Simulating Solid Tumors with a Microenvironment-Coupled Agent-Based Computational Model

Dániel Kiss; Anna Lovrics

doi:10.2478/auseme-2018-0006

Abstract

In this paper, we introduce a three-dimensional lattice-based computational model in which every lattice point can be occupied by an agent of various types (e.g. cancer cell, blood vessel cell or extracellular matrix). The behavior of agents can be associated to different chemical compounds that obey mass-transfer laws such as diffusion and decay in the surrounding environment. Furthermore, agents are also able to produce and consume chemical compounds. After a detailed description, the capabilities of the model are demonstrated by presenting and discussing a simulation of a biological experiment available in the literature.

References

[1] Starruß, J., de Back, W., Brusch, L., Deutsch, A. “Morpheus: a user-friendly modeling environment for multiscale and multicellular systems biology”, Bioinformatics 30, 9 (2014) 1331–1332.10.1093/bioinformatics/btt772
Search in Google Scholar Back to article
[2] 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 (2018).10.1371/journal.pcbi.1005991
Search in Google Scholar Back to article
[3] Kang, S., Kahan, S., McDermott, J., Flann, N., Shmulevich, I., “Biocellion: accelerating computer simulation of multicellular biological system models”, Bioinformatics 30, 21 (2014) 3101–3108.10.1093/bioinformatics/btu498
Search in Google Scholar Back to article
[4] Drasdo, D., Höhme, S., “Individual-Based Approaches to Birth and Death in Avascular Tumors”, Mathematical and Computer Modelling 37, (2003) 1163–1175.10.1016/S0895-7177(03)00128-6
Search in Google Scholar Back to article
[5] Drasdo, D., Hoehme, S., Block, M., “On the Role of Physics in the Growth and Pattern Formation of Multi-Cellular Systems: What can we Learn from Individual-Cell Based Models?”, Journal of Statistical Physics 128, 1 (2007) 287–345.10.1007/s10955-007-9289-x
Search in Google Scholar Back to article
[6] Walker, D. C., Southgate, J., Hill, G., Holcombe, M., Hosea, D. R., Wood, S. M., Mac Neil, S., Smallwood, R. H., “The epitheliome: agent-based modelling of the social behaviour of cells.”, BioSystems 76, (2004) 89–100.10.1016/j.biosystems.2004.05.02515351133
Search in Google Scholar Back to article
[7] Cytowski, M., Szymanska, Z., “Large-Scale Parallel Simulations of 3D Cell Colony Dynamics”, Computing in Science & Engineering 16, 5 (2014) 86–95.10.1109/MCSE.2014.2
Search in Google Scholar Back to article
[8] Cytowski, M., Szymanska, Z., Uminski, P., Andrejczuk, G., Raszkowski K., “Implementation of an Agent-Based Parallel Tissue Modelling Framework for the Intel MIC Architecture”, Scientific Programming 8721612, (2017).10.1155/2017/8721612
Search in Google Scholar Back to article
[9] Waclaw, B., Bozic, I., Pittman, M. E., Hruban, R. H., Vogelstein, B., Nowak, M. A., “A spatial model predicts that dispersal and cell turnover limit intratumour heterogeneity”, Nature 525, 7568 (2015) 261–264.10.1038/nature14971478280026308893
Search in Google Scholar Back to article
[10] Summers, H. D., Wills, J. W., Brown, M. R., Rees, P., “Poisson-Event-Based Analysis of Cell Proliferation”, Cytometry A. 87, 5 (2015) 385–392.10.1002/cyto.a.22620496494725572722
Search in Google Scholar Back to article
[11] Yates, C. A., Ford, M. J., Mort, R. L., “A Multi-Stage Representation of Cell Proliferation as a Markov Process”, Bulletin of Mathematical Biology 79, 12 (2017) 2905–2928.10.1007/s11538-017-0356-4
Search in Google Scholar Back to article
[12] Ghaffarizadeh, A., Friedman, S. H., Macklin, P., “BioFVM: an efficient, parallelized diffusive transport solver for 3-D biological simulations”, Bioinformatics 32, 8 (2016) 1256–1258.10.1093/bioinformatics/btv730
Search in Google Scholar Back to article
[13] Ramachandran, P., Varoquaux, G., “Mayavi: 3D Visualization of Scientific Data”, IEEE Computing in Science & Engineering, 13, 2 (2011), 40–51.10.1109/MCSE.2011.35
Search in Google Scholar Back to article
[14] Roberts, D. L., Selim, M. S., “Comparative study of six explicit and two implicit finite difference schemes for solving one-dimensional parabolic partial differential equations”, International Journal for Numerical Methods in Engineering 20 (1984) 817–844.10.1002/nme.1620200504
Search in Google Scholar Back to article
[15] Mombach, J., Glazier, J., “Single cell motion in aggregates of embryonic cells”, Phys. Rev. Lett. 76, 3032 (1996).10.1103/PhysRevLett.76.3032
Search in Google Scholar Back to article
[16] Grimes, D. R., Kelly, C., Bloch, K., Partridge, M., “A method for estimating the oxygen consumption rate in multicellular tumour spheroids”, J. R. Soc. Interface 11, 20131124 (2014).10.1098/rsif.2013.1124389988124430128
Search in Google Scholar Back to article
[17] Macklin, P., Edgerton, M. E., Thompson, A. M., Cristini, V., “Patient-calibrated agent-based modelling of ductal carcinoma in situ (DCIS): From microscopic measurements to macroscopic predictions of clinical progression”, Journal of Theoretical Biology 301 (2012) 122–140.10.1016/j.jtbi.2012.02.002
Search in Google Scholar Back to article
[18] Kiss, D., Lovrics, A., “Performance analysis of a computational off-lattice tumor growth model”, Proceedings of the IEEE 30th Jubilee Neumann Colloquium (2017).10.1109/NC.2017.8263270
Search in Google Scholar Back to article

Simulating Solid Tumors with a Microenvironment-Coupled Agent-Based Computational Model

Abstract

Paradigm

My account