The Wound Environment Agent-Based Model: A Digital Twin for Wound Healing

The Wound Environment Agent-Based Model is a digital twin representation of volumetric muscle loss injury and its subsequent healing. The model is informed by a range of clinical data, including CT scans, quantitative histology, proteomics, and genomics. To capture the heterogeneity of the clinical population, we employed machine-learning techniques to discover a set of model parameterizations that could not be invalidated by experimental data. We then use deep reinforcement learning on the digital twin, instantiated with a range of parametrizations to determine both the most generalizable therapies to speed healing and reduce scar formation, and individually, to optimize dose strengths and timings for individuals. For more information see: https://www.biorxiv.org/content/10.1101/2024.06.04.595972v2 *Contents* 00:00 - Introduction 05:29 - The Wound Environment Agent-Based Model: A Digital Twin for Wound Healing 49:56 - Questions and Discussions For a copy of slides in this video, visit: https://docs.google.com/presentation/d/1XZjUothBiHYi4xvRK69uKKK-lWGOx38M/edit?usp=sharing&ouid=105981546878895367801&rtpof=true&sd=true If you found this video useful, please check out our other videos on computational modeling, infection and immunology: https://youtube.com/playlist?list=PLiEtieOeWbMKh9VcQoinSwODcSZKMTGat Please consider joining our IMAG/MSM WG on Multiscale Modeling and Viral Pandemics: https://www.imagwiki.nibib.nih.gov/content/msm-viral-pandemics-meetings Please also consider joining the Global Alliance for Immune Prediction and Intervention: http://glimprint.org/