Adrien Coulier
Division of Scientific Computing
Department of Information Technology
Uppsala University
Uppsala
In the field of Systems Biology, noise plays an important role due
to the low copy number of molecules in living cells. Stochastic models
are therefore an unavoidable tool to study this phenomenon.
Simulating stochastic models, however, remains computationally expensive,
especially when considering large populations of cells.
There is therefore an increasing need for model simplification methods.
Cells, however, remain incredibly complex organisms who are far from
being completely understood, which makes model simplification particularly
challenging. As a matter of fact, understanding the origins of intrinsic
noise in cells is a critical question to address.
The goal of this research project is then twofold: first we want to quantify the effects of various approximation techniques on the results of a simulation. Second, we want to develop adaptive methods that will choose which level of approximation should be used in order to optimize the accuracy and the computational cost of a simulation.