While we have learned a lot about gene regulation and control from single cell models, there is a limit to what can be understood without considering cell-cell interaction. However, there is a fundamental computational gap between detailed models of single cells and models of multicellular systems comprising of large number of interacting cells such as bacterial colonies, tissue and tumors.
We seek to bridge the vast computational gap between quantitative, stochastic models of intracellular regulatory pathways and coarse-level models of multicellular systems. We also engage in development of simulation methodology for modeling specific biological systems toghether with collaborators.
- Marketa Kaucka, Evgeny Ivashkin, Daniel Gyllborg, Tomas Zikmund, Marketa Tesarova, Jozef Kaiser, Meng Xie, Julian Petersen, Vassilis Pachnis, Silvia K Nicolis , Tian Yu, Paul Sharpe, Ernest Arenas, Hjalmar Brismar, Hans Blom, Hans Clevers , Ueli Suter, Andrei S Chagin, Kaj Fried, Andreas Hellander and Igor Adameyko, (2016) Analysis of neural crest-derived clones reavals novel aspects of facial development, Science Advances 2(8).
We seek to develop intelligent cloud services to support more productive use of simulations for complex computational experimentation.
The integration between on the one hand data, modeling and algorithms, and on the other hand the specification, coordination and execution of large scale and data-intensive computational experiments poses a fundamental problem in all scientific disciplines relying on modeling and simulation. While high quality data, well-parametrized models and efficient and accurate simulation algorithms are the fundamentals of successful computational experimentation they are in themselves not nearly sufficient for productive, collaborative and reproducible computationally-driven scientific discovery: software to orchestrate the experiments is needed. Today it is largely left to the modeler or engineer to manually tune models to fit data, to choose algorithms, to configure simulation workflows and to analyze simulation result. This is a big burden to place on e.g. a biologist who is mainly interested in how she can use modeling and simulation to learn new things about a biological system of interest.
Open source computational science and engineering (CSE) software is an integral part of methodology-oriented computational research and a priority in the group. Due to the ongoing transformation of e-infrastructure to clouds, methods and workflows that promote horizontal scalability and elasticity for cloud applications are needed, and this may in many cases require re-thinking of how we best make use of computational resources. Other important questions include reproducibility and handling of large and complex data.
Selected recent publications:
- B. Drawert, A. Hellander, B. Bales, D. Banerjee, G. Bellesia, B.J. Daigle, Jr. G. Douglas, M. Gu, A. Gupta, S. Hellander, C. Horuk, D. Nath, A. Takkar, S. Wu, P. Lötstedt, C. Krintz, L. R. Petzold (2016) Stochastic Simulation Service: Bridging the gap between the computational expert and the biologist, PloS Comp. Bio. (to appear)
- B. Drawert, M. Trogdon, S. Toor, L. Petzold and A. Hellander (2016) MOLNs: A cloud appliance for interactive, reproducible and scalable spatial stochastic computational experiments, SIAM J. Sci. Comput. 38(3), C179–C202.
- J. H. Abel, B. Drawert, A. Hellander, and L. R. Petzold (2015). GillesPy: A Python package for stochastic model building and simulation, IEEE LSL (to appear)
- C. Horuk, G. Douglas, A. Gupta, C. Krintz, B. Bales, G. Bellesia, B. Drawert, R. Wolski, L. Petzold, and A. Hellander, Automatic and Portable Cloud Deployment for Scientific Simulations, IEEE/ACM International Conference on High Performance Computing and Simulation, July 2014.
A theme in the last decade of computational systems biology has been how molecular noise is a factor that needs to be acc
ounted for, both to understand how gene regulatory networks are able to operate robustly in a noisy molecular environment and to explain phenotypic variability on both the individual cell and population levels. A particularly intriguing question is the interplay between spatial and temporal aspects of intracellular signaling is organized. Numerically, efficient spatial stochastic methods are needed to study this, but they become much more computationally demanding, largely due to the multiscale nature of the pathways and processes. A central area in the group is have the development of hybrid simulation methods for stochastic reaction-diffusion processes.
- E. Blanc, S. Engblom, A. Hellander and P. Lötstedt (2016) Mesoscopic modeling of stochastic reaction-diffusion kinetics in the subdiffusive regime, Multiscale Model. Simul., 14(2), 668–707.
- L. Meinecke, S. Engblom, A. Hellander, P. Lötstedt (2016) Analysis and design of jump coefficients in discrete stochastic diffusion models, SIAM J. Sci. Comput. 38(1), A55–A83.
- M. Lawson, L. Petzold and A. Hellander (2015) Accuracy of the Michaelis-Menten approximation when analyzing effects of molecular noise, Roy. Soc. Interface, 12(106) 2015
- S. Hellander, L. Petzold and A. Hellander (2015), Reaction rates for mesoscopic reaction-diffusion kinetics, Phys. Rev. E., 92(2), 023312.