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A parallel cellular automata algorithm for the deterministic simulation of 3-D multicellular tissue growth

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Abstract

Besides generating faster solutions, parallel computers can be used to solve bigger or more complex problems. In particular, they can be utilized to run simulations at finer resolutions and to model physical phenomena more realistically. We describe in this article the development of a parallel cellular automata algorithm used in the three-dimensional simulation of multicellular tissue growth. Computational models of this genre are becoming ever more important because they provide an alternative approach to continuous models and an ability to describe the dynamics of complex biological systems evolving in time. We report on the different components of the model where cellular automata is used to model different types of cell populations that execute persistent random walks on the computational grid, collide, aggregate, and proliferate until they reach confluence. We elaborate on the main issues encountered in the parallelization of the algorithm as well as its implementation on a parallel machine with a particular focus on maintaining determinism. By delaying the exchange of cells in the shared boundaries between neighboring processors till after all events related to cell division and motion are accounted for in a given time step, good parallel performance results are obtained on a high-performance cluster.

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References

  1. Hennessy, J.L., Patterson, D.A.: Computer Architecture: A Quantitative Approach, 5th edn. Morgan Kaufmann Publishers, San Francisco, CA (2012)

    Google Scholar 

  2. Brodtkorb, A.R., Dyken, C., Hagen, T.R., Hjelmervik, J.M., Storaasli, O.O.: State-of-the-art in heterogeneous computing. Sci. Prog. 18(1), 1–33 (2010)

    Google Scholar 

  3. Wolfram, S.: Cellular Automata and Complexity: Collected Papers. Addison-Wesley, Reading, MA (1994)

    Google Scholar 

  4. Chaudhuri, P.P., Chowdhury, D.R., Nandi, S., Chattopadhyay, S.: Additive Cellular Automata: Theory and Applications, vol. 1. IEEE Computer Society Press, Los Alamitos, CA (1997)

    Google Scholar 

  5. Deutsch, A., Dormann, S.: Cellular Automaton Modeling of Biological Pattern Formation: Characterization, Applications, and Analysis. Springer-Verlag, Boston (2005)

    Google Scholar 

  6. Lysaght, M.J., Hazlehurst, A.L.: Tissue engineering: the end of the beginning. Tissue Eng. 10(1–2), 309–320 (2004)

    Article  Google Scholar 

  7. An, G., Mi, Q., Dutta-Moscato, J., Vodovotz, Y.: Agent-based models in translational systems biology. Wiley Interdiscip. Rev. 1(2), 159–171 (2009)

    Google Scholar 

  8. Majno, G., Joris, I.: Cells, Tissues and Disease: Principles of General Pathology. Oxford University Press, Oxford (2004)

    Google Scholar 

  9. Page, E.H., Nance, R.E.: Parallel discrete event simulation: a modeling methodological perspective. In: Proceedings of the 1994 Workshop on Parallel and Distributed Simulation, pp. 88–93 (1994)

  10. Hwang, M., Garbey, M., Berceli, S.A., Tran-Son-Tay, R.: Rule-based simulation of multi-cellular biological systems—a review of modeling techniques. Cell. Mol. Bioeng. 2(3), 285–294 (2009)

    Article  Google Scholar 

  11. Lauffenburger, D.A., Linderman, J.J.: Receptors: Models for Binding Trafficking and Signaling. Oxford University Press, New York (1993)

    Google Scholar 

  12. Levin, S.A., Grenfell, B., Hastings, A., Perelson, A.S.: Mathematical and computational challenges in population biology and ecosystems science. Science 275(5298), 334–343 (1997)

    Article  Google Scholar 

  13. Ben Youssef, B., Tang, L.: Simulation of multiple cell population dynamics using a 3-D cellular automata model for tissue growth. Int. J. Nat. Comput. Res. 1(3), 1–18 (2010)

    Article  Google Scholar 

  14. Tang, L., Ben Youssef, B.: A 3-D computational model for multicellular tissue growth. In: Proceedings of the 3rd International Symposium on Biomedical Simulation (ISBMS’06). Lecture Notes in Computer Science, vol. 4072, pp. 29–39 (2006)

