Email Address: email@example.com
Other Research Theme Collaborations: COLD
BSU Research overview
New experimental techniques to probe biological processes are developed in ever faster succession, from genome sequencing to the exploration of gene expression patterns in tissues and cells. Despite the detection of many associations between variations in the genetic make-up or in the gene expression patterns and certain characteristics of the organism, understanding the details of biological processes behind such connections has turned out to be more challenging. We develop statistical, mathematical, and algorithmic methods that support the construction of more detailed models of cellular processes from high throughput data, particularly for exploring gene regulation using data on gene expression and the structure of genomes, but also for exploring signalling, metabolism or the immune system. A Bayesian framework enables inference in a principled way, providing clear strategies to model complex data and integrate prior knowledge. If necessary for computational efficiency, we also resort to more algorithmic approaches. Nonparametric methods, for example based on Gaussian processes or Dirichlet process priors, allow us to be as open about mechanistic details as possible and let the data speak for themselves.
Selected PapersWang, D., Rendon, A. & Wernisch, L. (2012)Transcription factor and chromatin features predict genes associated with eQTLs.
Nucleic Acids Research :
Reid, J. E. & Wernisch, L. (2011)STEME: efficient EM to find motifs in large data sets.
Nucleic Acids Research 39: e126
Newton, R., Hinds, J. & Wernisch, L. (2011)Empirical Bayesian models for analysing molecular serotyping microarrays.
BMC Bioinformatics 12: 88
Luo, Y., Lim, C. L., Nichols, J., Martinez-Arias, A. & Wernisch, L. (2013)Cell signalling regulates dynamics of Nanog distribution in embryonic stem cell populations.
Journal of the Royal Society Interface 10: 20120525
Domedel-Puig, N., Pournara, I. & Wernisch, L. (2010)Statistical model comparison applied to common network motifs.
BMC Systems Biology 4: 18