Welcome to the Hellander Lab

Combining multiscale modeling, machine learning and applied cloud computing to develop smart tools for probing living systems

Who we are

We are a group with its methodological base in scientific computing and we are strong in traditional areas such as mathematical modeling, solution of differential equations and high-performance computing. Stochastic models, discrete event simulation, and multiscale chemical kinetics have been particularly active areas of research . A prominent application area in our group is computational systems biology, where we seek to gain insight into the functioning of cellular regulatory systems through simulation of molecular and mechanistic processes.

We are located at the Division of Scientific Computing at the Department of Information Technology, Uppsala University. We participate in the eSSENCE e-Science collaboration and the Distributed Computing Applications (DCA) group.

Our approach

Solutions to challenging problems are rarely found by using methods from only one discipline. One of our core values is that we curiously explore new and emerging technology to improve our problem solving capability.  Most of our active projects combine methods and software from traditional scientific computing, machine learning, scientific cloud computing and data engineering. For example, by integrating discrete event simulation, global optimization,  human-in-the loop semi-supervised learning, and implemented using modern cloud computing platforms, we develop next-generation of intelligent software for model exploration of high-dimensional systems.


Ongoing Projects

Likelihood-free parameter inference
Biochemical reaction networks represent complex cellular regulatory mechanisms. These networks are typically analyzed using discrete stochastic simulation models. The models may involve numerous reactions involving a large number of chemical ...
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Hierarchical Analysis of Spatial and Temporal Data
The HASTE project, a SSF-funded project on computational science and big data, takes a holistic approach to new, intelligent ways of processing and managing very large amounts of microscopy images ...
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Scalable simulation of stochastic multicellular systems
In multicellular systems, cells of different types interact in various ways, both mechanically and chemically, to regulate complex processes. There is a large computational gap between detailed models of sub-cellular, ...
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Intelligent model exploration
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 ...
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StochSS: Stochastic Simulation Service
StochSS is an integrated development environment (IDE) for discrete stochastic biochemical simulations. Users make use of a graphical user interface (GUI) to define their problem, including its domain (geometry, volume), ...
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Multiscale simulations of chemical kinetics
Life spans in size from small organisms consisting of single cells to complex organisms built up of billions of cells. Even the single-cell organisms are challenging to fully understand and ...
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