Multiscale agent-based cancer modeling

Agent-based modeling (ABM) is an in silico technique that is being used in a variety of research areas such as in social sciences, economics and increasingly in biomedicine as an interdisciplinary tool to study the dynamics of complex systems. Here, we describe its applicability to integrative tumor biology research by introducing a multi-scale tumor modeling platform that understands brain cancer as a complex dynamic biosystem. We summarize significant findings of this work, and discuss both challenges and future directions for ABM in the field of cancer research.

[1]  R P Araujo,et al.  A mathematical model of combination therapy using the EGFR signaling network. , 2005, Bio Systems.

[2]  Alissa M. Weaver,et al.  Tumor Morphology and Phenotypic Evolution Driven by Selective Pressure from the Microenvironment , 2006, Cell.

[3]  J. Murray,et al.  A quantitative model for differential motility of gliomas in grey and white matter , 2000, Cell proliferation.

[4]  Jesse A. Engelberg,et al.  Agent-Based Simulations of In Vitro Multicellular Tumor Spheroid Growth , 2005 .

[5]  Mauro Ferrari,et al.  Mathematical modeling of cancer progression and response to chemotherapy , 2006, Expert review of anticancer therapy.

[6]  Paul Davidsson,et al.  Agent Based Social Simulation: A Computer Science View , 2002, J. Artif. Soc. Soc. Simul..

[7]  S. Coons,et al.  Dichotomy of astrocytoma migration and proliferation , 1996, International journal of cancer.

[8]  Paola Pisani,et al.  Genetic Pathways to Glioblastoma , 2004, Cancer Research.

[9]  Weizhong Dai,et al.  A Numerical Method for Optimizing Laser Power in the Irradiation of a 3-D Triple-Layered Cylindrical Skin Structure , 2005 .

[10]  Steven C Bankes,et al.  Agent-based modeling: A revolution? , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[11]  J. Murray,et al.  Virtual and real brain tumors: using mathematical modeling to quantify glioma growth and invasion , 2003, Journal of the Neurological Sciences.

[12]  A. Barabasi,et al.  Fractal Concepts in Surface Growth: Frontmatter , 1995 .

[13]  M Kiessling,et al.  Amplification of the epidermal‐growth‐factor‐receptor gene correlates with different growth behaviour in human glioblastoma , 2007, International journal of cancer.

[14]  Le Zhang,et al.  Simulating brain tumor heterogeneity with a multiscale agent-based model: Linking molecular signatures, phenotypes and expansion rate , 2006, Math. Comput. Model..

[15]  Eric Bonabeau,et al.  Agent-based modeling: Methods and techniques for simulating human systems , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[16]  C. Heldin,et al.  Effect of epidermal growth factor on membrane motility and cell locomotion in cultures of human clonal glioma cells , 1982, Journal of neuroscience research.

[17]  Jeffrey M. Hausdorff,et al.  Is walking a random walk? Evidence for long-range correlations in stride interval of human gait. , 1995, Journal of applied physiology.

[18]  Naila Moreira In pixels and in health: Computer modeling pushes the threshold of medical research , 2006 .

[19]  Thomas S. Deisboeck,et al.  Simulating the time series of a selected gene expression profile in an agent-based tumor model , 2003, nlin/0308002.

[20]  D. Kirschner,et al.  Modeling immunotherapy of the tumor – immune interaction , 1998, Journal of mathematical biology.

[21]  Thomas S Deisboeck,et al.  Emerging patterns in tumor systems: simulating the dynamics of multicellular clusters with an agent-based spatial agglomeration model. , 2002, Journal of theoretical biology.

[22]  Gernot Schaller,et al.  Multicellular tumor spheroid in an off-lattice Voronoi-Delaunay cell model. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[23]  P. Kleihues,et al.  Primary and secondary glioblastomas: from concept to clinical diagnosis. , 1999, Neuro-oncology.

[24]  Jose L. Segovia-Juarez,et al.  Identifying control mechanisms of granuloma formation during M. tuberculosis infection using an agent-based model. , 2004, Journal of theoretical biology.

[25]  P. Maini,et al.  A mathematical model of the effects of hypoxia on the cell-cycle of normal and cancer cells. , 2004, Journal of theoretical biology.

[26]  M Koslow,et al.  Pathways leading to glioblastoma multiforme: a molecular analysis of genetic alterations in 65 astrocytic tumors. , 1994, Journal of neurosurgery.

