SpheroidSim—Preliminary evaluation of a new computational tool to predict the influence of cell cycle time and phase fraction on spheroid growth

There is a relative paucity of research that integrates materials science and bioengineering with computational simulations to decipher the intricate processes promoting cancer progression. Therefore, a first‐generation computational model, SpheroidSim, was developed that includes a biological data set derived from a bioengineered spheroid model to obtain a quantitative description of cell kinetics.

[1]  Robert J Gillies,et al.  Defining Cancer Subpopulations by Adaptive Strategies Rather Than Molecular Properties Provides Novel Insights into Intratumoral Evolution. , 2017, Cancer research.

[2]  Dimos Poulikakos,et al.  A Nanoprinted Model of Interstitial Cancer Migration Reveals a Link between Cell Deformability and Proliferation. , 2016, ACS nano.

[3]  James A. Glazier,et al.  Filopodial-Tension Model of Convergent-Extension of Tissues , 2016, PLoS Comput. Biol..

[4]  Nick Jagiella,et al.  Inferring Growth Control Mechanisms in Growing Multi-cellular Spheroids of NSCLC Cells from Spatial-Temporal Image Data , 2016, PLoS Comput. Biol..

[5]  Dietmar W Hutmacher,et al.  Biomaterial science meets computational biology , 2015, Journal of Materials Science: Materials in Medicine.

[6]  M. Bouvet,et al.  Nanoparticle albumin-bound-paclitaxel: a limited improvement under the current therapeutic paradigm of pancreatic cancer , 2015, Expert opinion on pharmacotherapy.

[7]  Sreeurpa Ray,et al.  The Cell: A Molecular Approach , 1996 .

[8]  David Robert Grimes,et al.  A method for estimating the oxygen consumption rate in multicellular tumour spheroids , 2014, Journal of The Royal Society Interface.

[9]  Walter de Back,et al.  Morpheus: a user-friendly modeling environment for multiscale and multicellular systems biology , 2014, Bioinform..

[10]  M. Zaman The role of engineering approaches in analysing cancer invasion and metastasis , 2013, Nature Reviews Cancer.

[11]  Dietmar W Hutmacher,et al.  A multiscale road map of cancer spheroids – incorporating experimental and mathematical modelling to understand cancer progression , 2013, Journal of Cell Science.

[12]  Alexander G. Fletcher,et al.  Chaste: An Open Source C++ Library for Computational Physiology and Biology , 2013, PLoS Comput. Biol..

[13]  H M Byrne,et al.  Growth of confined cancer spheroids: a combined experimental and mathematical modelling approach. , 2013, Integrative biology : quantitative biosciences from nano to macro.

[14]  Anna V. Taubenberger,et al.  Phenotypic Characterization of Prostate Cancer LNCaP Cells Cultured within a Bioengineered Microenvironment , 2012, PloS one.

[15]  Georgios S. Stamatakos,et al.  Exploiting Clinical Trial Data Drastically Narrows the Window of Possible Solutions to the Problem of Clinical Adaptation of a Multiscale Cancer Model , 2011, PloS one.

[16]  William C Hines,et al.  Why don't we get more cancer? A proposed role of the microenvironment in restraining cancer progression , 2011, Nature Medicine.

[17]  Dietmar W. Hutmacher,et al.  Bioengineered 3D platform to explore cell-ECM interactions and drug resistance of epithelial ovarian cancer cells. , 2010, Biomaterials.

[18]  Stefan Hoehme,et al.  A cell-based simulation software for multi-cellular systems , 2010, Bioinform..

[19]  L. Kunz-Schughart,et al.  Multicellular tumor spheroids: an underestimated tool is catching up again. , 2010, Journal of biotechnology.

[20]  H. Byrne Dissecting cancer through mathematics: from the cell to the animal model , 2010, Nature Reviews Cancer.

[21]  Mark W. Tibbitt,et al.  Hydrogels as extracellular matrix mimics for 3D cell culture. , 2009, Biotechnology and bioengineering.

[22]  V. Quaranta,et al.  Integrative mathematical oncology , 2008, Nature Reviews Cancer.

[23]  S. Jonathan Chapman,et al.  Mathematical Models of Avascular Tumor Growth , 2007, SIAM Rev..

[24]  Juergen Friedrich,et al.  Experimental anti-tumor therapy in 3-D: Spheroids – old hat or new challenge? , 2007, International journal of radiation biology.

[25]  L. Griffith,et al.  Capturing complex 3D tissue physiology in vitro , 2006, Nature Reviews Molecular Cell Biology.

[26]  Jayanta Debnath,et al.  Modelling glandular epithelial cancers in three-dimensional cultures , 2005, Nature Reviews Cancer.

[27]  R. Agarwal,et al.  Ovarian cancer: strategies for overcoming resistance to chemotherapy , 2003, Nature Reviews Cancer.

[28]  Graeme Wake,et al.  A mathematical model for analysis of the cell cycle in cell lines derived from human tumors , 2003, Journal of mathematical biology.

[29]  S. Zavgorodni,et al.  Growth of a virtual tumour using probabilistic methods of cell generation , 2002, Australasian Physics & Engineering Sciences in Medicine.

[30]  P Ubezio,et al.  Cell cycle effects of gemcitabine , 2001, International journal of cancer.

[31]  J. King,et al.  Mathematical modelling of avascular-tumour growth. , 1997, IMA journal of mathematics applied in medicine and biology.

[32]  R. Kreienberg,et al.  Morphological, immunohistochemical and biochemical characterization of 6 newly established human ovarian carcinoma cell lines , 1992, International journal of cancer.

[33]  P Ubezio,et al.  Cell cycle simulation for flow cytometry. , 1990, Computer methods and programs in biomedicine.

[34]  Abbas Shirinifard,et al.  Multi-scale modeling of tissues using CompuCell3D. , 2012, Methods in cell biology.