The Influence of Nutrients Diffusion on a Metabolism-driven Model of a Multi-cellular System

• Autorzy: Maspero Davide , Damiani Chiara , Antoniotti Marco , Graudenzi Alex , Di Filippo Marzia , Vanoni Marco , Caravagna Giulio , Colombo Riccardo , Ramazzotti Daniele , Pescini Dario

Identyfikatory

DOI

10.3233/FI-2020-1883

• Języki publikacji: EN

Abstrakty

EN

The metabolic processes related to the synthesis of the molecules needed for a new round of cell division underlie the complex behaviour of cell populations in multi-cellular systems, such as tissues and organs, whereas their deregulation can lead to pathological states, such as cancer. Even within genetically homogeneous populations, complex dynamics, such as population oscillations or the emergence of specific metabolic and/or proliferative patterns, may arise, and this aspect is highly amplified in systems characterized by extreme heterogeneity. To investigate the conditions and mechanisms that link metabolic processes to cell population dynamics, we here employ a previously introduced multi-scale model of multi-cellular system, named FBCA (Flux Balance Analysis with Cellular Automata), which couples biomass accumulation, simulated via Flux Balance Analysis of a metabolic network, with the simulation of population and spatial dynamics via Cellular Potts Models. In this work, we investigate the influence that different modes of nutrients diffusion within the system may have on the emerging behaviour of cell populations. In our model, metabolic communication among cells is allowed by letting secreted metabolites to diffuse over the lattice, in addition to diffusion of nutrients from given sources. The inclusion of the diffusion processes in the model proved its effectiveness in characterizing plausible biological scenarios.

Słowa kluczowe

EN

cancer development; cellular potts model; diffusion; flux balance analysis; multi-scale modeling

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2020
• Tom: Vol. 171, nr 1-4
• Strony: 279--295
• Opis fizyczny: Bibliogr. 30 poz., rys., wykr.

Twórcy

autor

Maspero Davide

• Department of Informatics Systems and Communication, Univ. of Milano-Bicocca, Milan, Italy
• Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy

autor

Damiani Chiara

• Department of Informatics Systems and Communication, Univ. of Milano-Bicocca, Milan, Italy
• SYSBIO Centre of Systems Biology, Univ. of Milano-Bicocca, Milan, Italy

autor

Antoniotti Marco

• Department of Informatics Systems and Communication, Univ. of Milano-Bicocca, Milan, Italy
• NeuroMI Milan Center for Neuroscience, Univ. of Milano-Bicocca, Milan, Italy

autor

Graudenzi Alex

• Department of Informatics Systems and Communication, Univ. of Milano-Bicocca, Milan, Italy
• Institute of Molecular Bioimaging and Physiology, Italian National Research Council, Milan, Italy

autor

Di Filippo Marzia

• Department of Biotechnology and Biosciences, Univ. Milano-Bicocca, Milan, Italy

autor

Vanoni Marco

• Department of Biotechnology and Biosciences, Univ. Milano-Bicocca, Milan, Italy
• SYSBIO Centre of Systems Biology, Univ. of Milano-Bicocca, Milan, Italy

autor

Caravagna Giulio

• Data Scientist, Institute of Cancer Research, ICR, London, UK

autor

Colombo Riccardo

• Department of Biomedical and Clinical Sciences "L. Sacco", Univ. of Milan, Milan, Italy

autor

Ramazzotti Daniele

• Department of Pathology, Stanford University, Stanford, CA 94305, USA

autor

Pescini Dario

• Department of Statistics and Quantitative Methods, Univ. of Milano-Bicocca, Milan, Italy

