Evidence map›Paper›PMID 24793698›Full record

ReviewSeminars in cancer biology2015

Simulating cancer growth with multiscale agent-based modeling.

Zhihui Wang, Joseph D Butner, Romica Kerketta, Vittorio Cristini, Thomas S Deisboeck

Abstract readReview
In one paragraph

Review in Seminars in cancer biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 117 papers.

0numbers the graph read from it
0cells of the map it votes in
117citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

117 citing papers in PubMed.

  1. Article
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  3. Review
  4. Article
  5. Article
  6. 75 Years of Mathematical Oncology.bioRxiv : the preprint server for biology · 2026
    Article
  7. Article
  8. Enhancing randomized clinical trials with digital twins.NPJ systems biology and applications · 2025
    Review
  9. Review
  10. Review
  11. Review
  12. Article
  13. Article
  14. Review
  15. Review
  16. Article
  17. Article
  18. Article
  19. Article
  20. Review

57 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Zhihui WangDepartment of Pathology, University of New Mexico, Albuquerque, NM 87131, USA. Electronic address: zwang@salud.unm.edu.
Joseph D ButnerDepartment of Chemical Engineering and Center for Biomedical Engineering, University of New Mexico, Albuquerque, NM 87131, USA.
Romica KerkettaDepartment of Pathology, University of New Mexico, Albuquerque, NM 87131, USA.
Vittorio CristiniDepartment of Pathology, University of New Mexico, Albuquerque, NM 87131, USA; Department of Chemical Engineering and Center for Biomedical Engineering, University of New Mexico, Albuquerque, NM 87131, USA; Department of Mathematics, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
Thomas S DeisboeckThinkMotu LLC, Wellesley, MA 02481, USA(2). Electronic address: ts.deisboeck@thinkmotu.com.

Funding

TRANS-NETWORK PROJECTSU54CA143907 · NCI · UNIVERSITY OF SOUTHERN CALIFORNIA · PI NOLAN, GARRY P · 2009 to 2013
$15.2M
Texas Center for Cancer NanomedicineU54CA151668 · NCI · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI CRISTINI, VITTORIO, FERRARI, MAURO · 2010 to 2014
$12.4M
TRANS-NETWORK PROJECTSU54CA143837 · NCI · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI FERRARI, MAURO · 2009 to 2014
$10.8M
The Core of the Computational BiologyU54CA149196 · NCI · METHODIST HOSPITAL RESEARCH INSTITUTE · PI WONG, STEPHEN TC · 2010 to 2015
$10.6M
NCI NIH HHS 1U54CA143837NCI NIH HHS 1U54CA143907NCI NIH HHS 1U54CA149196NCI NIH HHS 1U54CA151668NCI NIH HHS U54 CA143837NCI NIH HHS U54 CA143907NCI NIH HHS U54 CA149196NCI NIH HHS U54 CA151668
6 · The paper itself

Abstract

There have been many techniques developed in recent years to in silico model a variety of cancer behaviors. Agent-based modeling is a specific discrete-based hybrid modeling approach that allows simulating the role of diversity in cell populations as well as within each individual cell; it has therefore become a powerful modeling method widely used by computational cancer researchers. Many aspects of tumor morphology including phenotype-changing mutations, the adaptation to microenvironment, the process of angiogenesis, the influence of extracellular matrix, reactions to chemotherapy or surgical intervention, the effects of oxygen and nutrient availability, and metastasis and invasion of healthy tissues have been incorporated and investigated in agent-based models. In this review, we introduce some of the most recent agent-based models that have provided insight into the understanding of cancer growth and invasion, spanning multiple biological scales in time and space, and we further describe several experimentally testable hypotheses generated by those models. We also discuss some of the current challenges of multiscale agent-based cancer models.

Indexed as

Models, BiologicalModels, TheoreticalTranslational Research, BiomedicalComputer SimulationHumansNeoplasmsDrug discoveryMathematical modelingSignaling pathwayTranslational researchTumor growth and invasion

Identifiers

PMID24793698
PMCPMC4216775

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.