Abstract
Parallel three-dimensional (3D) cellular automaton models of tumor growth can efficiently model tumor morphology over many length and time scales. Here, we extended an existing two-dimensional (2D) model of tumor growth to study how tumor morphology could change over time and verified the 3D model with the initial 2D model on a per-slice level. However, increasing the dimensionality of the model imposes constraints on memory and time-to-solution that could quickly become intractable when simulating long temporal durations. Parallelizing such models would enable larger tumors to be investigated and also pave the way for coupling with treatment models. We parallelized the 3D growth model using N-body and lattice halo exchange schemes and further optimized the implementation to adaptively exchange information based on the state of cell expansion. We demonstrated a factor of 20x speedup compared to the serial model when running on 340 cores of Stampede2’s Knight’s Landing compute nodes. This proof-of-concept study highlighted that parallel 3D models could enable the exploration of large problem and parameter spaces at tractable run times.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Randles, A., et al.: Computational modelling of perivascular-niche dynamics for the optimization of treatment schedules for glioblastoma. Nat. Biomed. Eng. 5(4), 346–359 (2021)
Tang, J., et al.: Irradiation of juvenile, but not adult, mammary gland increases stem cell self-renewal and estrogen receptor negative tumors. Stem Cells (Dayton, Ohio) 32(3), 649–661 (2014)
Gao, X., McDonald, J.T., Hlatky, L., Enderling, H.: Acute and fractionated irradiation differentially modulate glioma stem cell division kinetics. Can. Res. 73(5), 1481–1490 (2013)
Piotrowska, M.J., Angus, S.D.: A quantitative cellular automaton model of in vitro multicellular spheroid tumour growth. J. Theor. Biol. 258(2), 165–178 (2009)
Jiao, Y., Torquato, S.: Emergent behaviors from a cellular automaton model for invasive tumor growth in heterogeneous microenvironments. PLoS Comput. Biol. 7(12), e1002314 (2011)
Poleszczuk, J., Enderling, H.: A high-performance cellular automaton model of tumor growth with dynamically growing domains. Appl. Math. 5(1), 144–152 (2014)
Morton, C.I., Hlatky, L., Hahnfeldt, P., Enderling, H.: Non-stem cancer cell kinetics modulate solid tumor progression. Theor. Biol. Med. Model. 8(1), 48 (2011)
Norton, K.-A., Jin, K., Popel, A.S.: Modeling triple-negative breast cancer heterogeneity: effects of stromal macrophages, fibroblasts and tumor vasculature. J. Theor. Biol. 452, 56–68 (2018)
Alfonso, J.C.L., Jagiella, N., Núñez, L., Herrero, M.A., Drasdo, D.: Estimating dose painting effects in radiotherapy: a mathematical model. PLoS ONE 9(2), e89380 (2014)
Ghaffarizadeh, A., Heiland, R., Friedman, S.H., Mumenthaler, S.M., Macklin, P.: PhysiCell: an open source physics-based cell simulator for 3-D multicellular systems. PLoS Comput. Biol. 14(2), e1005991 (2018)
Salguero, A.G., Capel, M.I., Tomeu, A.J.: Parallel cellular automaton tumor growth model. In: Fdez-Riverola, F., Mohamad, M.S., Rocha, M., De Paz, J.F., González, P. (eds.) PACBB2018 2018. AISC, vol. 803, pp. 175–182. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-98702-6_21
Towns, J., et al.: XSEDE: accelerating scientific discovery. Comput. Sci. Eng. 16(5), 62–74 (2014)
Acknowledgements
We thank Daniel Puleri, Simbarashe Chidyagwai, Sayan Roychowdhury, and Raveena Kothare for fruitful discussions. This work used an Extreme Science and Engineering Discovery Environment (XSEDE) allocation, which is supported by National Science Foundation grant number ACI-1548562. This work used Stampede2 at TACC through allocation TG-MDE210001. The research of Cyrus Tanade was supported by the National Science Foundation Graduate Research Fellowship Program under Grant No. NSF GRFP DGE 1644868. The work of Amanda Randles was supported by the National Institutes of Health under award number U01CA253511. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH or the NSF.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Tanade, C., Putney, S., Randles, A. (2022). Developing a Scalable Cellular Automaton Model of 3D Tumor Growth. In: Groen, D., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2022. ICCS 2022. Lecture Notes in Computer Science, vol 13350. Springer, Cham. https://doi.org/10.1007/978-3-031-08751-6_1
Download citation
DOI: https://doi.org/10.1007/978-3-031-08751-6_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-08750-9
Online ISBN: 978-3-031-08751-6
eBook Packages: Computer ScienceComputer Science (R0)