A history of the study of solid tumour growth: The contribution of mathematical modelling
By S. Mcelwain, Robyn P. Araujo. Bulletin of Mathematical Biology, 2004
791 citations recorded by the source corpus.
Subfields: Cell Biology, Modeling and Simulation, Oncology
View Paper: https://www.semanticscholar.org/paper/d83586a431b0ffce25b289ddcfc2a0755c5515b5
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Cell Biology
In Modeling and Simulation
- Alberto d'Onofrio
- Steven M. Wise
- Leonid Berezansky
- Hermann B. Frieboes
- Vittorio Cristini
- John S. Lowengrub
In Oncology
Citations in the corpus
- Global dynamics of Nicholson-type delay systems with applications (2011, background): “According to the recent experimental data [7,8] (see also [4]), the Gompertz model is not suitable for extrapolating the specific growth rate (or generation time) of the cells when the concentration is low and/or at the early stage of cell development.”
- Three-dimensional multispecies nonlinear tumor growth--I Model and numerical method. (2008, methodology): “These include elastic, poroelastic and viscoelastic models, e.g., Jones et al. (2000), Ambrosi and Mollica (2002), Lubarda and Hoger (2002), Araujo and McElwain (2004), Ben-Amar and Gorielly (2005), Fung (1990), Humphrey (2003), Roose et al. (2003), and McArthur and Please (2004).”
- Interactions Between the Immune System and Cancer: A Brief Review of Non-spatial Mathematical Models (2011, background): “The majority of these reviews focus on spatial models, which are described either by partial differential equations (PDEs) or cellular automata (e.g., Araujo and McElwain 2004; Roose et al. 2007; Chaplain 2008).”
- Mathematical Models of Avascular Tumor Growth (2007, neutral): “For example, the most recent review [21] discusses the general concepts of modeling from a historical perspective, rather than focusing on particular models.”
- Fractal growth of tumors and other cellular populations: Linking the mechanistic to the phenomenological modeling and vice versa (2009, positive): “A similar behavior was also predicted in [10], where an interesting individual based model was used, and in [11], where also experimental data are reported (see also [1]).”
- Tumor morphology and phenotypic evolution driven by selective pressure from the microenvironment. (2006, background): “Mathematical modeling is an ideal approach for teasing apart mechanisms of cancer invasion because it can simultaneously and quantitatively consider interactions between multiple factors (Araujo and McElwain, 2004; Chaplain, 1996).”