How Boolean Models Can be Used to Model Heterogeneity in Cancer Studies

There are many challenges to address when modelling cancer evolution. Heterogeneity is one key aspect to consider and can be studied at the level of the patients (all patients have different molecular profiles) or/and at the level of the tumour (with the coexistence of multiple clones inside the tumour). All these differences can be enhanced depending on the status of the tumour microenvironment and how the tumour cells interact with it. I will show some examples of how these flavours of heterogeneity are treated in mathematical models using a stochastic Boolean formalism and how omics data can be integrated into these models to provide patient-specific models. Moderator: James A. Glazier, PhD, Indiana University, Bloomington To learn more see: Calzone, Laurence, Vincent Noël, Emmanuel Barillot, Guido Kroemer, and Gautier Stoll. "Modeling signaling pathways in biology with MaBoSS: From one single cell to a dynamic population of heterogeneous interacting cells." Computational and Structural Biotechnology Journal 20 (2022): 5661-5671. https://www.sciencedirect.com/science/article/pii/S2001037022004512 *Contents* 0:00 - Introduction: J Glazier 2:26 - Coming Up Next week 4:40 - Laurence Calzone: How Boolean Models Can be Used to Model Heterogeneity in Cancer Studies 32:08 - Question & Answers Session To view the slides in this video, visit: https://drive.google.com/file/d/1zhZA1ZDfj6ujuoZrzuTThMNX4CXYjs1Q/ If you found this video useful, please check out our other videos on computational modeling, infection and immunology: https://tinyurl.com/GLIMPRINTVideos 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/