegrave A, Zetterberg H, Matthews Pet al., 2019, Breaking the cycle: reversal of flux in the tricarboxylic acid cycle by dimethyl fumarate, Neurology, Neuroimmunology and Neuroinflammation, Vol: 6, ISSN: 2332-7812

ObjectiveTo infer possible molecular effectors of therapeutic effects and adverse events for the pro-drug dimethyl fumarate (DMF, Tecfidera) in the plasma of relapsing-remitting MS patients (RRMS) based on untargeted blood plasma metabolomics. MethodsBlood samples were collected from 27 RRMS patients at baseline and six weeks after initiation of treatment with DMF (BG-12; Tecfidera). Patients were separated into a discovery (n=15) and a validation cohort (n=12). Ten healthy controls were also recruited and blood samples were collected over the same time intervals. Untargeted metabolomic profiling using ultrahigh performance liquid chromatography-tandem mass spectrometry (UPLC-MS) was performed on plasma samples from the discovery cohort and healthy controls at Metabolon Inc. (Durham, NC). UPLC-MS was then performed on samples from the validation cohort at the National Phenome Centre (Imperial College, UK). Plasma neurofilament concentration (NfL) was also assayed for all subjects using the Simoa platform (Quanterix, Lexington, MA). Time course and cross-sectional statistical analyses were performed to identify pharmacodynamic changes in the metabolome secondary to DMF and relate these to adverse events. Results In the discovery cohort, tricarboxylic acid (TCA) cycle intermediates fumarate and succinate and TCA cycle metabolites succinyl-carnitine and methyl succinyl-carnitine were increased 6-weeks after the start of treatment (q < 0.05). We confirmed that methyl succinyl carnitine was also increased in the validation cohort 6-weeks after the start of treatment (q < 0.05). Changes in concentrations of these metabolites were not seen over a similar time period in blood from the untreated healthy control population. Increased succinyl-carnitine and methyl succinyl-carnitine were associated with adverse events from DMF (flushing, abdominal symptoms. The mean plasma NfL concentration before treatment was higher in the RRMS patients than in the healthy contro

Journal article

Gafson AR, Thorne T, McKechnie CIJ, Jimenez B, Nicholas R, Matthews PMet al., 2018, Lipoprotein markers associated with disability from multiple sclerosis, Scientific Reports, Vol: 8, ISSN: 2045-2322

Altered lipid metabolism is a feature of chronic infammatory disorders. Increased plasma lipids andlipoproteins have been associated with multiple sclerosis (MS) disease activity. Our objective was tocharacterise the specifc lipids and associated plasma lipoproteins increased in MS and to test for anassociation with disability. Plasma samples were collected from 27 RRMS patients (median EDSS,1.5, range 1–7) and 31 healthy controls. Concentrations of lipids within lipoprotein sub-classes weredetermined from NMR spectra. Plasma cytokines were measured using the MesoScale DiscoveryV-PLEX kit. Associations were tested using multivariate linear regression. Diferences between thepatient and volunteer groups were found for lipids within VLDL and HDL lipoprotein sub-fractions(p<0.05). Multivariate regression demonstrated a high correlation between lipids within VLDLsub-classes and the Expanded Disability Status Scale (EDSS) (p<0.05). An optimal model for EDSSincluded free cholesterol carried by VLDL-2, gender and age (R2=0.38, p<0.05). Free cholesterolcarried by VLDL-2 was highly correlated with plasma cytokines CCL-17 and IL-7 (R2=0.78, p<0.0001).These results highlight relationships between disability, infammatory responses and systemic lipidmetabolism in RRMS. Altered lipid metabolism with systemic infammation may contribute to immuneactivation.

