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How to Build a Multiscale Model in Biology

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Abstract

Biological processes span several scales in space, from the single molecules to organisms and ecosystems. Multiscale modelling approaches in biology are useful to take into account the complex interactions between different organisation levels in those systems. We review several single- and multiscale models, from the most simple to the complex ones, and discuss their properties from a multiscale point of view. Approaches based on master equations for stochastic processes, individual-based models, hybrid continuous-discrete models and structured PDE models are presented.

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References

  • IMAG (Interagency Modeling and Analysis Group) (2012) What exactly is multiscale modeling? http://www.imagwiki.nibib.nih.gov/mediawiki/index.php?title=What_exactly_is_Multiscale_Modeling

  • Anderson A (2005) A hybrid mathematical model of solid tumour invasion: the importance of cell adhesion. Math Med Biol 22(2):163–186

    Article  Google Scholar 

  • Anderson A, Weaver A, Cummings P, Quaranta V (2006) Tumor morphology and phenotypic evolution driven by selective pressure from the microenvironment. Cell (Cambridge, MA, US) 127(5):905–915

    Article  Google Scholar 

  • Battogtokh D, Aihara K, Tyson JJ (2006) Synchronization of eukaryotic cells by periodic forcing. Phys Rev Lett 96(14):148,102

    Article  Google Scholar 

  • Bekkal Brikci F, Clairambault J, Ribba B, Perthame B (2008) An age-and-cyclin-structured cell population model for healthy and tumoral tissues. J Math Biol 57(1):91–110

    Article  Google Scholar 

  • Benzekry S (2011) Mathematical analysis of a two-dimensional population model of metastatic growth including angiogenesis. J Evol Equ 11(1):187–213

    Article  Google Scholar 

  • Bernard S, Gonze D, Čajavec B, Herzel H, Kramer A (2007) Synchronization-induced rhythmicity of circadian oscillators in the suprachiasmatic nucleus. PLOS Comput Biol 3(4):e68

    Article  Google Scholar 

  • Billy F, Ribba B, Saut O, Morre-Trouilhet H, Colin T, Bresch D, Boissel JP, Grenier E, Flandrois JP (2009) A pharmacologically based multiscale mathematical model of angiogenesis and its use in investigating the efficacy of a new cancer treatment strategy. J Theor Biol 260(4):545–562

    Article  Google Scholar 

  • Byrne H, Drasdo D (2009) Individual-based and continuum models of growing cell populations: a comparison. J Math Biol 58(4):657–687

    Article  Google Scholar 

  • Chauvière A, Preziosi L, Verdier C (2009) Cell mechanics: from single scale-based models to multiscale modeling, vol. 32. Chapman & Hall/CRC, London

    Google Scholar 

  • Cristini V, Lowengrub J (2010) Multiscale modeling of cancer: an integrated experimental and mathematical modeling approach. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Deisboeck T, Stamatakos G (2010) Multiscale cancer modeling. vol. 34. Chapman & Hall/CRC, London

    Book  Google Scholar 

  • Dial KP, Greene E, Irschick DJ (2008) Allometry of behavior. Trends Ecol Evol 23(7):394–401

    Article  Google Scholar 

  • Doumic M (2007) Analysis of a population model structured by the cells molecular content. Math Model Nat Phenom 2(3):121–152

    Article  Google Scholar 

  • Drasdo D, Höhme S (2005) A single-cell-based model of tumor growth in vitro: monolayers and spheroids. Phys Biol 2:133

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Drasdo D, Loeffler M (2001) Individual-based models to growth and folding in one-layered tissues: intestinal crypts and early development. Nonliner Anal 47(1):245–256

    Article  Google Scholar 

  • Françoise JP (2005) Oscillations en biologie: analyse qualitative et modèles, vol. 46. Springer, Berlin

    Google Scholar 

  • Friedman A, Kao CY, Shih CW (2009) Asymptotic phases in a cell differentiation model. J Differ Equ 247(3):736–769

    Article  Google Scholar 

  • Friedman A, Kao CY, Shih CW (2012) Asymptotic limit in a cell differentiation model with consideration of transcription. J Differ Equ 252(10):5679–5711

    Article  Google Scholar 

  • Galle J, Loeffler M, Drasdo D (2005) Modeling the effect of deregulated proliferation and apoptosis on the growth dynamics of epithelial cell populations in vitro. Biophys J 88(1):62–75

    Article  Google Scholar 

  • Gatenby R, Gawlinski E (1996) A reaction-diffusion model of cancer invasion. Cancer Res 56(24):5745

    Google Scholar 

  • Gunawardena J (2012) A linear framework for time-scale separation in nonlinear biochemical systems. PLOS One 7(5):e36,321

    Article  Google Scholar 

  • Hoehme S, Brulport M, Bauer A, Bedawy E, Schormann W, Hermes M, Puppe V, Gebhardt R, Zellmer S, Schwarz M, et al (2010) Prediction and validation of cell alignment along microvessels as order principle to restore tissue architecture in liver regeneration. Proc Natl Acad Sci USA 107(23):10,371

    Article  Google Scholar 

  • Hoffmann M, Chang H, Huang S, Ingber D, Loeffler M, Galle J (2008) Noise-driven stem cell and progenitor population dynamics. PLOS One 3(8):e2922

