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.

Collaboration with groups in Mathematics, Life Sciences and Medicine led by:

 Francis Lévi, Robert Dallman, Michael White, Julian Davis, Mike Hastings, Marco Brancaccio

Warwick network: E-health applications for physical activity, sleep, and circadian rhythms

---

Recent publications and preprints (since 2015)

• Chen, S and Finkenstädt, B, Bayesian Spline-based hidden Markov Models with applications to activity acceleration data and sleep analysis, http://arxiv.org/abs/2011.01567 (earlier version), under review (J Am Stat Assoc).
• Zhang, Y, Komarzynski, S, Cordina-Duverger, E, Attari, A, Huang, Q, Aristizabal, A, Faraut, B, Léger, D, Adam, R, Guénel, P, Brettschneider, J, Finkenstädt, B and Lévi, FA, Digital circadian and sleep health in individual hospital shift workers: a cross sectional telemonitoring study,Link opens in a new window eBioMedicine, Volume 81, July 2022.

• Ali, M, Duprès, A, Huang, Q, Attari, A, Dulong, S, Li, XM, Bossevot-Demaris, R, Bouchahda, M, Komarzynski, S, Finkenstädt, B, Fritsch,A, Breda, G, Lévi, F, A Comprehensive Internet-Of-Things (IoT) platform for precision health care and chronotherapy in remote monitoring of patients with chronic diseases, under review (IEEE Journal of Biomedical and Health Informatics).

  Dulong S, Huang Q, Innominato P F, Karaboue A, Bouchahda M, Pruvost A, Théodoro F, Agrofoglio L A, Adam R, Finkenstädt B and Lévi F, Circadian and chemotherapy-related changes in urinary modified nucleosides excretion in patients with metastatic colorectal cancer, Scientific Reports 11, Article number: 24015, 2021.
• Mans Unosson, Marco Brancaccio, Michael Hastings, Adam M. Johansen, Barbel Finkenstadt, A spatio-temporal model to reveal oscillator phenotypes in molecular clocks: Parameter estimation elucidates circadian gene transcription dynamics in single-cellsLink opens in a new window, Plos Computational Biology 17 (12), 2021.
• Huang Q, Komarzynski S, Bolborea M, Finkenstadt B, Lévi F, Telemonitored human circadian temperature dynamics during daily routine, Frontiers in Physiology, 2021.
• Hadj-Amar, B, Finkenstadt, B, Fiecas, M, Huckstepp, R, Identifying the Recurrence of Sleep Apnea Using a Harmonic Hidden Markov ModelLink opens in a new window, Annals of Applied Statistics 15(3): 1171-1193 (September 2021). DOI: 10.1214/21-AOAS1455
• McNamara, AV et al, 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 burstingLink opens in a new window, 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.
• Touloupou, P, Finkenstädt, B and Spencer SEF (2019), Scalable Bayesian inference for coupled hidden Markov and semi-Markov models, Journal of Computational and Graphical Statistics, 2019.
• 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.
• Komarzynski, S, Bolborea, M, Huang, Q, Finkenstädt, B, Lévi, F (2019), Predictability of individual circadian phase during daily routine for medical applications of circadian clocks , Journal of Clinical Investigation (JCI) - Insight, 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.

---

Google Scholar