CAMBAM_2020

CRM CAMBAM PhysiCell training materials

Slides for Part 1:

https://github.com/physicell-training/CAMBAM_2020/blob/master/CRM-CAMBAM%20(Part%201).pdf

Slides for Part 2:

https://github.com/physicell-training/CAMBAM_2020/blob/master/CRM-CAMBAM%20(Part%202).pdf

Some links (most of which are in the slides):

• PhysiCell project & downloads http://PhysiCell.org
• Twitter updates: https://twitter.com/PhysiCell
• Tutorials: http://www.mathcancer.org/blog/physicell-tutorials/
• Tools (in progress): https://github.com/PhysiCell-Tools
• Training (in progress): https://github.com/PhysiCell-Training
• Wiki (in progress): http://PhysiCell.org/wiki

Models and training apps on nanoHUB (requires free registration):

• heterogeneous cancer growth (https://nanohub.org/tools/pc4heterogen)
• use the "biorobots" as a cell-based cancer therapy (https://nanohub.org/tools/pc4cancerbots)
• tumor-stroma biomechanical feedbacks (https://nanohub.org/tools/pc4livermedium)
• basic cancer immunotherapy model (https://nanohub.org/tools/pc4cancerimmune)
• COVID-19 simulation model(https://nanohub.org/tools/pc4covid19)
• Avengers Endgame battle using cell rules (https://nanohub.org/tools/pc4thanos)
• training on biased random cell migration (https://nanohub.org/tools/trmotility)
• Jupyter notebook to visualize data with Python (https://nanohub.org/tools/covid19analysis)

Models bundled in PhysiCell:

•biorobots, cancer biorobots, heterogeneity, cancer immunotherapy (3D version), virus-macrophage sample, project templates

Selected Publications

• BioFVM method paper (3-D diffusion) A. Ghaffarizadeh, S.H. Friedman, and P. Macklin. BioFVM: an efficient, parallelized diffusive transport solver for 3-D biological simulations.Bioinformatics32(8):1256-8, 2016. DOI:10.1093/bioinformatics/btv730

• PhysiCell method paper (agent-based model) A. Ghaffarizadeh, R. Heiland, S.H. Friedman, S.M. Mumenthaler, and P. Macklin. PhysiCell: an open source physics-based cell simulator for 3-D multicellular systems. PLoS Comput. Biol. 14(2):e1005991, 2018. DOI:10.1371/journal.pcbi.1005991.

• PhysiBoSS(PhysiCell+ MaBoSS for Boolean networks) G. Letort, A. Montagud, G. Stoll, R. Heiland, E. Barillot, P. Macklin, A. Zinovyev, and L. Calzone. PhysiBoSS: a multi-scale agent based modelling framework integrating physical dimension and cell signalling. Bioinformatics35(7):1188-96, 2019. DOI:10.1093/bioinformatics/bty766.

• xml2jupyter paper (create GUIs for cloud-hosted models) R. Heiland, D. Mishler, T. Zhang, E. Bower, and P. Macklin. xml2jupyter: Mapping parameters between XML and Jupyter widgets. Journal of Open Source Software4(39):1408, 2019. DOI:10.21105/joss.01408.

• PhysiCell+EMEWS(high-throughput 3D PhysiCellinvestigation) J. Ozik, N. Collier, J. Wozniak, C. Macal, C. Cockrell, S.H. Friedman, A. Ghaffarizadeh, R. Heiland, G. An, and P. Macklin. High-throughput cancer hypothesis testing with an integrated PhysiCell-EMEWS workflow. BMC Bioinformatics 19:483, 2018. DOI:10.1186/s12859-018-2510-x.

• PhysiCell+EMEWS2 (HPC accelerated by machine learning) J. Ozik, N. Collier, R. Heiland, G. An, and P. Macklin. Learning-accelerated Discovery of Immune-Tumour Interactions. Molec. Syst. Design Eng. 4:747-60, 2019. DOI:10.1039/c9me00036d.

• A review of cell-based modeling (in cancer): J. Metzcar, Y. Wang, R. Heiland, and P. Macklin. A review of cell-based computational modeling in cancer biology. JCO Clinical Cancer Informatics 3:1-13, 2019 (invited review). DOI:10.1200/CCI.18.00069.

• Progress on multicellular systems biology: P. Macklin, H.B. Frieboes, J.L. Sparks, A. Ghaffarizadeh, S.H. Friedman, E.F. Juarez, E. Jockheere, and S.M. Mumenthaler. "Progress Towards Computational 3-D Multicellular Systems Biology". In: . Rejniak(ed.), Systems Biology of Tumor Microenvironment, chap. 12, pp. 225-46, Springer, 2016. ISBN: 978-3-319-42021-9. (invited author: P. Macklin). DOI:10.1007/978-3-319-42023-3_12.

• Challenges for data-driven multicellular systems biology P. Macklin. Key challenges facing data-driven multicellular systems biology. GigaScience8(10):giz127, 2019. DOI:10.1093/gigascience/giz127

• COVID-19 community preprint Y. Wang et al., Rapid community-driven development of a SARS-CoV-2 tissue simulator. bioRxiv2020.04.02.019075 (2020). DOI:10.1101/2020.04.02.019075