Modeling & stochastic simulation
A core approach is to model and simulate complex systems using stochastic descriptions. Stochastic chemical kinetics, agent-based models and Kinetic Monte Carlo are specialities.
Computational Systems Biology
A prominent application area in the lab is computational systems biology. Systems biology is an interdiciplinary field where mathematical modling, simulation and advanced analytics are combined with cell biology to model and understand for example gene regulation on a system level. This is challenging from a simulation point of view since models often need to take into account molecular interactions and movement on a subcellular scale as well as cell-cell interactions between millions of cells. We develop both multiscale models to simulate such systems, and new smart methods and software that bridge simulation and artificial intelligence to disentangle the complex interactions that lead to qualitatively .
News
New paper on multiscale simulation of EGFR overexpression in Tumourigenesis. Stay tuned for more work by talented postdoc Anass Bouchnita 🙂 As usually a pleasure to publish in BMB! https://t.co/mzE8SBllgT
Good news, we have gotten funding for working on new methods for model development and model exploration to be added to StochSS! https://t.co/I9qVaLMhgD
Smart computational exploration of stochastic gene regulatory network models using human-in-the-loop semi-supervised learning https://t.co/o4QQMu0yKV #biorxiv_sysbio
In this preprint we propose tools that make it easier to explore stochastic models of GRNs through smart parameter sweeps. Tools like these will be central in next-gen StochSS.
Smart computational exploration of stochastic gene regulatory network models using human-in-the-loop semi-supervised learning https://t.co/2oulbBphgf #bioRxiv