Abstract
Increasing experimental evidence suggests that the behaviour of multi-cellular systems, such as tissues and organs, might be largely driven by the complex interplay occurring among metabolic networks. Computational approaches are required to unravel this complexity. However, they currently deal with either the simulation of the spatial dynamics of cell populations or with the simulation of metabolism of individual cells. In order to integrate the modeling of these two key biological processes, we here introduce FBCA (Flux Balance Cellular Automata) a new multi-scale modeling framework that combines a cellular automaton representation of the (higher-level) spatial/morphological dynamics of multi-cellular systems, i.e., the Cellular Potts Model, with a model of the (lower-level) metabolic activity of individual cells, as modeled via Flux Balance Analysis. The representation via cellular automata allows to identify and analyze complex emergent properties and patterns of real-world multi-cellular systems, in a variety of distinct experimental settings. We here present preliminary tests on a simplified model of intestinal crypt, in which cell populations with distinct metabolic properties compete for space and nutrients. The results may allow to cast a new light on the mechanisms linking metabolic properties to clonal dynamics in tissues.
A. Graudenzi, D. Maspero and C. Damiani—Equal contributors.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bjerknes, M.: Expansion of mutant stem cell populations in the human colon. J. Theor. Biol. 178(4), 381–385 (1996)
Bruce, M.B., Fields, J.Z., Bonham-Carter, O., Runquist, O.A.: Computer modeling implicates stem cell overproduction in colon cancer initiation. Cancer Res. 61(23), 8408–8411 (2001)
Buske, P., Galle, J., Barker, N., Aust, G., Clevers, H., Loeffler, M.: A comprehensive model of the spatio-temporal stem cell and tissue organisation in the intestinal crypt. PLoS Comput. Biol. 7(1), e1001045 (2011)
Cazzaniga, P., et al.: Computational strategies for a system-level understanding of metabolism. Metabolites 4(4), 1034–1087 (2014)
Damiani, C., et al.: A metabolic core model elucidates how enhanced utilization of glucose and glutamine, with enhanced glutamine-dependent lactate production, promotes cancer cell growth: the WarburQ effect. PLOS Comput. Biol. 13(9), e1005758 (2017)
Damiani, C., Di Filippo, M., Pescini, D., Maspero, D., Colombo, R., Mauri, G.: popFBA: tackling intratumour heterogeneity with flux balance analysis. Bioinformatics 33(14), i311–i318 (2017)
Damiani, C., Kauffman, S.A., Serra, R., Villani, M., Colacci, A.: Information transfer among coupled random boolean networks. In: Bandini, S., Manzoni, S., Umeo, H., Vizzari, G. (eds.) ACRI 2010. LNCS, vol. 6350, pp. 1–11. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15979-4_1
Chiara, D., et al.: Integration of single-cell RNA-seq data into metabolic models to characterize tumour cell populations. bioRxiv, 256644 (2018)
De Matteis, G., Graudenzi, A., Antoniotti, M.: A review of spatial computational models for multi-cellular systems, with regard to intestinal crypts and colorectal cancer development. J. Math. Biol. 66(7), 1409–1462 (2013)
Di Filippo, M., et al.: Zooming-in on cancer metabolic rewiring with tissue specific constraint-based models. Comput. Biol. Chem. 62, 60–69 (2016)
Graner, F., Glazier, J.A.: Simulation of biological cell sorting using a two-dimensional extended Potts model. Phys. Rev. Lett. 69(13), 2013 (1992)
Graudenzi, A., Caravagna, G., De Matteis, G., Antoniotti, M.: Investigating the relation between stochastic differentiation, homeostasis and clonal expansion in intestinal crypts via multiscale modeling. PLoS One 9(5), e97272 (2014)