Journal article

Thorne T, 2018, Approximate inference of gene regulatory network models from RNA-Seq time series data, BMC BIOINFORMATICS, Vol: 19, ISSN: 1471-2105

BackgroundInference of gene regulatory network structures from RNA-Seq data is challenging due to the nature of the data, as measurements take the form of counts of reads mapped to a given gene. Here we present a model for RNA-Seq time series data that applies a negative binomial distribution for the observations, and uses sparse regression with a horseshoe prior to learn a dynamic Bayesian network of interactions between genes. We use a variational inference scheme to learn approximate posterior distributions for the model parameters.ResultsThe methodology is benchmarked on synthetic data designed to replicate the distribution of real world RNA-Seq data. We compare our method to other sparse regression approaches and find improved performance in learning directed networks. We demonstrate an application of our method to a publicly available human neuronal stem cell differentiation RNA-Seq time series data set to infer the underlying network structure.ConclusionsOur method is able to improve performance on synthetic data by explicitly modelling the statistical distribution of the data when learning networks from RNA-Seq time series. Applying approximate inference techniques we can learn network structures quickly with only moderate computing resources.

Journal article

Thorne TW, 2016, NetDiff – Bayesian model selection for differential gene regulatory network inference, Scientific Reports, Vol: 6, ISSN: 2045-2322

Differential networks allow us to better understand the changes in cellular processes that are exhibited in conditions of interest, identifying variations in gene regulation or protein interaction between, for example, cases and controls, or in response to external stimuli. Here we present a novel methodology for the inference of differential gene regulatory networks from gene expression microarray data. Specifically we apply a Bayesian model selection approach to compare models of conserved and varying network structure, and use Gaussian graphical models to represent the network structures. We apply a variational inference approach to the learning of Gaussian graphical models of gene regulatory networks, that enables us to perform Bayesian model selection that is significantly more computationally efficient than Markov Chain Monte Carlo approaches. Our method is demonstrated to be more robust than independent analysis of data from multiple conditions when applied to synthetic network data, generating fewer false positive predictions of differential edges. We demonstrate the utility of our approach on real world gene expression microarray data by applying it to existing data from amyotrophic lateral sclerosis cases with and without mutations in C9orf72, and controls, where we are able to identify differential network interactions for further investigation.

Journal article

Thorne T, 2015, Empirical likelihood tests for nonparametric detection of differential expression from RNA-seq data, STATISTICAL APPLICATIONS IN GENETICS AND MOLECULAR BIOLOGY, Vol: 14, Pages: 575-583, ISSN: 2194-6302

Journal article

Zurauskiene J, Kirk P, Thorne T, Stumpf MPHet al., 2014, Bayesian non-parametric approaches to reconstructing oscillatory systems and the Nyquist limit, PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, Vol: 407, Pages: 33-42, ISSN: 0378-4371

Journal article

Zurauskiene J, Kirk P, Thorne T, Pinney J, Stumpf Met al., 2014, Derivative processes for modelling metabolic fluxes, Bioinformatics, Vol: 30, Pages: 1892-1898, ISSN: 1367-4803

Motivation: One of the challenging questions in modelling biological systems is to characterize the functional forms of the processes that control and orchestrate molecular and cellular phenotypes. Recently proposed methods for the analysis of metabolic pathways, for example, dynamic flux estimation, can only provide estimates of the underlying fluxes at discrete time points but fail to capture the complete temporal behaviour. To describe the dynamic variation of the fluxes, we additionally require the assumption of specific functional forms that can capture the temporal behaviour. However, it also remains unclear how to address the noise which might be present in experimentally measured metabolite concentrations.Results: Here we propose a novel approach to modelling metabolic fluxes: derivative processes that are based on multiple-output Gaussian processes (MGPs), which are a flexible non-parametric Bayesian modelling technique. The main advantages that follow from MGPs approach include the natural non-parametric representation of the fluxes and ability to impute the missing data in between the measurements. Our derivative process approach allows us to model changes in metabolite derivative concentrations and to characterize the temporal behaviour of metabolic fluxes from time course data. Because the derivative of a Gaussian process is itself a Gaussian process, we can readily link metabolite concentrations to metabolic fluxes and vice versa. Here we discuss how this can be implemented in an MGP framework and illustrate its application to simple models, including nitrogen metabolism in Escherichia coli.

