Evolutionary game theory in an agent-based brain tumor model: Exploring the ‘Genotype–Phenotype’ link

Mansury, Yuri; Diggory, Mark; Deisboeck, Thomas S.

doi:10.1016/j.jtbi.2005.05.027

Cited by 92 publications

(90 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…More recent work in this direction has investigated the impact of the micro-environment on tumour growth (Ferreira et al, 2002;Anderson, 2005;Anderson et al, 2006). Other studies have focused on how the cell processes information from the environment and how this influences tumour growth dynamics (Gerlee and Anderson, 2007a;Mansury et al, 2006Mansury et al, , 2002.…”

Section: Previous Workmentioning

confidence: 99%

A hybrid cellular automaton model of clonal evolution in cancer: The emergence of the glycolytic phenotype

Gerlee

Anderson

2008

Journal of Theoretical Biology

123

113

View full text Add to dashboard Cite

We present a cellular automaton model of clonal evolution in cancer aimed at investigating the emergence of the glycolytic phenotype. In the model each cell is equipped with a micro-environment response network that determines the behaviour or phenotype of the cell based on the local environment. The response network is modelled using a feed-forward neural network, which is subject to mutations when the cells divide. This implies that cells might react differently to the environment and when space and nutrients are limited only the fittest cells will survive. With this model we have investigated the impact of the environment on the growth dynamics of the tumour. In particular we have analysed the influence of the tissue oxygen concentration and extra-cellular matrix density on the dynamics of the model. We found that the environment influences both the growth and evolutionary dynamics of the tumour. For low oxygen concentration we observe tumours with a fingered morphology, while increasing the matrix density gives rise to more compact tumours with wider fingers. The distribution of phenotypes in the tumour is also affected, and we observe that the glycolytic phenotype is most likely to emerge in a poorly oxygenated tissue with a high matrix density. Our results suggest that it is the combined effect of the oxygen concentration and matrix density that creates an environment where the glycolytic phenotype has a growth advantage and consequently is most likely to appear.

show abstract

Section: Previous Workmentioning

confidence: 99%

A hybrid cellular automaton model of clonal evolution in cancer: The emergence of the glycolytic phenotype

Gerlee

Anderson

2008

Journal of Theoretical Biology

123

113

View full text Add to dashboard Cite

show abstract

“…Indeed, a C-ImmSim model captures important elements of EBV infection [46]. Other studies using agent-based simulation to address the course of specific diseases include [47][48][49][50][51][52][53][54].…”

Section: It Is Very Much An Open Question As To What Sort Of Mathematmentioning

confidence: 99%

A virtual look at Epstein–Barr virus infection: Simulation mechanism

Shapiro

Duca

Lee

et al. 2008

Journal of Theoretical Biology

View full text Add to dashboard Cite

Epstein-Barr virus (EBV) is an important human pathogen that establishes a lifelong persistent infection and for which no precise animal model exists. In this paper we describe in detail an agentbased model and computer simulation of EBV infection. Agents representing EBV and sets of B and T lymphocytes move and interact on a three-dimensional grid approximating Waldeyer's ring, together with abstract compartments for lymph and blood. The simulation allows us to explore the development and resolution of virtual infections in a manner not possible in actual human experiments. Specifically, we identify parameters capable of inducing clearance, persistent infection, or death.

show abstract

“…Hybrid models have been used to investigate not only tumour growth (Drasdo and Hohme, 2005;Jiang et al, 2005;Macklin et al, 2010;Mansury et al, 2006;Stott et al, 1999), but also tumour-induced angiogenesis (Anderson and Chaplain, 1998;Macklin et al, 2009;Perfahl et al, 2011;Shirinifard et al, 2009) and tumour morphology Anderson et al, 2006). By their nature, these models incorporate spatial variables that are associated with the tumour architecture.…”

Section: Discrete Modelling Techniquesmentioning

confidence: 99%

A multiscale road map of cancer spheroids – incorporating experimental and mathematical modelling to understand cancer progression

Loessner

Little

Pettet

et al. 2013

Journal of Cell Science

View full text Add to dashboard Cite

SummaryComputational models represent a highly suitable framework, not only for testing biological hypotheses and generating new ones but also for optimising experimental strategies. As one surveys the literature devoted to cancer modelling, it is obvious that immense progress has been made in applying simulation techniques to the study of cancer biology, although the full impact has yet to be realised. For example, there are excellent models to describe cancer incidence rates or factors for early disease detection, but these predictions are unable to explain the functional and molecular changes that are associated with tumour progression. In addition, it is crucial that interactions between mechanical effects, and intracellular and intercellular signalling are incorporated in order to understand cancer growth, its interaction with the extracellular microenvironment and invasion of secondary sites. There is a compelling need to tailor new, physiologically relevant in silico models that are specialised for particular types of cancer, such as ovarian cancer owing to its unique route of metastasis, which are capable of investigating anti-cancer therapies, and generating both qualitative and quantitative predictions. This Commentary will focus on how computational simulation approaches can advance our understanding of ovarian cancer progression and treatment, in particular, with the help of multicellular cancer spheroids, and thus, can inform biological hypothesis and experimental design.

show abstract

Evolutionary game theory in an agent-based brain tumor model: Exploring the ‘Genotype–Phenotype’ link

Cited by 92 publications

References 24 publications

A hybrid cellular automaton model of clonal evolution in cancer: The emergence of the glycolytic phenotype

A hybrid cellular automaton model of clonal evolution in cancer: The emergence of the glycolytic phenotype

A virtual look at Epstein–Barr virus infection: Simulation mechanism

A multiscale road map of cancer spheroids – incorporating experimental and mathematical modelling to understand cancer progression

Contact Info

Product

Resources

About