We are currently hiring a PhD student to work on the project From Single Cells to Tumors – Multiscale Simulation of Stochastic (multi)cellular Systems
Deadline for application: March 15.
Instructions for how to apply can be found here:
Instructions for application.
We have an opening for a PhD student in computational systems biology. Interested candidates are encouraged to contact me for more details.
Apply to the position here.
After more than a year of development, we are happy to announce the release of PyURDME 1.0.0! PyURDME is a Python module for spatial stochastic simulation model development and simulation. PyURDME is connected to URDME in that is uses a modified version of its core solver. While URDME is mainly designed as an interactive Matlab toolbox that makes use of the tight connection between Comsol Multiphysics to provide an interactive modeling environment, PyURDME is an object oriented API relying only on open source software, in particular the FeniCS/Dolfin project, providing great flexibility for modelers and developers to customize computational experiments.
PyURDME has also been designed with Cloud/Distributed computing in mind, and in particular it integrates well with the IPython tools, such as IPython Notebook. We are currently working on a platform for deploying PyURDME as a Cloud appliance, with support for interactive parallel computational experiments via IPython Parallel, so check back soon for updates on this project. We are also working on integration with StochSS, which ill provide an easy-to-use UI assisted endpoint to PyURDME.
PyURDME is a collaboration with Brian Drawert at UCSB.
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).
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.
We are happy to announce the release of URDME 1.2. New features include support for Comsol 4.1, 4.2 and 4.3. We have also added the ability to compile the solvers independently of Matlab libraries. This will greatly simplify deployment of URDME jobs by StochSS down the line.
To get URDME 1.2: www.urdme.org
Or visit us on GitHub.
Ever since the first version of URDME, a software framework based on our theoretical work on RDME simulations on unstructured meshes, was made public in 2008, we have wanted to write up a journal publication that describes the software. For variuos reasons we have not gotten around to it, until now.
The paper, published in BMC systems Biology, contains two modeling examples and one example that illustrates how one can use the framework as a tool in methods development. We also discuss the relationship between URDME and two other great RDME simulators, mesoRD and STEPS.
The RDME will break down in the limit of vanishing voxel sizes, in the sense that contributions from bimolecular reactions will be lost. The problem sets on earlier (for larger voxels), the more diffusion limited the reaction is. This is a problem that has attracted a lot of interest since it was pointed out by Samuel Isaacson in this paper.
Recently, corrections to the bimolecular rates that are explicitly mesh-dependent has been proposed to deal with the problem. Erban and Chapman finds an expression in 3D that works down to a critical size of the mesh.
In this paper, we use a theorem from Montroll to show that there will always be such a critical mesh size for which no local correction to the RDME can make it agree with the Smoluchowski model in the sense that the mean binding time between two particles should be the same in both models. In the limit of perfect diffusion control, we find analytical values for the critical size in both 2D and 3D. Interestingly, the value we find in 3D agrees with the value found by Erban and Chapman. We also discuss the relationship between the local corrections of Erban and Chapman and ours to those derived by Fange et. al.