    Article  Google Scholar 

  • Kærn M, Elston T, Blake W, Collins J (2005) Stochasticity in gene expression: from theories to phenotypes. Nat Rev Genet 6(6):451–464

    Article  Google Scholar 

  • Kaplan D, Glass L (1995) Understanding nonlinear dynamics, vol. 19. Springer, Berlin

    Book  Google Scholar 

  • Keller E, Segel L (1970) Initiation of slime mold aggregation viewed as an instability. J Theor Biol 26(3):399–415

    Article  Google Scholar 

  • Kepler T, Elston T (2001) Stochasticity in transcriptional regulation: origins, consequences, and mathematical representations. Biophys J 81(6):3116–3136

    Article  Google Scholar 

  • Lahutte-Auboin M, Guillevin R, Françoise JP, Vallée JN, Costalat R (2013) On a minimal model for hemodynamics and metabolism of lactate: application to low grade glioma and therapeutic strategies. Acta Biotheor 61(1):79–89

    Article  Google Scholar 

  • Lesart A, van der Sanden B, Hamard L, Estève F, Stéphanou A (2012) On the importance of the submicrovascular network in a computational model of tumour growth. Microvasc Res 84(2):188–204

    Article  Google Scholar 

  • Macal CM, North MJ (2005) Tutorial on agent-based modeling and simulation. In: proceedings of the 37th conference on winter simulation, pp. 2–15. Winter Simulation Conference

  • Magal P, Auger P, Ruan S (2008) Structured population models in biology and epidemiology. 1936. Springer, Berlin

    Book  Google Scholar 

  • Newman T, Grima R (2004) Many-body theory of chemotactic cell-cell interactions. Phys Rev E 70(5):051,916

    Google Scholar 

  • Paulsson J (2005) Models of stochastic gene expression. Phys Life Rev 2(2):157–175

    Article  Google Scholar 

  • Perkins T, Swain P (2009) Strategies for cellular decision-making. Mol Syst Biol 5:326

    Article  Google Scholar 

  • Powathil G, Gordon K, Hill L, Chaplain M (2012) Modelling the effects of cell-cycle heterogeneity on the response of a solid tumour to chemotherapy: biological insights from a hybrid multiscale cellular automaton model. J Theor Biol 308:1–19

    Google Scholar 

  • Qu Z, Garfinkel A, Weiss JN, Nivala M (2011) Multi-scale modeling in biology: how to bridge the gaps between scales? Prog Biophys Mol Biol 107(1):21–31

    Article  Google Scholar 

  • Railsback S, Grimm V (2011) Agent-based and individual-based modeling: a practical introduction. Princeton University Press, Princeton

    Google Scholar 

  • Ramis-Conde I, Chaplain M, Anderson A, Drasdo D (2009) Multi-scale modelling of cancer cell intravasation: the role of cadherins in metastasis. Phys Biol 6:016,008

    Article  Google Scholar 

  • Ribba B, Colin T, Schnell S (2006) A multiscale mathematical model of cancer, and its use in analyzing irradiation therapies. Theor Biol Med Model 3(1):7

    Article  Google Scholar 

  • Ribeiro A, Dai X, Yli-Harja O (2009) Variability of the distribution of differentiation pathway choices regulated by a multipotent delayed stochastic switch. J Theor Biol 260(1):66–76

    Article  Google Scholar 

  • Schnell S, Grima R, Maini P (2007) Multiscale modeling in biology new insights into cancer illustrate how mathematical tools are enhancing the understanding of life from the smallest scale to the grandest. Am Sci 95:134–42

    Google Scholar 

  • Spencer S, Gerety R, Pienta K, Forrest S (2006) Modeling somatic evolution in tumorigenesis. PLOS Comput Biol 2(8):e108

    Article  Google Scholar 

  • Treuil JP, Drogoul A, Zucker JD (2008) Modélisation et simulation à base d’agents: exemples commentés, outils informatiques et questions théoriques. Dunod

  • Turing A (1952) The chemical basis of morphogenesis. Proc R Soc B 237(641):37–72

    Google Scholar 

  • Van Kampen N (1992) Stochastic processes in physics and chemistry. Elsevier, North Holland

    Google Scholar 

  • Wikenros C, Sand H, Wabakken P, Liberg O, Pedersen HC (2009) Wolf predation on moose and roe deer: chase distances and outcome of encounters. Acta Theriologica 54(3):207–218

    Article  Google Scholar 

  • Wilkinson DJ (2009) Stochastic modelling for quantitative description of heterogeneous biological systems. Nat Rev Genet 10(2):122–133

    Article  Google Scholar 

  • Zhang L, Wang Z, Sagotsky J, Deisboeck T (2009) Multiscale agent-based cancer modeling. J Math Biol 58(4):545–559

    Article  Google Scholar 

Download references

Acknowledgements

I would like to thank the organisers of the SFBT (Francophone Society for Theoretical Biology) for the opportunity to present an early version of this work. I also thank colleagues from the Inria Dracula Team for insightful discussions, and C. Knibbe for code development used in Fig. 6.

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Correspondence to Samuel Bernard.

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Bernard, S. How to Build a Multiscale Model in Biology. Acta Biotheor 61, 291–303 (2013). https://doi.org/10.1007/s10441-013-9199-z

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