Journal article

Kirk P, Thorne T, Stumpf MPH, 2013, Model selection in systems and synthetic biology, CURRENT OPINION IN BIOTECHNOLOGY, Vol: 24, Pages: 767-774, ISSN: 0958-1669

Journal article

Thorne T, Fratta P, Hanna MG, Cortese A, Plagnol V, Fisher EM, Stumpf MPHet al., 2013, Graphical modelling of molecular networks underlying sporadic inclusion body myositis, MOLECULAR BIOSYSTEMS, Vol: 9, Pages: 1736-1742, ISSN: 1742-206X

Journal article

Thorne T, Stumpf MPH, 2012, Inference of temporally varying Bayesian Networks, BIOINFORMATICS, Vol: 28, Pages: 3298-3305, ISSN: 1367-4803

Journal article

Barnes CP, Filippi S, Stumpf MPH, Thorne Tet al., 2012, Considerate approaches to constructing summary statistics for ABC model selection, STATISTICS AND COMPUTING, Vol: 22, Pages: 1181-1197, ISSN: 0960-3174

Journal article

Thorne T, Stumpf MPH, 2012, Graph spectral analysis of protein interaction network evolution, JOURNAL OF THE ROYAL SOCIETY INTERFACE, Vol: 9, Pages: 2653-2666, ISSN: 1742-5689

Journal article

Kaloriti D, Tillmann A, Cook E, Jacobsen M, You T, Lenardon M, Ames L, Barahona M, Chandrasekaran K, Coghill G, Goodman D, Gow NAR, Grebogi C, Ho H-L, Ingram P, McDonagh A, de Moura APS, Pang W, Puttnam M, Radmaneshfar E, Romano MC, Silk D, Stark J, Stumpf M, Thiel M, Thorne T, Usher J, Yin Z, Haynes K, Brown AJPet al., 2012, Combinatorial stresses kill pathogenic Candida species, MEDICAL MYCOLOGY, Vol: 50, Pages: 699-709, ISSN: 1369-3786

Journal article

Harrington HA, Ho KL, Thorne T, Stumpf MPHet al., 2012, Parameter-free model discrimination criterion based on steady-state coplanarity, PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, Vol: 109, Pages: 15746-15751, ISSN: 0027-8424

Journal article

You T, Ingram P, Jacobsen MD, Cook E, McDonagh A, Thorne T, Lenardon MD, de Moura APS, Romano MC, Thiel M, Stumpf M, Gow NAR, Haynes K, Grebogi C, Stark J, Brown AJPet al., 2012, A systems biology analysis of long and short-term memories of osmotic stress adaptation in fungi., BMC Res Notes, Vol: 5

BACKGROUND: Saccharomyces cerevisiae senses hyperosmotic conditions via the HOG signaling network that activates the stress-activated protein kinase, Hog1, and modulates metabolic fluxes and gene expression to generate appropriate adaptive responses. The integral control mechanism by which Hog1 modulates glycerol production remains uncharacterized. An additional Hog1-independent mechanism retains intracellular glycerol for adaptation. Candida albicans also adapts to hyperosmolarity via a HOG signaling network. However, it remains unknown whether Hog1 exerts integral or proportional control over glycerol production in C. albicans. RESULTS: We combined modeling and experimental approaches to study osmotic stress responses in S. cerevisiae and C. albicans. We propose a simple ordinary differential equation (ODE) model that highlights the integral control that Hog1 exerts over glycerol biosynthesis in these species. If integral control arises from a separation of time scales (i.e. rapid HOG activation of glycerol production capacity which decays slowly under hyperosmotic conditions), then the model predicts that glycerol production rates elevate upon adaptation to a first stress and this makes the cell adapts faster to a second hyperosmotic stress. It appears as if the cell is able to remember the stress history that is longer than the timescale of signal transduction. This is termed the long-term stress memory. Our experimental data verify this. Like S. cerevisiae, C. albicans mimimizes glycerol efflux during adaptation to hyperosmolarity. Also, transient activation of intermediate kinases in the HOG pathway results in a short-term memory in the signaling pathway. This determines the amplitude of Hog1 phosphorylation under a periodic sequence of stress and non-stressed intervals. Our model suggests that the long-term memory also affects the way a cell responds to periodic stress conditions. Hence, during osmohomeostasis, short-term memory is dependent upon long-term me

