Pluripotent stem cells can differentiate into any other cell type. They are unrivalled tools to understand cell differentiation, provide alternatives to animal testing, and are key for regenerative medicine and improving ageing. One of the new focus of our lab is understanding the maintenance of pluripotent stem cells' pluripotency and differentiation into other cell types by developing computational models. The models contain biochemical reactions involved in signal transduction and gene regulation. Numerical simulations allow prediction of the temporal evolution of molecule concentrations and other cellular properties. Parameters are estimated from experimental datasets and literature. The following is one of the possible PhD studentship projects we would like to offer. The importance of non-coding RNAs in the regulation of stem cell gene regulatory networks is increasingly recognised (Rev Rosa and Brivanlou (2013) Int J Mol Sci, 14: 14346-14373). However, their precise interactions with the transcription factor network and the chromatin remodelling processes is not fully understood and quantitatively modelled. The candidate will develop computational models to shed light on the role of key microRNAs and long non-coding RNAs in the control of cellular pluripotency and differentiation. Those models will be connected to the cellular chassis developed in the group and the existing signalling and gene expression modules. A variety of modelling approaches will be used. Model parametrisation and validation will be performed with existing experimental results available in the literature and the institute. The candidate must have solid knowledge of molecular and cellular biology and basis in numerical analysis and statistics.
- Thiele et al. (2013) A community-driven global reconstruction of human metabolism. Nat Biotechnol, 31: 419-425
- M. Mattioni and N. Le Novère (2013) Integration of biochemical and electrical signaling - multiscale model of the medium spiny neuron of the striatum. PLoS ONE, 8: e66811
- F. Büchel et al (2013) Path2Models: Large-scale generation of computational models from biochemical pathway map. BMC Systems Biology, 7:116