Skip to main content
SpringerLink
Log in

Parallel Cellular Automaton Tumor Growth Model

  • Conference paper
  • First Online:
Practical Applications of Computational Biology and Bioinformatics, 12th International Conference (PACBB2018 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 803))

Abstract

“In silico” experimentation allows us to simulate the effect of different therapies by handling model parameters. Although the computational simulation of tumors is currently a well-known technique, it is however possible to contribute to its improvement by parallelizing simulations on computer systems of many and multi-cores. This work presents a proposal to parallelize a tumor growth simulation that is based on cellular automata by partitioning of the data domain and by dynamic load balancing. The initial results of this new approach show that it is possible to successfully accelerate the calculations of a known algorithm for tumor-growth.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    For the sake of simplicity, only substeps where changes have been made to the lists are shown in Fig. 3. Substeps 3 and 4 are actually part of the same overall step.

References

  1. Adamaztky, A., De Lacy, B., Tetsuya, A.: Reaction-Diffusion Computers. Elsevier (2005)

    Google Scholar 

  2. Alarcón, T., Byrne, H.M., Maini, P.K.: A cellular automaton model for tumor growth in inhomogeneous environments. J. Theor. Biol. 225(2), 257–274 (2003)

    Article  Google Scholar 

  3. Aubert, M., Badoual, M., Fereol, S., Christov, C., Grammaticos, B.: A cellular automaton model for the migration of glioma cells 3(2), 93–100 (2006)

    Google Scholar 

  4. Bandman, O.: Implementation of large-scale cellular automata models on multi-core computers and clusters. In: International Conference on High Performance and Simulation (HPCS), 1–5 July 2013. https://doi.org/10.1109/HPCSim.2013.6641431

  5. Blecic, I., Cecchini, A., Trunfio, G.A.: Cellular automata simulation of urban dynamics through GPGPU. J. Supercomputing 65, 614–629 (2013). https://doi.org/10.1007/s11227-013-0913-z

    Article  Google Scholar 

  6. Capel-Tuñon, M.I., et al.: Towards modal modelling of biological systems. Technical report: Michigan State University, pp. 1–12 (2008)

    Google Scholar 

  7. Chopard, B., Droz, M.: Cellular Automata in Modeling of Physical Systems. Cambridge University Press, Cambridge (1998)

    Book  Google Scholar 

  8. D’ambrosio, D., Filippone, G., Rongo, R., Spataro, W., Trunfio, G.A.: Cellular automata and GPGPU: an application to lava flow modeling. Int. J. Grid High Perform. Comput. (IJGHPC) 4(3), 18 (2012)

    Google Scholar 

  9. Deutsch, A., Dorman, S.: Cellular Automata Model of Biological Patterns. Characterization, Applications and Analysis. Birkhuser (2005)

    Google Scholar 

  10. Enderling, H., Anderson, A., Chaplain, M., Beheshti, A., Hlatky, L., Hahnfeldt, P.: Paradoxical dependencies of tumor dormancy and progression on basic cell kinetics. Cancer Res. 69, 8814–8821 (2009)

    Article  Google Scholar 

  11. Gibson, M.J., Keedwell, E.C., Savic, D.A.: An investigation of the efficient implementation of cellular automata on multi-core CPU and GPU hardware. J. Parallel Distrib. Comput. 77, 1125 (2015)

    Article  Google Scholar 

  12. Jiao, Y., Torquato, S.: Emergent behaviors from a cellular automaton model for invasive tumor growth in heterogeneous microenvironments. PLOS Comput. Biol. 7, Article ID: e1002314. https://doi.org/10.1371/journal.pcbi.1002314

  13. Khan, M.A., Shefeeq, T., Kumar, A.: Mathematical modeling and computer simulation in cancer dynamics. Int. J. Math. Model. Simul. Appl. 4(3), 239–254 (2011)

    Google Scholar 

  14. Patel, A.A., Gawlinski, E.T., Lemieux, S.K., Gatenby, R.A.: A cellular automaton model of early tumor growth and invasion: the effects of native tissue vascularity and increased anaerobic tumor metabolism. J. Theor. Biol. 213(3), 315–331 (2001)

    Article  MathSciNet  Google Scholar 

  15. 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)

    Article  Google Scholar 

  16. Polesczuk, J., Enderling, H.: A high-performance cellular automaton model of tumor growth with dynamically growing domains. Appl. Math. 5, 144–152 (2014)

    Article  Google Scholar 

  17. Ribba, B., Alarcón, T., Marron, K., Maini, K., Agur, Z.: The use of hybrid cellular automaton models for improving cancer therapy, pp. 444–453 (2004)

    Google Scholar 

  18. Rybacki, S., Himmelspach, J., Uhrmacher, A.: Experiments with Single Core, Multi Core, and GPU-based computation of cellular automata. In: 2009 First International Conference on Advances in System Simulation, pp. 62–69 (2009)

    Google Scholar 

  19. Tomeu, A.J., Salguero, A.G., Capel, M.I.: A parallelisation tale of two languages. Ann. Multicore GPU Program. 2(1), 81–94 (2015)

    Google Scholar 

  20. Trisilowati, Mallet, D.G.: Experimental modeling of cancer treatment. ISRN Oncology, 2012, Article ID 828701 (2012). https://doi.org/10.5402/2012/828701

Download references

Author information

Authors and Affiliations

  1. University of Cádiz, Cádiz, Spain

    Alberto G. Salguero & Antonio J. Tomeu

  2. University of Granada, Granada, Spain

    Manuel I. Capel

Authors
  1. Alberto G. Salguero
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Manuel I. Capel
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Antonio J. Tomeu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alberto G. Salguero .

Editor information

Editors and Affiliations

  1. Escuela Superior de Ingeniería Informática, Universidad de Vigo, Ourense, Spain

    Florentino Fdez-Riverola

  2. Faculty of Computing, Department of Software Engineering, Universiti Teknologi Malaysia, Johor, Malaysia

    Mohd Saberi Mohamad

  3. Department de Informática, Universidade do Minho, Braga, Portugal

    Miguel Rocha

  4. Departamento de Informática y Automática, Facultad de Ciencias, Universidad de Salamanca, Salamanca, Spain

    Juan F. De Paz

  5. Departamento de Sistemas Informáticos, Universidad de Castilla-La Mancha, Albacete, Albacete, Spain

    Pascual González

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Salguero, A.G., Capel, M.I., Tomeu, A.J. (2019). Parallel Cellular Automaton Tumor Growth Model. In: Fdez-Riverola, F., Mohamad, M., Rocha, M., De Paz, J., González, P. (eds) Practical Applications of Computational Biology and Bioinformatics, 12th International Conference. PACBB2018 2018. Advances in Intelligent Systems and Computing, vol 803. Springer, Cham. https://doi.org/10.1007/978-3-319-98702-6_21

Download citation

Publish with us

Policies and ethics

Search

Navigation