  15. Ben Youssef, B.: Simulation of cell population dynamics using 3-D cellular automata. In: Proceedings of the 6th International Conference on Cellular Automata for Research and Industry (ACRI’04). Lecture Notes in Computer Science, vol. 3305, pp. 562–571 (2004)

  16. Frame, K.K., Hu, W.S.: A model for density-dependent growth of anchorage-dependent mammalian cells. Biotechnol. Bioeng. 32, 1061–1066 (1988)

    Article  Google Scholar 

  17. Cherry, R.S., Papoutsakis, E.T.: Modelling of contact-inhibited animal cell growth on flat surfaces and spheres. Biotechnol. Bioeng. 33, 300–305 (1989)

    Article  Google Scholar 

  18. Lim, J.H.F., Davies, G.A.: A stochastic model to simulate the growth of anchorage-dependent cells on flat surfaces. Biotechnol. Bioeng. 36, 547–562 (1990)

    Article  Google Scholar 

  19. Ruaan, R.C., Tsai, G.J., Tsao, G.T.: Monitoring and modeling density-dependent growth of anchorage-dependent cells. Biotechnol. Bioeng. 41, 380–389 (1993)

    Article  Google Scholar 

  20. Zygourakis, K., Bizios, R., Markenscoff, P.: Proliferation of anchorage-dependent contact-inhibited cells: I. Development of theoretical models based on cellular automata. Biotechnol. Bioeng. 38(5), 459–470 (1991)

    Article  Google Scholar 

  21. Hawboldt, K.A., Kalogerakis, N., Behie, L.A.: A cellular automaton model for microcarrier cultures. Biotechnol. Bioeng. 43(1), 90–100 (1994)

    Article  Google Scholar 

  22. Forestell, S.P., Milne, B.J., Behie, L.A.: A cellular automaton model for the growth of anchorage-dependent mammalian cells used in vaccine production. Chem. Eng. Sci. 47(9–11), 2381–2386 (1992)

    Article  Google Scholar 

  23. Lee, Y., Markenscoff, P., McIntire, L.V., Zygourakis, K.: Characterization of endothelial cell locomotion using a Markov chain model. Biochem. Cell Biol. 73, 461–472 (1995)

    Article  Google Scholar 

  24. Lee, Y., Kouvroukoglou, S., McIntire, L.V., Zygourakis, K.: A cellular automaton model for the proliferation of migrating contact-inhibited cells. Biophys. J. 69(10), 1284–1298 (1995)

    Article  Google Scholar 

  25. Chang, L., Gilbert, E.S., Eliashberg, N., Keasling, J.D.: A three-dimensional, stochastic simulation of biofilm growth and transport-related factors that affect structure. Microbiology 149(10), 2859–2871 (2003)

    Article  Google Scholar 

  26. Kansal, A.R., Torquato, S., Harsh IV, G.R., Chiocca, E.A., Deisboeck, T.S.: Simulated brain tumor growth dynamics using a three-dimensional cellular automaton. J. Theor. Biol. 203(4), 367–382 (2000)

    Article  Google Scholar 

  27. Cickovski, T.M., Huang, C., Chaturvedi, R., Glimm, T., Hentschel, H.G.E., Alber, M.S., Glazier, J.A., Newman, S.A., Izaguirre, J.A.: A framework for three-dimensional simulation of morphogenesis. IEEE/ACM T. Comput. Biol. Bioinformat. 2(4), 273–288 (2005)

    Article  Google Scholar 

  28. Motta, S., Pappalardo, F.: Mathematical modeling of biological systems. Brief. Bioinforma. 14(4), 411–422 (2012)

    Article  Google Scholar 

  29. Azuaje, F.: Computational discrete models of tissue growth and regeneration. Brief. Bioinforma. 12(1), 64–77 (2011)

    Article  Google Scholar 

  30. Drasdo, D., Kree, R., McCaskill, J.S.: Monte Carlo approach to tissue-cell populations. Phys. Rev. E 52(6), 6635–6657 (1995)