Hanahan, D., Weinberg, R.A.: Hallmarks of cancer: the next generation. Cell 144(5), 646–674 (2011)
Khandelwal, R.A., Olivier, B.G., Röling, W.F.M., Teusink, B., Bruggeman, F.J.: Community flux balance analysis for microbial consortia at balanced growth. PloS One 8(5), e64567 (2013)
Murray, P.J., Walter, A., Fletcher, A.G., Edwards, C.M., Tindall, M.J., Maini, P.K.: Comparing a discrete and continuum model of the intestinal crypt. Phys. Biol. 8(2), 026011 (2011)
Noble, D.: Modeling the heart-from genes to cells to the whole organ. Science 295(5560), 1678–1682 (2002)
Orth, J.D., Thiele, I., Palsson, B.Ø.: What is flux balance analysis? Nat. Biotech. 28(3), 245 (2010)
Pitt-Francis, J., et al.: Chaste: a test-driven approach to software development for biological modelling. Comput. Phys. Commun. 180(12), 2452–2471 (2009)
Serra, R., Villani, M., Damiani, C., Graudenzi, A., Colacci, A.: The diffusion of perturbations in a model of coupled random boolean networks. In: Umeo, H., Morishita, S., Nishinari, K., Komatsuzaki, T., Bandini, S. (eds.) ACRI 2008. LNCS, vol. 5191, pp. 315–322. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-79992-4_40
Rubinacci, S., et al.: CoGNaC: a chaste plugin for the multiscale simulation of gene regulatory networks driving the spatial dynamics of tissues and cancer. Cancer Inform. 14, 53–65 (2015). CIN–S19965
Schellenberger, J., et al.: Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2. 0. Nat. Protoc. 6(9), 1290–1307 (2011)
Scianna, M., Preziosi, L.: Cellular Potts Models: Multiscale Extensions and Biological Applications. CRC Press, Boca Raton (2013)
Shirinifard, A., Gens, J.S., Zaitlen, B.L., Popławski, N.J., Swat, M., Glazier, J.A.: 3D multi-cell simulation of tumor growth and angiogenesis. PloS One 4(10), e7190 (2009)
Steinberg, M.S.: On the mechanism of tissue reconstruction by dissociated cells. I. population kinetics, differential adhesiveness, and the absence of directed migration. Proc. Natl. Acad. Sci. 48(9), 1577–1582 (1962)
Van Hoek, M.J.A., Merks, R.M.H.: Emergence of microbial diversity due to cross-feeding interactions in a spatial model of gut microbial metabolism. BMC Syst. Biol. 11(1), 56 (2017)
Van Leeuwen, I.M.M., Byrne, H.M., Jensen, O.E., King, J.R.: Crypt dynamics and colorectal cancer: advances in mathematical modelling. Cell Prolif. 39(3), 157–181 (2006)
Walpole, J., Papin, J.A., Peirce, S.M.: Multiscale computational models of complex biological systems. Annu. Rev. Biomed. Eng. 15, 137–154 (2013)
Wong, S.Y., Chiam, K.-H., Lim, C.T., Matsudaira, P.: Computational model of cell positioning: directed and collective migration in the intestinal crypt epithelium. J. Roy. Soc. Interface 7(Suppl. 3), S351–S363 (2010)
Acknowledgments
The institutional financial support to SYSBIO - within the Italian Roadmap for ESFRI Research Infrastructures - is gratefully acknowledged. CD received funding from FLAG-ERA grant ITFoC.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Graudenzi, A., Maspero, D., Damiani, C. (2018). Modeling Spatio-Temporal Dynamics of Metabolic Networks with Cellular Automata and Constraint-Based Methods. In: Mauri, G., El Yacoubi, S., Dennunzio, A., Nishinari, K., Manzoni, L. (eds) Cellular Automata. ACRI 2018. Lecture Notes in Computer Science(), vol 11115. Springer, Cham. https://doi.org/10.1007/978-3-319-99813-8_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-99813-8_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-99812-1
Online ISBN: 978-3-319-99813-8
eBook Packages: Computer ScienceComputer Science (R0)