Statitstical Inference for small & large dynamical data sets in Systems Biology

Understanding how genes regulate each other to coordinate cellular processes and respond to their environment is of paramount importance particularly after the success of genome sequencing that led to the identification of almost the whole genome of several organisms.Transcriptional regulation is a core part of gene regulation and despite the plethora of reverse-engineering methods, more efforts are currently needed to integrate joint modeling of multiple experimental conditions, for which data are increasingly becoming available providing enhanced opportunities to inferring gene regulation. I will provide an overview of current approaches to modelling single-cell as well as aggregate bioimaging gene expression data addressing  questions such as identifying small gene networks, spatial-temporal synchronisation and the oscillatory nature of gene expression. Particular interest focuses on the circadian clock and its implication for chronotherapy in medicine, where the  analysis of large data sets from circadian biomarkers, including those generated by sensors installed in wearable devices, plays a role for the monitoring and personalization of treatment timing of cancer patients.

Bärbel Finkenstädt obtained doctorate 1994 at Humboldt University Berlin with  thesis in Econometrics and Statistics. She pursued postdoctoral research in nonlinear time series analysis with Prof Howell Tong, University of Kent, UK, and in the mathematical and statistical modelling of the spread of epidemics with Prof Bryan Grenfell, University of Cambridge, UK.  She has held University lecturing position in Statistics at University of Warwick since 2000, and has worked in statistical modelling and inference methods inference for data sets that arise in the Life Sciences in collaboration with various experimental groups in UK and Europe.