Computational Systems Biology
The CSB project group currently consists of
 Per Lötstedt, Professor in Numerical Analysis
 Stefan Engblom, Senior Lecturer
 Andreas Hellander, Senior Lecturer
 Pavol Bauer, PhD student
 Sonja Mathias, PhD student
 Adrien Coulier, PhD student
 Fredrik Wrede, PhD student
From left to right: Sonja Mathias, Andreas Hellander, Stefan Engblom, Adrien Coulier, Per Lötstedt, Pavol Bauer, Lina Meinecke
Research
Modern molecular biology has discovered and examined a vast number of cellular components. The mechanisms of regulation and functionality of the different molecules is still largely unknown. To understand the principles for cellular regulation mathematical models are now constructed and there is a need to simulate these models in silico. In some biological systems, the dynamics is given by partial differential equations. In other systems, a trajectory of the system is generated by discrete stochastic simulation. The randomness is due to the small number of molecules involved in the reaction networks and probabilistic models for chemical reactions. There is a need to develop numerical multiscale algorithms for simulation from single molecules to concentrations of chemical species and from single cells to agglomeration of cells in tissues.
Past research endeavours can be found here.
Modelling Cell Populations
In a colony, biological cells are able to exhibit emergent behaviour. However, the internal mechanisms enabling communication between cells remain poorly understood. To tackle this problem, we are interested in developing efficient ways to simulate these biological systems. The challenges consist both of the modelling itself  the exact underlying biological processes are usually unknown  and the multiscale aspects of the processes which can take place on a wide range of spatial and temporal scales.
Molecular Crowding
Up to 35% of the volume inside living cells are occupied by macromolecules. This leads to diffusion behaving differently in vivo and in vitro.
Publications
A full list of all publications including theses can be found here.
2016

Stochastic Simulation Service: Bridging the gap between the computational expert and the biologist. In PloS Computational Biology, volume 12, number 12, 2016. (DOI, Fulltext).

Analysis of neural crestderived clones reveals novel aspects of facial development. In Science Advances, volume 2, number 8, pp e1600060:116, 2016. (DOI, Fulltext).

Mesoscopic modeling of stochastic reaction–diffusion kinetics in the subdiffusive regime. In Multiscale Modeling & simulation, volume 14, pp 668707, 2016. (DOI).

Stochastic diffusion processes on Cartesian meshes. In Journal of Computational and Applied Mathematics, volume 294, pp 111, 2016. (DOI, fulltext:postprint).

Analysis and design of jump coefficients in discrete stochastic diffusion models. In SIAM Journal on Scientific Computing, volume 38, pp A55A83, 2016. (DOI, fulltext:print).

MOLNs: A cloud platform for interactive, reproducible, and scalable spatial stochastic computational experiments in systems biology using PyURDME. In SIAM Journal on Scientific Computing, volume 38, pp C179C202, 2016. (DOI).
2015

Stochastic focusing coupled with negative feedback enables robust regulation in biochemical reaction networks. In Journal of the Royal Society Interface, volume 12, number 113, pp 20150831:110, 2015. (DOI, fulltext:postprint).

Strong convergence for splitstep methods in stochastic jump kinetics. In SIAM Journal on Numerical Analysis, volume 53, pp 26552676, 2015. (DOI, fulltext:print).

Fast eventbased epidemiological simulations on national scales. In The international journal of high performance computing applications, volume 30, pp 438453, 2016. (DOI, Fulltext).

Efficient interprocess synchronization for parallel discrete event simulation on multicores. In Proc. 3rd ACM SIGSIM Conference on Principles of Advanced Discrete Simulation, pp 183194, ACM Press, New York, 2015. (DOI).

Sensitivity estimation and inverse problems in spatial stochastic models of chemical kinetics. In Numerical Mathematics and Advanced Applications: ENUMATH 2013, volume 103 of Lecture Notes in Computational Science and Engineering, pp 519527, Springer, 2015. (DOI).

Simulation of stochastic diffusion via first exit times. In Journal of Computational Physics, volume 300, pp 862886, 2015. (DOI, fulltext:postprint).

Accuracy of the Michaelis–Menten approximation when analysing effects of molecular noise. In Journal of the Royal Society Interface, volume 12, number 106, pp 20150054:110, 2015. (DOI, fulltext:postprint).

Reaction rates for mesoscopic reactiondiffusion kinetics. In Physical Review E. Statistical, Nonlinear, and Soft Matter Physics, volume 91, pp 023312:112, 2015. (DOI, fulltext:print).
Doctoral theses

Stochastic Simulation of Multiscale ReactionDiffusion Models via First Exit Times. Ph.D. thesis, Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology nr 1376, Acta Universitatis Upsaliensis, Uppsala, 2016. (fulltext, preview image).

Stochastic Simulation of ReactionDiffusion Processes. Ph.D. thesis, Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology nr 1042, Acta Universitatis Upsaliensis, Uppsala, 2013. (fulltext).

Multiscale Stochastic Simulation of ReactionTransport Processes: Applications in Molecular Systems Biology. Ph.D. thesis, Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology nr 832, Acta Universitatis Upsaliensis, Uppsala, 2011. (fulltext).

Numerical Solution Methods in Stochastic Chemical Kinetics. Ph.D. thesis, Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology nr 564, Acta Universitatis Upsaliensis, Uppsala, 2008. (fulltext).

Numerical Methods for Stochastic Modeling of Genes and Proteins. Ph.D. thesis, Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology nr 358, Acta Universitatis Upsaliensis, Uppsala, 2007. (fulltext).
History
This project was started in 2002 and has been supported by the National Graduate School in Scientific Computing, the Graduate School in Mathematics and Computing, the Swedish Foundation for Strategic Research, the eSSENCE strategic collaboration on eScience, the Swedish Research Council, and NIH under contract 1R01EB014877  01.
 Emilie Blanc
 Stefan Hellander
 Lina Meinecke
 Paul Sjöberg