• Features Of TOPAS-NBio - Release 1.0

      Schuemann, J.; McNamara, A.; Ramos-Mendez, J.; Dominguez-Kondo, N.; Perl, J.; Yoo, D.; Henthorn, Nicholas; Warmenhoven, John; Ingram, Samuel; Merchant, Michael J; et al. (2021)
      Purpose: The goal of the TOPAS-nBio project is to provide intuitive nanometer scale Monte Carlo (MC) simulations for radiobiology experiments that do not require programming expertise. Here we present new functionalities of the TOPAS-nBio system available in the first full release (v1.0). Methods: Since the open-source beta-release of TOPAS-nBio in 2019, the framework offers to connect energy deposition within irradiated cells (physics) via molecular reactions (chemistry) to cell kill/repair (biology) at the level of sub-cellular targets such as DNA. To facilitate the setup of simulations we further developed a Graphical User (GUI) Interface. TOPAS-nBio is an extension to TOPAS and layered on top of the Geant4/Geant4-DNA MC toolkit. The new release was built for TOPAS release 3.6 (based on Geant4.10.6.p3) and will be compatible with all future releases of TOPAS. Results: New geometries that were developed include two new methods to fill the cell nucleus: a whole nucleus DNA model using chromatin fibers and a fractal walk filling pattern, as well as a filling of the nucleus using spheres to represent topologically associated domains (TADs) of DNA based on the Hi-C technique. New scoring options offer direct scoring of single and double strand breaks (SSB and DSB) as well as output the DNA damage in the SDD (Standard for DNA Damage) scoring format. Chemistry was improved to provide better agreement with experimental data of G-values, both for step-by-step chemistry and a newly included independent reaction time method, which offers faster chemistry simulations. Biological outcome can now be simulated directly within TOPAS-nBio using the DaMaRiS biological effect model, or via the SDD which offers a convenient interface to other models such as MEDRAS. Conclusion: The new features of TOPAS-nBio v1.0, offer improved modeling from initial DNA damage to cell outcome, or from energy depositions to G-value propagation over time is possible