Evidence map›Paper›PMID 30715927›Full record

ReviewJCO clinical cancer informatics2019

A Review of Cell-Based Computational Modeling in Cancer Biology.

John Metzcar, Yafei Wang, Randy Heiland, Paul Macklin

Open access · greenAbstract readReview
In one paragraph

Review in JCO clinical cancer informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 207 papers.

0numbers the graph read from it
0cells of the map it votes in
207citing papers in PubMed
23.3field-weighted citation impact, top 1% of its field
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

207 citing papers in PubMed, 413 citations in OpenAlex.

  1. Article
  2. Virtual twins and the future of human developmental biology.NPJ systems biology and applications · 2026
    Review
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  20. The future of mathematical oncology in the age of AI.NPJ systems biology and applications · 2026
    Review

147 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

4 authors at 1 institution in 1 country.

John MetzcarIndiana University, Bloomington, IN.
Yafei WangIndiana University, Bloomington, IN.
Randy HeilandIndiana University, Bloomington, IN.
Paul MacklinIndiana University, Bloomington, IN.
Indiana University Bloomington · US

Funding

TRANS-NETWORK PROJECTSU54CA143907 · NCI · UNIVERSITY OF SOUTHERN CALIFORNIA · PI HILLIS, WILLIAM DANIEL · 2009 to 2013
$15.2M
Multiscale systems biology modeling to exploit tumor-stromal metabolic crosstalk in colorectal cancerU01CA232137 · NCI · UNIVERSITY OF SOUTHERN CALIFORNIA · PI FINLEY, STACEY DELERIA, MACKLIN, PAUL T · 2018 to 2022
$2.8M
(PQB6)An Integrative Computational and Bioengineered Tissue Model of MetastasisR01CA180149 · NCI · UNIVERSITY OF SOUTHERN CALIFORNIA · PI AGUS, DAVID B, ATALA, ANTHONY · 2013 to 2016
$2.3M
NCI NIH HHS R01 CA180149NCI NIH HHS U01 CA232137NCI NIH HHS U54 CA143907
6 · The paper itself

Abstract

Cancer biology involves complex, dynamic interactions between cancer cells and their tissue microenvironments. Single-cell effects are critical drivers of clinical progression. Chemical and mechanical communication between tumor and stromal cells can co-opt normal physiologic processes to promote growth and invasion. Cancer cell heterogeneity increases cancer's ability to test strategies to adapt to microenvironmental stresses. Hypoxia and treatment can select for cancer stem cells and drive invasion and resistance. Cell-based computational models (also known as discrete models, agent-based models, or individual-based models) simulate individual cells as they interact in virtual tissues, which allows us to explore how single-cell behaviors lead to the dynamics we observe and work to control in cancer systems. In this review, we introduce the broad range of techniques available for cell-based computational modeling. The approaches can range from highly detailed models of just a few cells and their morphologies to millions of simpler cells in three-dimensional tissues. Modeling individual cells allows us to directly translate biologic observations into simulation rules. In many cases, individual cell agents include molecular-scale models. Most models also simulate the transport of oxygen, drugs, and growth factors, which allow us to link cancer development to microenvironmental conditions. We illustrate these methods with examples drawn from cancer hypoxia, angiogenesis, invasion, stem cells, and immunosurveillance. An ecosystem of interoperable cell-based simulation tools is emerging at a time when cloud computing resources make software easier to access and supercomputing resources make large-scale simulation studies possible. As the field develops, we anticipate that high-throughput simulation studies will allow us to rapidly explore the space of biologic possibilities, prescreen new therapeutic strategies, and even re-engineer tumor and stromal cells to bring cancer systems under control.

Indexed as

Models, BiologicalAnimalsCell BiologyCell HypoxiaComputer SimulationEpithelial-Mesenchymal TransitionHumansNeoplasm InvasivenessNeoplasmsNeoplastic Stem CellsSystems BiologyTumor Microenvironment

Identifiers

PMID30715927
PMCPMC6584763
OpenAlexW2911948847

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.