Evidence map›Paper›PMID 41099842›Full record

ReviewJournal of mathematical biology2025

Go-or-grow models in biology: a monster on a leash.

Ryan Thiessen, Martina Conte, Tracy L Stepien, Thomas Hillen

Abstract readReview
In one paragraph

Review in Journal of mathematical biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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.

Ryan Thiessen *Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, Canada.
Martina Conte *Department of Mathematical, Physical, and Computer Sciences, University of Parma, Parma, Italy. conte.martina.93@gmail.com.ORCID 0000-0002-8477-3267
Tracy L StepienDepartment of Mathematics, University of Florida, Gainesville, FL, USA.
Thomas HillenDepartment of Mathematical and Statistical Sciences, University of Alberta, Edmonton, Canada.

Funding

Gruppo Nazionale per la Fisica Matematica CUP E53C22001930001Ministero dell'Università e della Ricerca CUP D53D23018960001Ministero dell'Università e della Ricerca project code P2022PSMT7National Science Foundation DMS-2151566National Science Foundation RGPIN-2023-04269Natural Sciences and Engineering Research Council of Canada Canadian Graduate Scholarship
6 · The paper itself

Abstract

Go-or-grow approaches represent a specific class of mathematical models used to describe populations where individuals either migrate or reproduce, but not both simultaneously. These models have a wide range of applications in biology and medicine, chiefly among those the modeling of brain cancer spread. The analysis of go-or-grow models has inspired new mathematics, and it is the purpose of this review to highlight interesting and challenging mathematical properties of reaction-diffusion models of the go-or-grow type. We provide a detailed review of biological and medical applications before focusing on key results concerning solution existence and uniqueness, pattern formation, critical domain size problems, and traveling waves. We present new results related to the critical domain size and traveling wave problems, and we connect these findings to the existing literature. Moreover, we demonstrate the high level of instability inherent in go-or-grow models. We argue that there is currently no accurate numerical solver for these models, and emphasize that special care must be taken when dealing with the "monster on a leash".

Indexed as

Models, BiologicalAnimalsComputer SimulationHumansMathematical ConceptsPopulation DynamicsCritical domain sizeGliomaGo-or-grow dichotomyInstabilities and PatternsTraveling Waves

Identifiers

PMID41099842
PMCPMC12532697

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.