    Article  Google Scholar 

  31. Schaller, G., Meyer-Hermann, M.: Multicellular tumor spheroid in an off-lattice voronoi-DeLaunay cell model. Phys. Rev. E 71(5 Pt 1), 051910 (2005)

  32. Palsson, E.: A three-dimensional model of cell movement in multicellular systems. Future Gener. Comput. Syst. 17, 835–852 (2001)

    Article  Google Scholar 

  33. Beyer, T., Meyer-Hermann, M.: Delauny object dynamics for tissues involving highly motile cells. In: Chauviere, A., Preziosi, L., Verdier, C. (eds.) Cell Mechanics: From Single Scale-Based Models to Multiscale Modeling, pp. 417–442. CRC Press, Boca Raton, FL (2010)

    Chapter  Google Scholar 

  34. Jiang, Y., Levine, H., Glazier, J.: Possible cooperation of differential adhesion and chemotaxis in mound formation of Dictyostelium. Biophys. J. 75(6), 2615–2625 (1998)

    Article  Google Scholar 

  35. Fu, Y.X., Chaplin, D.D.: Development maturation of secondary and lymphoid tissues. Annu. Rev. Immunol. 17, 399–433 (1999)

    Article  Google Scholar 

  36. Beyer, T., Schaller, G., Deutsch, A., Meyer-Hermann, M.: Parallel dynamic and kinetic regular triangulation in three dimensions. Comput. Phys. Commun. 172(2), 86–108 (2005)

    Article  Google Scholar 

  37. Drasdo, D., Jagiella, N., Ramis-Conde, I., Vignon-Clemental, I.E., Weens, W.: Modeling steps from benign tumor to invasive cancer: examples of intrinsically multiscale problems. In: Chauviere, A., Preziosi, L., Verdier, C. (eds.) Cell Mechanics: From Single Scale-Based Models to Multiscale Modeling, pp. 379–416. CRC Press, Boca Raton, FL (2010)

    Chapter  Google Scholar 

  38. Marée, A.F., Hogeweg, P.: How amoeboids self-organize into a fruiting body: multicellular coordination in Dictyostelium discoideum. Proc. Natl. Acad. Sci. USA 98(7), 3879–3883 (2001)

    Article  Google Scholar 

  39. Tchuente, M.: Computation on automata networks. In: Fogelman-Soulie, F., Robert, Y., Tchuente, M. (eds.) Automata Networks in Computer Science: Theory and Applications, pp. 101–132. Princeton University Press, Princeton, NJ (1987)

    Google Scholar 

  40. Lee, Y., McIntire, L.V., Zygourakis, K.: Analysis of endothelial cell locomotion: differential effects of motility and contact inhibition. Biotechnol. Bioeng. 43(7), 622–634 (1994)

    Article  Google Scholar 

  41. Cheng, G., Ben Youssef, B., Markenscoff, P., Zygourakis, K.: Cell population dynamics modulate the rates of tissue growth processes. Biophys. J. 90(3), 713–724 (2006)

    Article  Google Scholar 

  42. Fox, G.C., Williams, R.D., Messina, P.C.: Parallel Computing Works!. Morgan Kaufmann Publishers, Inc, San Fransisco, CA (1994)

    Google Scholar 

  43. Quinn, M.J.: Parallel Programming in C with MPI and OpenMP. McGraw-Hill, Dubuque, IA (2004)

    Google Scholar 

  44. Pancake, C.M.: Is parallelism for you? IEEE Comput. Sci. Eng. 3(2), 18–37 (1996)

    Article  Google Scholar 

  45. van Hanxleden, R., Scott, L.R.: Load balancing on message passing architectures. J. Parallel Distrib. Comput. 13(3), 312–324 (1991)

