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try.stochss.org: Try StochSS as a Service

Posted on June 22, 2016June 22, 2016 By admin No Comments on try.stochss.org: Try StochSS as a Service

To make it easy to try StochSS, our software for rapid model development and simulation of stochastic regulatory networks, we are now providing it as a service on http://try.stochss.org. To use it, simply follow the link to set up your account. Since this only require a modern browser and a valid email address (no software installation), we hope…

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News, Software, Stochastic Chemical Kinetics, StochSS

At Isaac Newton Institute

Posted on February 15, 2016February 15, 2016 By admin No Comments on At Isaac Newton Institute

I will be spending Feb 15-March 17 at the Isaac Newton Institute, Cambride, UK, for a program on Stochastic Dynamical Systems in Biology: Numerical Methods and Applications. Big thanks to the organizers,Radek Erban (Oxford), David Holcman (ENS – Paris), Samuel Isaacson (Boston) and Konstantinos Zygalakis (Southampton) for organizing this amazing opportunity to gather many creative people in…

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Isaac Newton Institute, Multiscale methods, Stochastic Chemical Kinetics

StochSS 1.6 is now officially released!

Posted on July 6, 2015November 6, 2015 By admin No Comments on StochSS 1.6 is now officially released!

New capabilities of Version 1.6 include: Spatial visualization now supports animation, wireframe rendering, and mesh slicing FlexCloud: run ‘cloud’ jobs on dedicated hardware (in addition to using EC2) Import SBML models Many bug fixes and stability enhancements Details and instructions on how to obtain the code can be found on the Download page Tutorials are…

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News, PyURDME, Software, Stochastic Chemical Kinetics, StochSS

StochSS 1.4: Introducing Spatial Stochastic Modeling and Simulations

Posted on September 1, 2014September 1, 2014 By admin No Comments on StochSS 1.4: Introducing Spatial Stochastic Modeling and Simulations

We are excited to announce the release of StochSS 1.4 Version 1.4 includes spatial stochastic simulation capabilities powered by  PyURDME (http://www.pyurdme.org/). Details and instructions on how to obtain the code can be found on the Download page Tutorials are available on the Documentation page Linda Petzold and Chandra Krintz – University of California Santa Barbara Per Lotstedt and Andreas Hellander – Uppsala…

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News, PyURDME, Reaction Diffusion Master Equation, Software, Stochastic Chemical Kinetics, StochSS

PhD and Postdoc positions

Posted on January 17, 2014 By admin No Comments on PhD and Postdoc positions

The Center for Applied Mathematics (CIM) in Uppsala are looking for up to 3 PhD students in applied mathematics. Within this call there is an opportunity to joint the group working on the project From cell-cell interactions to embryo development: Multiscale models and simulation in systems biology. This project is a collaboration with Carolina Wählby….

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Hes1, Multiscale methods, News, Reaction Diffusion Master Equation, Stochastic Chemical Kinetics, StochSS, URDME

Perspective: Stochastic Algorithms for Chemical Kinetics

Posted on May 15, 2013May 15, 2013 By admin No Comments on Perspective: Stochastic Algorithms for Chemical Kinetics

In a new paper in the Journal of Chemical Physics, Dan Gillespie, myself and Linda Petzold review theory and algorithms for well-mixed and spatial mesoscopic chemical kinetics. In an associated podcast, available in the journal’s Perspectives collection, we share some of our views on the grand challenges facing us as methods developers as the field…

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News, Stochastic Chemical Kinetics

Multiscale and hybrid algorithms for stochastic chemical kinetics

Posted on April 18, 2012January 15, 2013 By admin No Comments on Multiscale and hybrid algorithms for stochastic chemical kinetics
Multiscale and hybrid algorithms for stochastic chemical kinetics

Many biochemical network models display scale separation with respect to reaction rates and/or molecular copy numbers. Depending on the type of question under study, different models are best suited to simulate the system. For some parts of the system, a macroscopic model might be appropriate. For other parts, a mesoscopic model may provide additional insight…

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Multiscale methods, Reaction Diffusion Master Equation, Stochastic Chemical Kinetics

Data-and simulation-driven life science. Much of our work in eScience and applied ML has applications in life science, and in Systems Biology in particular. We aim to enable data-and simulation-driven scientific discovery.

HASTE - a cloud native framework for intelligent processing of image streams: http://haste.research.it.uu.se/

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Andreas HellanderFollow

Andreas Hellander
A_HellanderAndreas Hellander@A_Hellander·
11 May

Are you using StochSS? Please help us gather insights into what is working well and what can be improved by filling in this short survey https://forms.gle/mEqfASuUd3MDWuPS9

@LindaPetzold @briandrawert @mhucka

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A_HellanderAndreas Hellander@A_Hellander·
9 May

Apply to this PhD student position in the eSSENCE and SciLifeLab graduate school in data-intensive science!

This project is the intersection of cybersecurity and big data with main supervisor @sztoor.
https://www.uu.se/en/about-uu/join-us/details/?positionId=501061

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A_HellanderAndreas Hellander@A_Hellander·
28 Apr

PhD position in the eSSENCE/@scilifelab graduate school in data-intensive science with @cnettel: https://uu.se/en/about-uu/join-us/details/?positionId=501716 Apply and be part of a new interdiciplinary research effort @UU_University!

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A_HellanderAndreas Hellander@A_Hellander·
23 Apr

If you are a current user of StochSS please let us know your thoughts by filling out this brief user survey: https://forms.gle/3r836iph8gqFEpZX7

#systemsbiology #stochss @LindaPetzold @briandrawert @prashant_rsingh

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Retweet on TwitterAndreas Hellander Retweeted
AssistSweASSIST Sweden@AssistSwe·
14 Apr

Soon the partners in the ASSIST project will attend a workshop on federated learning arranged by Scaleout. Partners from different countries (Sweden, Belgium, Netherlands, Turkey) will contribute with nodes that train a segmentation network.

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Decentralized AI, Federated Learning. One focus area of the group is development of methods and software to address decentralized and privacy-preserving AI. We are core contributors to the FEDn open source framework for scalable federated machine learning:

https://github.com/scaleoutsystems/fedn
Introduction to Federated Learning by Andreas Hellander
Join the discussion on Decentralized AI:

Scaleout Systems is a spin-out from ISCL on a mission to enable decentralized AI and federated learning to production.

https://www.scaleoutsystems.com/

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