Professor Bärbel Finkenstädt Rand
Office hours Term 3
Wednesdays 12:30 - 13:30
Note: All Personal Tutees are required to meet by end of week 5 (in-person only, Room MSB1.20).
Research Interests:
My research focuses on developing statistical and machine learning methodologies—including Bayesian, parametric, and non-parametric approaches—as well as filtering techniques for State Space and Hidden Markov Models. I work on change point detection, dynamic regime transitions, spectral analysis, and other innovative methods for modeling temporal oscillatory phenomena.
My goal is to create data-driven methodologies that generate meaningful insights in the biomedical sciences. I am particularly interested in modeling oscillatory and pulsatile dynamics—such as those found in epidemics, gene expression, and molecular clocks—and developing inference techniques for temporal and spatio-temporal data across scales, from single cells to meta-populations. My past work spans epidemiology (infectious disease dynamics), analytical population ecology, and transcriptional dynamics in molecular genetics.
Current Focus:
I am now exploring large-scale physiological and actigraphic datasets from wearable sensors. In collaboration with the Chronotherapy Group at Warwick and Université Paris-Saclay, we are developing statistical methods to estimate parameters that quantify circadian rhythm stability and compute individual circadian phase. This work supports personalized medicine for cancer patients by optimizing therapy timing based on telemonitored circadian biomarkers. Beyond oncology, these methods have broad applications in diseases where circadian-aligned treatment and continuous health monitoring are beneficial.
Selected 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, Journal of the American Statistical Association, in pressLink opens in a new window
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, The LancetLink 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
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.
