↓ Skip to main content

Cellular Potts Modeling of Tumor Growth, Tumor Invasion, and Tumor Evolution

Overview of attention for article published in Frontiers in oncology, January 2013
Altmetric Badge

About this Attention Score

  • Good Attention Score compared to outputs of the same age (72nd percentile)
  • High Attention Score compared to outputs of the same age and source (84th percentile)

Mentioned by

twitter
2 X users
wikipedia
1 Wikipedia page

Citations

dimensions_citation
156 Dimensions

Readers on

mendeley
207 Mendeley
citeulike
2 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Cellular Potts Modeling of Tumor Growth, Tumor Invasion, and Tumor Evolution
Published in
Frontiers in oncology, January 2013
DOI 10.3389/fonc.2013.00087
Pubmed ID
Authors

András Szabó, Roeland M. H. Merks

Abstract

Despite a growing wealth of available molecular data, the growth of tumors, invasion of tumors into healthy tissue, and response of tumors to therapies are still poorly understood. Although genetic mutations are in general the first step in the development of a cancer, for the mutated cell to persist in a tissue, it must compete against the other, healthy or diseased cells, for example by becoming more motile, adhesive, or multiplying faster. Thus, the cellular phenotype determines the success of a cancer cell in competition with its neighbors, irrespective of the genetic mutations or physiological alterations that gave rise to the altered phenotype. What phenotypes can make a cell "successful" in an environment of healthy and cancerous cells, and how? A widely used tool for getting more insight into that question is cell-based modeling. Cell-based models constitute a class of computational, agent-based models that mimic biophysical and molecular interactions between cells. One of the most widely used cell-based modeling formalisms is the cellular Potts model (CPM), a lattice-based, multi particle cell-based modeling approach. The CPM has become a popular and accessible method for modeling mechanisms of multicellular processes including cell sorting, gastrulation, or angiogenesis. The CPM accounts for biophysical cellular properties, including cell proliferation, cell motility, and cell adhesion, which play a key role in cancer. Multiscale models are constructed by extending the agents with intracellular processes including metabolism, growth, and signaling. Here we review the use of the CPM for modeling tumor growth, tumor invasion, and tumor progression. We argue that the accessibility and flexibility of the CPM, and its accurate, yet coarse-grained and computationally efficient representation of cell and tissue biophysics, make the CPM the method of choice for modeling cellular processes in tumor development.

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 207 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 7 3%
India 2 <1%
France 1 <1%
Belgium 1 <1%
Canada 1 <1%
Venezuela, Bolivarian Republic of 1 <1%
Romania 1 <1%
Unknown 193 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 59 29%
Researcher 40 19%
Student > Master 23 11%
Student > Bachelor 22 11%
Professor > Associate Professor 13 6%
Other 24 12%
Unknown 26 13%
Readers by discipline Count As %
Physics and Astronomy 39 19%
Agricultural and Biological Sciences 32 15%
Biochemistry, Genetics and Molecular Biology 20 10%
Mathematics 20 10%
Engineering 18 9%
Other 40 19%
Unknown 38 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 19 July 2019.
All research outputs
#7,853,486
of 25,576,801 outputs
Outputs from Frontiers in oncology
#2,757
of 22,703 outputs
Outputs of similar age
#78,417
of 289,927 outputs
Outputs of similar age from Frontiers in oncology
#46
of 328 outputs
Altmetric has tracked 25,576,801 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 22,703 research outputs from this source. They receive a mean Attention Score of 3.0. This one has done well, scoring higher than 87% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 289,927 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.
We're also able to compare this research output to 328 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 84% of its contemporaries.