PhD and Postdoc positions

The Center for Applied Mathematics (CIM) in Uppsala are looking for up to 3 PhD students in applied mathematics. Within this call there is an opportunity to joint the group working on the project From cell-cell interactions to embryo development: Multiscale models and simulation in systems biology. This project is a collaboration with Carolina Wählby.

The division of Scientific Computing are looking for 3 PhD students in numerical analysis/scientific computing. Here, a variant of the above project more focused on the multiscale method development is also available: Stochastic simulation of gene expression: From individual to interacting cells.

We are also looking for a Postdoc in Scientific Computing. This is an open position where the candidate will formulate a research plan (within the areas of interests in the department).

First release of StochSS

We are excited to announce the first release of StochSS: Stochastic Simulation Service.  StochSS is an integrated development environment featuring state of the art algorithms for discrete stochastic biochemical simulation. StochSS is designed to enable you to easily scale up your simulations in complexity, deploying compute resources as needed.  The current version includes algorithms for simulation of well-mixed systems via StochKit2.  Problems can be specified via a graphical user interface (or imported as StochKit2 models).

You can obtain the code at www.stochss.org.

Spatial Stochastic Simulation of the Hes1 gene regulatory network

Individual mouse embryonic stem cells have been found to exhibit highly variable differentiation responses under the same environmental conditions. Recent experimatal evidence suggest that the noisy cyclic expression of Hes1 and its downstream genes are  responsible for this, but the mechanism underlying this variability in expression is not well understood.

Together with Mark Chaplain’s group, we have recenly published a new paper in Journal of the Royal Society Interface, where we propose a spatial stochastic model of the Hes1 regulatory network. Simulations of this model with URDME suggest that the Hes1 oscillations will intrinsically give rise to broad period distributions, and hence very heterogenous cell popuations with respect to Hes1 expression. Our work suggests a simple mechanism to explain the observations that cells that were sorted according to high or low expression of Hes1 relaxed back to a heterogenous mixture of Hes1 expression. We also propose experiments to control the precise differentiation response using drug treatment.