Bibliografia

• [1] Hanahan D, Weinberg R. Hallmarks of Cancer: The Next Generation. Cell, 2011. 144:646-674. doi: 10.1016/j.cell.2011.02.013.
• [2] Ward P, Thompson C. Metabolic Reprogramming: A Cancer Hallmark Even Warburg Did Not Anticipate. Cancer Cell, 2012. 21:297-308. doi:10.1016/j.ccr.2012.02.014.
• [3] Orth JD, Thiele I, Palsson BØ. What is flux balance analysis? Nature Biotechnology, 2010. 28:245. doi:10.1038/nbt.1614.
• [4] Cazzaniga P, Damiani C, Besozzi D, Colombo R, Nobile M, Gaglio D, Pescini D, Molinari S, Mauri G, Alberghina L, Vanoni M. Computational Strategies for a System-Level Understanding of Metabolism. Metabolites, 2014. 4:1034-1087. doi:10.3390/metabo4041034.
• [5] Burrell RA, McGranahan N, Bartek J, Swanton C. The causes and consequences of genetic heterogeneity in cancer evolution. Nature, 2013. 501:nature12625. doi:10.1038/nature12625.
• [6] Marusyk A, Almendro V, Polyak K. Intra-tumour heterogeneity: a looking glass for cancer? Nature Reviews Cancer, 2012. 12:323. doi:10.1038/nrc3261.
• [7] McGranahan N, Swanton C. Biological and therapeutic impact of intratumor heterogeneity in cancer evolution. Cancer cell, 2015. 27(1):15-26. doi:10.1016/j.ccell.2014.12.001.
• [8] Caravagna G, Graudenzi A, Ramazzotti D, Sanz-Pamplona R, Sano LD, Mauri G, Moreno V, Antoniotti M, Mishra B. Algorithmic methods to infer the evolutionary trajectories in cancer progression. Proceedings of the National Academy of Sciences, 2016. 113:E4025-E4034. doi:10.1073/pnas.1520213113.
• [9] Lipinski KA, Barber LJ, Davies MN, Ashenden M, Sottoriva A, Gerlinger M. Cancer Evolution and the Limits of Predictability in Precision Cancer Medicine. Trends in Cancer, 2016. 2:49-63. doi:10.1016/j.trecan.2015.11.003.
• [10] Navin NE. The first five years of single-cell cancer genomics and beyond. Genome Research, 2015. 25:1499-1507. doi:10.1101/gr.191098.115.
• [11] Gawad C, Koh W, Quake SR. Single-cell genome sequencing: current state of the science. Nature Reviews Genetics, 2016. 17:175-188. doi:10.1038/nrg.2015.16.
• [12] Cristini V, Lowengrub J. Multiscale modeling of cancer: an integrated experimental and mathematical modeling approach. Cambridge University Press, 2010. doi:10.1017/cbo9780511781452.
• [13] Deisboeck TS, Stamatakos GS. Multiscale cancer modeling. CRC Press, 2010. doi:10.1201/b10407.
• [14] Matteis GD, Graudenzi A, Antoniotti M. A review of spatial computational models for multi-cellular systems, with regard to intestinal crypts and colorectal cancer development. Journal of Mathematical Biology, 2013. 66:1409-1462. doi:10.1007/s00285-012-0539-4.
• [15] Graudenzi A, Caravagna G, Matteis GD, Antoniotti M. Investigating the Relation between Stochastic Differentiation, Homeostasis and Clonal Expansion in Intestinal Crypts via Multiscale Modeling. PLoS ONE, 2014. 9:e97272. doi:10.1371/journal.pone.0097272.
• [16] Rubinacci S, Graudenzi A, Caravagna G, Mauri G, Osborne J, Pitt-Francis J, Antoniotti M. CoGNaC: A Chaste Plugin for the Multiscale Simulation of Gene Regulatory Networks Driving the Spatial Dynamics of Tissues and Cancer. Cancer Informatics, 2015. 14:53-65. doi:10.4137/cin.s19965.
• [17] Mina P, Bernardo Md, Savery NJ, Tsaneva-Atanasova K. Modelling emergence of oscillations in communicating bacteria: a structured approach from one to many cells. Journal of the Royal Society, Interface, 2013. 10:20120612. doi:10.1098/rsif.2012.0612.
• [18] Graudenzi A, Maspero D, Damiani C. Modeling spatio-temporal dynamics of metabolic networks with cellular automata and constraint-based methods. In: Giancarlo Mauri ADKNLM Samira El Yacoubi (ed.), Cellular Automata. ACRI 2018. Lecture Notes in Computer Science, volume 11115. Springer, Cham, 2018 pp. 16-29. doi:10.1007/978-3-319-99813-8_2.
• [19] Graner F, Glazier JA. Simulation of biological cell sorting using a two-dimensional extended Potts model. Physical Review Letters, 1992. 69(13):2013-2016. doi:10.1103/physrevlett.69.2013.
• [20] Marée AF, Grieneisen VA, Hogeweg P. The Cellular Potts Model and biophysical properties of cells, tissues and morphogenesis. In: Single-cell-based models in biology and medicine, pp. 107-136. Springer, 2007. doi:10.1007/978-3-7643-8123-3_5.
• [21] Scianna M, Preziosi L. Multiscale developments of the cellular Potts model. Multiscale Modeling & Simulation, 2012. 10(2):342-382. doi:10.1137/100812951.
• [22] Szabó A, Merks RM. Cellular Potts Modeling of Tumor Growth, Tumor Invasion, and Tumor Evolution. Frontiers in oncology, 2013. 3:87. doi:10.3389/fonc.2013.00087.
• [23] Maspero D, Graudenzi A, Singh S, Pescini D, Mauri G, Antoniotti M, Damiani C. Synchronization effects in a metabolism-driven model of multi-cellular system. volume 900. 2019 pp. 115-126. doi:10.1007/978-3-030-21733-4_9.
• [24] Scianna M, Preziosi L. Cellular Potts Models: Multiscale Extensions and Biological Applications. CRC Press, 2013. doi:10.1201/b14075.
• [25] Filippo MD, Colombo R, Damiani C, Pescini D, Gaglio D, Vanoni M, Alberghina L, Mauri G. Zoomingin on cancer metabolic rewiring with tissue specific constraint-based models. Computational Biology and Chemistry, 2016. 62:60-69. doi:10.1016/j.compbiolchem.2016.03.002.
• [26] Steinberg MS. On the mechanism of tissue reconstruction by dissociated cells, I. Population kinetics, differential adhesiveness, and the absence of directed migration. Proceedings of the National Academy of Sciences, 1962. 48(9):1577-1582. doi:10.1073/pnas.48.9.1577.
• [27] Dan D, Mueller C, Chen K, Glazier JA. Solving the advection-diffusion equations in biological contexts using the cellular Potts model. Physical Review E, 2005. 72(4):041909. doi:10.1103/physreve.72.041909.
• [28] Damiani C, Di Filippo M, Pescini D, Maspero D, Colombo R, Mauri G. popFBA: tackling intra-tumour heterogeneity with Flux Balance Analysis. Bioinformatics, 2017. 33(14):i311-i318. doi:10.1093/bioinformatics/btx251.
• [29] Graudenzi A, Maspero D, Di Filippo M, Gnugnoli M, Isella C, Mauri G, Medico E, Antoniotti M, Damiani C. Integration of transcriptomic data and metabolic networks in cancer samples reveals highly significant prognostic power. Journal of Biomedical Informatics, 2018. 87:37-149. doi:10.1016/j.jbi.2018.09.010.
• [30] Schellenberger J, Que R, Fleming RMT, Thiele I, Orth JD, Feist AM, Zielinski DC, Bordbar A, Lewis NE, Rahmanian S, Kang J, Hyduke DR, Palsson BØ. Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0. Nature protocols, 2011. 6(9):1290. doi:10.1038/nprot.2011.308.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).