BioDynaMo Version 1.04 Released

 I am pleased to announce the release of BioDynaMo 1.04.

Please have a look at the release notes, installation instructions and examples for further information.

Thanks and enjoy!
Lukas Breitwieser for the BioDynaMo team

Comments

Post a Comment

Popular posts from this blog

BioDynaMo in 2024 - End of the year remarks, and Holidays greetings

Image
Dear all,  as we are approaching the end of 2024, we would like to take a moment to reflect on our collaborative achievements within the BioDynaMo community. It has been a year marked by innovation, resilience, and shared commitment to advancing our understanding of various biological systems through innovative computational modelling. This year, the consortium had several important successes. We would like to specifically mention/highlight: The paper presenting BioDynaMo’s application to cancer treatment. Radiation-induced fibrosis is a major risk factor in radiotherapy for lung cancer patients. BioDynaMo members including our Nicolo as first author published in the very prestigious Nature Communications Medicine journal, showing how BioDynaMo can be used to simulate the impact of radiation on the lung on a single cell level. Such modelling can be used to improve radiotherapy: https://www.nature.com/articles/s43856-024-00442-w Also in the domain of oncology, a study leve...
Read more

To know what we know: from the science of emergence, to BioDynaMo

Image
   NetBioMed , a satellite of the NetSci conference , in their own words brings together researchers on Machine Learning, Digital Twins and Complex Systems towards solving problems in biology and medicine . On June the 2nd 2025  Dr Marco Manca  (President of SCImPULSE Foundation ) has joined friends and colleagues from around the world to discuss the epistemology of life sciences. In an intense half an hour talk, Dr Manca has lead the audience through a brief history of delusions in medical knowledge, to immediately focus on the processes and pitfalls of building human knowledge, the impact of assumptions (dscussing the case of ergodicity assumptions) and models on measurements (and thus, on data). He discussed the pros and cons of machine learning and the limitations of the aspiration to extract "objective knowledge" from data, or even dependable knowledge, because of the very founding principles on which ML as we have it today works. He then mo...
Read more