    Article  Google Scholar 

  46. Chung, C.A., Lin, T.-H., Chen, S.-D., Huang, H.-I.: Hybrid cellular automaton modeling of nutrient modulated cell growth in tissue engineering constructs. J. Theor. Biol. 262(2), 267–278 (2010)

    Article  Google Scholar 

  47. Hoshino, T., Hiromoto, R., Sekiguchi, S., Majima, S.: Mapping schemes of the particle-in-cell method implemented on the PAX computer. Parallel Comput. 9(1), 53–75 (1989)

    Article  Google Scholar 

  48. van Hanxleden, R., Scott, L.R.: Correctness and determinism of parallel Monte Carlo processes. Parallel Comput. 18(2), 121–132 (1992)

    Article  Google Scholar 

  49. Fox, G.C., Johnson, M.A., Lyzenga, G.A., Otto, S.W., Salmon, J.K., Walker, D.W.: Solving Problems on Concurrent Processors: General Techniques and Regular Problems, vol. I. Prentice Hall, Englewood Cliffs, NJ (1988)

    Google Scholar 

  50. Knuth, D.E.: The Art of Computer Programming-Volume 2: Seminumerical Algorithms, 2nd edn. Addison-Wesley, Reading, MA (1981)

    Google Scholar 

  51. Fishmann, G.S., Moore, L.R.: An exhaustive analysis of multiplicative congruential random number generators with modulus 2\(^{31}\)-1. SIAM J. Sci. Stat. Comput. 7(1), 24–45 (1986)

    Article  Google Scholar 

  52. Ben Youssef, B., Sammouda, R.: Pseudorandom number generation in the context of a 3D simulation model for tissue growth. In: Proceedings of the 14th International Conference on Computational Science (ICCS 2014), Procedia-Computer Sciences, 29C, pp. 2391–2400. Elsevier, Edinburgh (2014)

  53. Levesque, J.: High Performance Computing: Programming and Applications. Chapman & Hall, Boca Raton, FL (2011)

    Google Scholar 

  54. L’Ecuyer, P.: Random number generation. In: Gentle, J.E., Haerdle, W., Mori, Y. (eds.) Handbook of Computational Statistics, 2nd edn, pp. 35–71. Springer-Verlag, Berlin (2012)

    Chapter  Google Scholar 

  55. Grama, A., Gupta, A., Karypis, G., Kumar, V.: Introduction to Parallel Computing, 2nd edn. Addison-Wesley, New York (2003)

    Google Scholar 

  56. Kuck, D.J.: High Performance Computing: Challenges for Future Systems. Oxford University Press, New York (1996)

    Google Scholar 

  57. Jin, H., Jespersen, D., Mehrotra, P., Biswas, R., Huang, L., Chapman, B.: High performance computing using MPI and OpenMP on multi-core parallel systems. Parallel Comput. 37(9), 562–575 (2011)

  58. Dematté, L., Prandi, D.: GPU computing for systems biology. Brief. Bioinforma. 2(3), 323–333 (2010)

    Article  Google Scholar 

  59. Ben Youssef, B.: A visualization tool of 3-D time varying data for the simulation of tissue growth. Multimed. Tools Appl. 73(3), 1795–1817 (2014)

  60. Ben Youssef, B.: Visualization of spatial patterns of cells using a 3-D simulation model for multicellular tissue growth. In: Proceedings of the 4th IEEE International Conference on Multimedia Computing and Systems (ICMCS’14), pp. 367–374. IEEE Xplore (2014)

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Acknowledgments

The author would like to gratefully acknowledge the continued support for this research work provided by the Research Center in the College of Computer & Information Sciences (under Project Number: RC121231) as well as the Deanship of Scientific Research, both at King Saud University. Further, we would like to thank the anonymous reviewers for their valuable comments on the manuscript as well as acknowledge the support of Simon Fraser University, Canada, for providing us with access to its HPC Cluster.

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Ben Youssef, B. A parallel cellular automata algorithm for the deterministic simulation of 3-D multicellular tissue growth. Cluster Comput 18, 1561–1579 (2015). https://doi.org/10.1007/s10586-015-0455-7

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