Book chapters

• Gene Regulatory Network Inference Babtie AC, Stumpf MPH., Thorne T, Reference Module in Biomedical Sciences, Elsevier 2019.
• Statistical Null Models for Biological Network Analysis Kelly WP, Thorne T, Stumpf MPH, Statistical and Evolutionary Analysis of Biological Networks. Imperial College Press 2009.

Journal Publications

• Shrinkage estimation of gene interaction networks in single-cell RNA sequencing data Vo DHT, Thorne T, BMC Bioinformatics 25 (1), 339, 2024.
• Calibration of stochastic, agent-based neuron growth models with Approximate Bayesian Computation Duswald T, Breitwieser L, Thorne T, Wohlmuth B, Bauer R, Journal of Mathematical Biology 89 (5), 50, 2024.
• Identifying sub-populations of cells in single cell transcriptomic data – a Bayesian mixture modelling approach to zero-inflation of counts Wilson T, Vo DHT, Thorne T, Journal of Computational Biology 30 (10), 1059-1074, 2023.
• Inhibition of the angiotensin II type 2 receptor AT2R is a novel therapeutic strategy for glioblastoma Perryman R, Renziehausen A, Shaye H, Kostagianni AD, Tsiailanis AD, Thorne T, Chatziathanasiadou MV, Sivolapenko GB, El Mubarak MA, Han GW, Zarzycka B, Katritch V, Lebon G, Lo Nigro C, Lattanzio L, Morse SV, Choi JJ, O’Neill K, Kanaki Z, Klinakis A, Crook T, Cherezov V, Tzakos AG, Syed N, PNAS, 119 (32) e2116289119, 2022.
• Topological approximate Bayesian computation for parameter inference of an angiogenesis model Thorne T, Kirk PDW, Harrington HA, Bioinformatics, 2022.
• A Dynamic Bayesian Network Approach for Analysing Topic-Sentiment Evolution. Liang H, Ganeshbabu U, Thorne T, IEEE Access, 8, 54164–54174, 2020.
• Breaking the cycle: Reversal of flux in the tricarboxylic acid cycle by dimethyl fumarate. Gafson AR, Savva C, Thorne T, David M, Gomez-Romero M, Lewis MR, Nicholas R, Heslegrave A, Zetterberg H, Matthews PM, Neurology Neuroimmunology & Neuroinflammation 6 (3), 2019.
• Approximate inference of gene regulatory network models from RNA-Seq time series data. Thorne T, BMC Bioinformatics, 19:127, 2018.
• Lipoprotein markers associated with disability from multiple sclerosis. Gafson AR, Thorne T, McKechnie CIJ, Jimenez B, Nicholas R, Matthews PM Scientific Reports 8, 17026, 2018.
• NetDiff - Bayesian model selection for differential gene regulatory network inference. Thorne T, Scientific Reports, 6:39224, 2016.
• Empirical likelihood tests for nonparametric detection of differential expression from RNA-seq data. Thorne T, Statistical Applications in Genetics and Molecular Biology, 14(6), 575–583, 2015.
• Bayesian nonparametric approaches to reconstructing oscillatory systems and the Nyquist limit. Žurauskienė J, Kirk P, Thorne T, Stumpf MPH, Physica A, 2014.
• Derivative processes for modelling metabolic fluxes. Žurauskienė J, Kirk P, Thorne T, Pinney J, Stumpf M, Bioinformatics, 2014.
• Model selection in systems and synthetic biology. Kirk P, Thorne T, Stumpf MPH, Curr. Opin. Biotechnol., 4, 24, 767–774, 2013.
• Graphical modelling of molecular networks underlying sporadic inclusion body myositis. Thorne T, Fratta P, Hanna MG, Cortese A, Plagnol V, Fisher EM, Stumpf MPH, Mol Biosyst, 7, 9, 1736–1742, 2013.
• Inference of temporally varying Bayesian networks. Thorne T, Stumpf MPH, Bioinformatics, 24, 28, 3298–3305, 2012.
• Graph spectral analysis of protein interaction network evolution. Thorne T, Stumpf MPH, J R Soc Interface, 75, 9, 2653–2666, 2012.
• Combinatorial stresses kill pathogenic Candida species. 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, 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 AJP, Med. Mycol., 7, 50, 699–709, 2012.
• Parameter-free model discrimination criterion based on steady-state coplanarity. Harrington HA, Ho KL, Thorne T, Stumpf MPH, Proc. Natl. Acad. Sci. U.S.A., 39, 109, 15746–15751, 2012.
• Considerate approaches to constructing summary statistics for ABC model selection. Barnes CP, Filippi S, Stumpf MPH, Thorne T, Statistics and Computing, 6, 22, 1181–1197, 2012.
• Calibrating spatio-temporal models of leukocyte dynamics against in vivo live-imaging data using approximate Bayesian computation. Liepe J, Taylor H, Barnes CP, Huvet M, Bugeon L, Thorne T, Lamb JR, Dallman MJ, Stumpf MPH, Integr Biol (Camb), 3, 4, 335–345, 2012.
• A systems biology analysis of long and short-term memories of osmotic stress adaptation in fungi. 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 AJP, BMC Res Notes, 1, 5, 258, 2012.
• Prediction of putative protein interactions through evolutionary analysis of osmotic stress response in the model yeast Saccharomyces cerevisae. Thorne TW, Ho H, Huvet M, Haynes K, Stumpf MPH, Fungal Genet. Biol., 5, 48, 504–511, 2011.
• The evolution of the phage shock protein response system: interplay between protein function, genomic organization, and system function. Huvet M, Toni T, Sheng X, Thorne T, Jovanovic G, Engl C, Buck M, Pinney JW, Stumpf MPH, Mol. Biol. Evol., 3, 28, 1141–1155, 2011.
• Estimating the size of the human interactome. Stumpf MPH, Thorne T, de Silva E, Stewart R, An HJ, Lappe M, Wiuf C, Proc. Natl. Acad. Sci. U.S.A., 19, 105, 6959–6964, 2008.
• Evolution at the system level: the natural history of protein interaction networks. Stumpf MPH, Kelly WP, Thorne T, Wiuf C, Trends Ecol. Evol. (Amst.), 7, 22, 366–373, 2007.
• Generating confidence intervals on biological networks. Thorne T, Stumpf MPH, BMC Bioinformatics, 1, 8, 467, 2007.
• The effects of incomplete protein interaction data on structural and evolutionary inferences. de Silva E, Thorne T, Ingram P, Agrafioti I, Swire J, Wiuf C, Stumpf MPH, BMC Biol., 1, 4, 39, 2006.
• Multi-model inference of network properties from incomplete data. Stumpf MPH, Thorne T, J.Integr.Bioinformatics, 32, 3(2), 2006.