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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

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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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yannik_schaelteYannik Schรคlte@yannik_schaelte·
9 Jun

๐ŸŽ’Summer school: ๐ˆ๐ง๐ฏ๐ž๐ซ๐ฌ๐ž ๐ฉ๐ซ๐จ๐›๐ฅ๐ž๐ฆ๐ฌ ๐Ÿ๐จ๐ซ ๐ฆ๐ฎ๐ฅ๐ญ๐ข-๐ฌ๐œ๐š๐ฅ๐ž ๐ฆ๐จ๐๐ž๐ฅ๐ฌ

๐Ÿ’ฌWith: Linda Petzold, Christiane Fuchs, @dennisprangle, @StefanEngblom, @A_Hellander

โฐAugust 22-26
๐Ÿ“Œ@HCM_Bonn

โœ๏ธDetails+register: http://www.hcm.uni-bonn.de/events/eventpages/hausdorff-school/hausdorff-schools-2022/inverse-2022/

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A_HellanderAndreas Hellander@A_Hellander·
25 May

Really enjoyed presenting our work on federated learning with FEDn at CCGRID22 last week. Some takeaways from the talk (1/3):

1. Algorithm development must have real-world scalability in mind and there is a risk in missing this aspect if only considering simulation of FL.

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A_HellanderAndreas Hellander@A_Hellander·
25 May

Such a beautiful paper put together by @adameykolab, and one of the most fun applications of fluid mechanics modeling I have seen in recent years @AnassBouchnita @MurtazoNazarov. Thanks for the collaboration!

Igor Adameyko@adameykolab

After a long struggle, failed revision in Science, loads of happiness and pain, our paper on surface-associated water streams integrating polyps into a coral colony, is out in Current Biology. https://www.cell.com/current-biology/fulltext/S0960-9822(22)00672-8?fbclid=IwAR0j5TqfenF0_tp1hDam43K6jdfWS9iw16OzGVHZsVZ89eDe0Yq8Aa-ykuY#%20 1/10

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adameykolabIgor Adameyko@adameykolab·
14 May

After a long struggle, failed revision in Science, loads of happiness and pain, our paper on surface-associated water streams integrating polyps into a coral colony, is out in Current Biology. https://www.cell.com/current-biology/fulltext/S0960-9822(22)00672-8?fbclid=IwAR0j5TqfenF0_tp1hDam43K6jdfWS9iw16OzGVHZsVZ89eDe0Yq8Aa-ykuY#%20 1/10

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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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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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