↓ Skip to main content

Mathematical models of cancer metabolism

Overview of attention for article published in Cancer & Metabolism, December 2015
Altmetric Badge

About this Attention Score

  • Average Attention Score compared to outputs of the same age

Mentioned by

twitter
1 X user
facebook
1 Facebook page

Citations

dimensions_citation
28 Dimensions

Readers on

mendeley
96 Mendeley
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
Mathematical models of cancer metabolism
Published in
Cancer & Metabolism, December 2015
DOI 10.1186/s40170-015-0140-6
Pubmed ID
Authors

Elke Katrin Markert, Alexei Vazquez

Abstract

Metabolism is essential for life, and its alteration is implicated in multiple human diseases. The transformation from a normal to a cancerous cell requires metabolic changes to fuel the high metabolic demands of cancer cells, including but not limited to cell proliferation and cell migration. In recent years, there have been a number of new discoveries connecting known aberrations in oncogenic and tumour suppressor pathways with metabolic alterations required to sustain cell proliferation and migration. However, an understanding of the selective advantage of these metabolic alterations is still lacking. Here, we review the literature on mathematical models of metabolism, with an emphasis on their contribution to the identification of the selective advantage of metabolic phenotypes that seem otherwise wasteful or accidental. We will show how the molecular hallmarks of cancer can be related to cell proliferation and tissue remodelling, the two major physiological requirements for the development of a multicellular structure. We will cover different areas such as genome-wide gene expression analysis, flux balance models, kinetic models, reaction diffusion models and models of the tumour microenvironment. We will also highlight current challenges and how their resolution will help to achieve a better understanding of cancer metabolism and the metabolic vulnerabilities of cancers.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user 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 96 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Malaysia 1 1%
Spain 1 1%
United States 1 1%
Brazil 1 1%
Unknown 92 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 32 33%
Researcher 18 19%
Student > Master 8 8%
Student > Doctoral Student 7 7%
Student > Bachelor 6 6%
Other 11 11%
Unknown 14 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 26 27%
Biochemistry, Genetics and Molecular Biology 18 19%
Mathematics 6 6%
Engineering 6 6%
Medicine and Dentistry 5 5%
Other 16 17%
Unknown 19 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 23 August 2018.
All research outputs
#14,830,609
of 22,836,570 outputs
Outputs from Cancer & Metabolism
#131
of 204 outputs
Outputs of similar age
#217,018
of 390,618 outputs
Outputs of similar age from Cancer & Metabolism
#2
of 3 outputs
Altmetric has tracked 22,836,570 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 204 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.0. This one is in the 29th percentile – i.e., 29% of its peers scored the same or lower than it.
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 390,618 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 3 others from the same source and published within six weeks on either side of this one.