About my research interests
My area of research is at the interface between statistics and life sciences where I am interested in developing scientific models alongside Bayesian statistical methodologies that allow us to infer these from (increasingly large) data sets. The modeling often involves very interesting non-linearities and stochastic processes that give rise to novel statistical methodologies. I am in particular interested in the modelling of oscillatory phenomena in biology (epidemics, gene expression, molecular clocks, etc) combined with the analysis of temporal and/or spatio-temporal data (from single cells to meta-populations) and have worked on applications in epidemiology (dynamics of infectious diseases), analytical population dynamics in ecology, transcriptional dynamics of genes and large actigraphic data sets obtained from wearable devices.
My collaborations also include addressing statistical and mathematical questions in chronobiology and circadian rhythm with collaborators in the biological sciences and medicine. In a project funded by the Medical Research Council (MRC) I am collaborating with members of the Chronotherapy group at Warwick and INSERM France, to develop statistical methods and models for large data sets of biomarkers on circadian oscillations, including actigraphic data from wearable devices, with the aim of using these for personalized medicine and treatment of cancer patients in their home, and basic research in chronobiology.
Current and most recent PhD students:
Silvia Calderazzo, Simone Tiberi, Panayiota Touloupou (with S Spencer), Elena Camacho-Aguilar (with D Rand), Mans Unosson (with A Johanson), Beniamino Hadj-Amar, Sida Chen, Yiyuan Zhang (with J Brettschneider).
Current and most recent Postdoctoral Collaborators:
Collaboration with groups in Mathematics, Life Sciences and Medicine led by:
Recent publications and preprints (since 2015)
Sida Chen and Bärbel Finkenstädt, Bayesian inference for spline-based hidden Markov models, http://arxiv.org/abs/2011.01567, under review.
Transcription factor Pit-1 affects transcriptional timing in the dual-promoter human prolactin gene, Endocrinology, 2021, https://doi.org/10.1210/endocr/bqaa249
Cavallaro, M, Walsh, M, Jones, M,Teahan, J, Tiberi, S, Finkenstädt, B and Hebenstreit, D, 3’-5’ crosstalk contributes to transcriptional bursting, to appear in Genome Biology, 2021.
Lévi, F, Komarzynski, S, Huang, Q, Young, T, Ang, Y, Fuller, C, Bolborea, M, Brettschneider, J, Finkenstädt, B, Fursse, J, White, DP, and PF Innominato, Tele-monitoring of cancer patients' rhythms during daily life identifies actionable determinants of circadian and sleep disruption, Cancers, 2020, 12(7).
Touloupou, P, Finkenstãdt, B, Besser, TE, French, NP, and Spencer, SEF, Bayesian inference for multi-strain epidemics with application to Escherichia Coli O157:H7 in feedlot cattle, Annals of Applied Statistics, 2020, 14 (4), 1925-1944.
Hadj-Amar, B., Finkenstädt, B., Fiecas, M., Lévi, F. & Huckstepp, R. (2019), Bayesian Model Search for Nonstationary Periodic Time Series , Journal of the American Statistical Association, 2019.
Momiji, H, Hassall, K, Featherstone, K, McNamara, AV, Patist, AL, Spiller, DG, White, MRH, Davis, JRE, Finkenstädt, B, Rand, DA (2019), Juxtacrine signalling and space-time coordinated prolactin production, Plos Computational Biology, 2019.
Calderazzo, S, Brancaccio, M and Finkenstädt, B (2019), Filtering and Inference for stochastic oscillators with distributed delays, Bioinformatics, 2019.
Tiberi, S, Walsh, M, Cavallaro, M, Hebenstreit, D and Finkenstädt, B (2018), Bayesian inference on stochastic gene transcription from flow cytometry data, Bioinformatics, 2018.
Komarzynski S, Huang Q, Innominato PF, Maurice M, Arbaud A, Beau J, Bouchahda M, Ulusakarya A, Beaumatin N, Virasolvy F, Breda G, Finkenstädt B and Lévi F (2018), Relevance of a mobile internet platform for capturing inter- and intrasubject variations in circadian coordination during daily routine: Pilot study, Journal of Medical Internet Research 2018.
Huang, Q, Cohen, D, Komarzynski, S, Li, XM, Innominato, P, Lévi, F and Finkenstädt, B (2018), Hidden Markov Models for monitoring Circadian Rhythmicity in Telemetric Activity Data, Journal of the Royal Society - Interface, 2018.
Dunham, L, Momiji, H, Harper, C, Hey, K, McNamara, A, Featherstone, K, Spiller, D, Rand, D, Finkenstädt, B, White, M, Davis, J (2017), Asymmetrical switching behaviour in transcriptional control, Cell Systems 2017.
Minas, G, Jenkins, D, Rand, DA and Finkenstädt B (2017), Inferring transcriptional logic from multiple dynamic experiments, Bioinformatics, 2017.
Minas, G, Momiji, H, Jenkins, D, Costa, MJ, Rand, DA and Finkenstädt B (2017), ReTrOS: A MAT- LAB Toolbox for Reconstructing Transcriptional Activity from Gene and Protein Expression Data, BMC Bioinformatics, 2017.
Bechtold, U, Penfold, C, et al., Time-series transcriptomics reveals that AGAMOUS-LIKE22 links primary metabolism to developmental processes in drought-stressed Arabidopsis, The Plant Cell, 2016.
Featherstone, K, Hey, K, Momiji, H, McNamara, AV, Patist, AL, Woodburn, J, Spiller, DG, Christian, HC, McNeilly, AS, Mullins, JJ, Finkenstädt BF, Rand, DA, White, MRH, Davis, JRE, Spatially coordinated dynamic gene transcription in living pituitary tissue. eLife, 2015.
Hey, K, Momiji, H, Featherstone K, Davis J, White M, Rand D, Finkenstädt B, A stochastic transcriptional switch model for single cell imaging data, Biostatistics, 2015.
Office hours (on MS Teams)