About my research interests
My area of research is broadly at the interface between mathematical biology and statistics where I am interested in developing realistic stochastic models alongside Bayesian statistical methodologies that allow us to infer models from observed data. Often the modeling side involves very interesting non-linearities and stochastic processes giving rise to statistical methodologies that are computationally challenging. 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) from such systems and have worked on applications in epidemiology (dynamics of infectious diseases), on analytical population dynamics in ecology, and on modelling and inference for the stochastic transcriptional dynamics of single genes and transcriptionally interacting genes (including inference about small networks of genes).
My recent collaborations also include addressing questions in chronobiology and circadian rhythm with collaborators in the biological sciences and medicine. In collaboration with Francis Lévi and members of the Chronotherapy group at Warwick Medical School, Warwick Systems Biology and INSERM France, I am developing statistical methods and models for inference from large data sets of biomarkers on circadian oscillations, such as actigraphy data, with the aim of using these for personalized medicine and treatment of cancer patients in their home, as well as basic research in chronobiology.
Current or 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 (OxWaSp DTC).
Postdoctoral Collaborators on Research Grants: Qi Huang, Giorgos Minas, Hiroshi Momiji.
Some selected publications
Featherstone et al, Spatially Coordinated Dynamic Gene Transcription in Living Pituitary Tissue, eLife 2016;5:e08494
Hey et al, A stochastic transcriptional switch model for single cell imaging data, Biostatistics 2015, Vol.16, No. 4, 655-69. doi:10.1093/biostatistics/kxv010
Finkenstädt et al, Quantifying intrinsic and extrinsic noise in gene transcription using the linear noise approximation: an application to single cell data, Annals of Applied Statistics 2013, Vol. 7, No. 4, 1960–1982.
Costa et al, Inference on periodicity of circadian time series, Biostatistics 2013, 14(4):792-806. doi: 10.1093/biostatistics/kxt020.
Woodcock et al, A hierarchical model of transcriptional dynamics allows robust estimation of transcription rates in populations of single cells with variable gene copy number, Bioinformatics 2013, 29 (12): 1519-1525. doi: 10.1093/bioinformatics/btt201.
Jenkins, D, Finkenstädt B, et al, A temporal switch model for estimating transcriptional activity in gene expression. Bioinformatics 2013 May 1; 29(9): 1158–1165. doi:10.1093/bioinformatics/btt111 (link)
Gould et al, Network balance via CRY signalling controls the Arabidopsis circadian clock over ambient temperatures, Molecular Systems Biology 2013 9:650; doi:10.1038/msb.2013.7; (link)
Windram et al, Arabidopsis defence against Botrytis cinerea: chronology and regulation deciphered by high-resolution temporal transcriptomic analysis, Plant Cell 2012 24 (10), 3949 - 3965; (link)
Harper CV, Finkenstädt B, et al Dynamic Analysis of Stochastic Transcription Cycles. PLoS Biology 2011 9(4): e1000607. doi:10.1371/journal.pbio.1000607 (link)
Komorowski, M. , Finkenstädt , B., Rand, D. A. , (2010); Using single fluorescent reporter gene to infer half-life of extrinsic noise and other parameters of gene expression, Biophysical Journal 2010, Vol 98, Issue 12, 2759-2769, online at http://www.cell.com/biophysj/
Komorowski et al, Bayesian inference of biochemical kinetic parameters using the linear noise approximation, BMC Bioinformatics 2009, 10:343 doi:10.1186/1471-2105-10-343, 2009, online at: http://www.biomedcentral.com/1471-2105/10/343
Finkenstadt et al, Reconstruction of transcriptional dynamics from gene reporter data using differential equations, Bioinformatics 2008; 24: 2901 - 2907; (link)
Heron, E., Finkenstädt, B. F. and Rand, D.A., (2007), Bayesian Inference for dynamic transcriptional regulation; the Hes1 system as a case study. Bioinformatics 2007, 23 (19), 2589-2595; (link)
Lekone, P.E. and Finkenstädt, B. F., (2006), Statistical Inference in a stochastic epidemic SEIR model with control intervention: Ebola as a case study. Biometrics, 2006 (62), 1170-1177.
Finkenstädt, B. F., Morton, A.M and Rand, D. A. (2005), Modelling antigenic drift in weekly flu incidence. Statistics in Medicine, 2005 (24), 3447-3461.
email: Barbel.Finkenstadt 'at' warwick.ac.uk
Telephone: 024 7657 2580