[27]  J. Murray,et al.  Virtual brain tumours (gliomas) enhance the reality of medical imaging and highlight inadequacies of current therapy , 2002, British Journal of Cancer.

[28]  Weizhong Dai,et al.  A Numerical Method for Obtaining an Optimal Temperature Distribution in a 3-D Triple-Layered Cylindrical Skin Structure Embedded with a Blood Vessel , 2006 .

[29]  M. Chaplain,et al.  Continuous and discrete mathematical models of tumor-induced angiogenesis , 1998, Bulletin of mathematical biology.

[30]  S Torquato,et al.  Cellular automaton of idealized brain tumor growth dynamics. , 2000, Bio Systems.

[31]  R. Bjerkvig,et al.  Effects of growth factors on a human glioma cell line during invasion into rat brain aggregates in culture , 2004, Acta Neuropathologica.

[32]  S. Hubbard,et al.  Evolution of searching and life history characteristics in individual-based models of host-parasitoid-microbe associations. , 2005, Journal of theoretical biology.

[33]  Heather J. Ruskin,et al.  An Agent-Based Approach to Immune Modelling: Priming Individual Response , 2008 .

[34]  O D Laerum,et al.  Effect of epidermal growth factor on glioma cell growth, migration, and invasion in vitro. , 1990, Cancer research.

[35]  M. Chaplain,et al.  Two-dimensional models of tumour angiogenesis and anti-angiogenesis strategies. , 1997, IMA journal of mathematics applied in medicine and biology.

[36]  Stephanie Forrest,et al.  Modeling Somatic Evolution in Tumorigenesis , 2006, PLoS Comput. Biol..

[37]  Thomas S Deisboeck,et al.  The effects of EGF-receptor density on multiscale tumor growth patterns. , 2005, Journal of theoretical biology.

[38]  J. Murray,et al.  Quantifying Efficacy of Chemotherapy of Brain Tumors with Homogeneous and Heterogeneous Drug Delivery , 2002, Acta biotheoretica.

[39]  K. Painter,et al.  A continuum approach to modelling cell-cell adhesion. , 2006, Journal of theoretical biology.

[40]  Mike Holcombe,et al.  Individual cell-based simulation of 3D multicellular spheroid self-assembly , 2006 .

[41]  M. Chaplain,et al.  Mathematical modelling of tumour invasion and metastasis , 2000 .

[42]  G An,et al.  Agent-based computer simulation and sirs: building a bridge between basic science and clinical trials. , 2001, Shock.

[43]  T. Deisboeck,et al.  Development of a three-dimensional multiscale agent-based tumor model: simulating gene-protein interaction profiles, cell phenotypes and multicellular patterns in brain cancer. , 2006, Journal of theoretical biology.

[44]  C. Peng,et al.  Fractal landscapes and molecular evolution: modeling the myosin heavy chain gene family. , 1993, Biophysical journal.

[45]  C. Peng,et al.  Mosaic organization of DNA nucleotides. , 1994, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[46]  H. Stanley,et al.  Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. , 1995, Chaos.

[47]  A L Goldberger,et al.  Correlation approach to identify coding regions in DNA sequences. , 1994, Biophysical journal.

[48]  T. Skalak,et al.  The FASEB Journal express article 10.1096/fj.03-0933fje. Published online February 6, 2004. Multicellular simulation predicts microvascular patterning and in silico tissue assembly , 2022 .

[49]  A. Barabasi,et al.  Fractal concepts in surface growth , 1995 .

[50]  P. Maini,et al.  Modelling aspects of cancer dynamics: a review , 2006, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[51]  G. An In silico experiments of existing and hypothetical cytokine-directed clinical trials using agent-based modeling* , 2004, Critical care medicine.

[52]  M. Chaplain,et al.  Mathematical modelling of cancer cell invasion of tissue , 2005, Math. Comput. Model..

[53]  Thomas S Deisboeck,et al.  Simulating the impact of a molecular 'decision-process' on cellular phenotype and multicellular patterns in brain tumors. , 2004, Journal of theoretical biology.

[54]  Thomas S. Deisboeck,et al.  Simulating ‘structure–function’ patterns of malignant brain tumors , 2004 .

[55]  Thomas S Deisboeck,et al.  The impact of "search precision" in an agent-based tumor model. , 2003, Journal of theoretical biology.