Journal article

Liepe J, Taylor H, Barnes CP, Huvet M, Bugeon L, Thorne T, Lamb JR, Dallman MJ, Stumpf MPHet al., 2012, Calibrating spatio-temporal models of leukocyte dynamics against in vivo live-imaging data using approximate Bayesian computation, INTEGRATIVE BIOLOGY, Vol: 4, Pages: 335-345, ISSN: 1757-9694

Journal article

Thorne TW, Ho H-L, Huvet M, Haynes K, Stumpf MPHet al., 2011, Prediction of putative protein interactions through evolutionary analysis of osmotic stress response in the model yeast Saccharomyces cerevisae, FUNGAL GENETICS AND BIOLOGY, Vol: 48, Pages: 504-511, ISSN: 1087-1845

Journal article

Huvet M, Toni T, Sheng X, Thorne T, Jovanovic G, Engl C, Buck M, Pinney JW, Stumpf MPet al., 2010, The evolution of the Phage shock protein (Psp) response system: interplay between protein function, genomic organization and system function., Mol Biol Evol

Journal article

Kelly WP, Thorne TW, Stumpf MPH, 2009, Statistical Null Models for Biological Network Analysis, Statistical and Evolutionary Analysis of Biological Networks

Book chapter

Stumpf MPH, Thorne T, de Silva E, Stewart R, An HJ, Lappe M, Wiuf Cet al., 2008, Estimating the size of the human interactome, PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, Vol: 105, Pages: 6959-6964, ISSN: 0027-8424

Journal article

Thorne T, Stumpf MPH, 2007, Generating confidence intervals on biological networks, BMC BIOINFORMATICS, Vol: 8, ISSN: 1471-2105

Journal article

Stumpf MPH, Kelly WP, Thorne T, Wiuf Cet al., 2007, Evolution at the system level: the natural history of protein interaction networks, TRENDS IN ECOLOGY & EVOLUTION, Vol: 22, Pages: 366-373, ISSN: 0169-5347

Journal article

de Silva E, Thorne T, Ingram P, Agrafioti I, Swire J, Wiuf C, Stumpf MPHet al., 2006, The effects of incomplete protein interaction data on structural and evolutionary inferences, BMC BIOLOGY, Vol: 4

Journal article

Stumpf MPH, Thorne TW, 2006, Multi-model inference of network properties from incomplete data, Journal of Integrative Bioinformatics, ISSN: 1613-4516

Journal article

Wilson T, Thorne T, Identifying sub-populations of cells in single cell transcriptomic data – a Bayesian mixture model approach to zero-inflation of counts

<jats:title>Abstract</jats:title><jats:p>Single cell RNA-seq data exhibit large numbers of zero count values, that we demonstrate can, for a subset of transcripts, be better modelled by a zero inflated negative binomial distribution. We develop a novel Dirichlet process mixture model which employs both a mixture at the cell level to model multiple cell types, and a mixture of single cell RNA-seq counts at the transcript level to model the transcript specific zero-inflation of counts. It is shown that this approach outperforms previous approaches that applied multinomial distributions to model single cell RNA-seq counts, and also performs better or comparably to existing top performing methods. By taking a Bayesian approach we are able to build interpretable models of expression within clusters, and to quantify uncertainty in cluster assignments. Applied to a publicly available data set of single cell RNA-seq counts of multiple cell types from the mouse cortex and hippocampus, we demonstrate how our approach can be used to distinguish sub-populations of cells as clusters in the data, and to identify gene sets that are indicative of membership of a sub-population. The methodology is implemented as an open source Snakemake pipeline available from <jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://github.com/tt104/scmixture">https://github.com/tt104/scmixture</jats:ext-link>.</jats:p>

Journal article

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