---
_id: '15315'
abstract:
- lang: eng
text: Single and collective cell migration are fundamental processes critical for
physiological phenomena ranging from embryonic development and immune response
to wound healing and cancer metastasis. To understand cell migration from a physical
perspective, a broad variety of models for the underlying physical mechanisms
that govern cell motility have been developed. A key challenge in the development
of such models is how to connect them to experimental observations, which often
exhibit complex stochastic behaviours. In this review, we discuss recent advances
in data-driven theoretical approaches that directly connect with experimental
data to infer dynamical models of stochastic cell migration. Leveraging advances
in nanofabrication, image analysis, and tracking technology, experimental studies
now provide unprecedented large datasets on cellular dynamics. In parallel, theoretical
efforts have been directed towards integrating such datasets into physical models
from the single cell to the tissue scale with the aim of conceptualising the emergent
behaviour of cells. We first review how this inference problem has been addressed
in both freely migrating and confined cells. Next, we discuss why these dynamics
typically take the form of underdamped stochastic equations of motion, and how
such equations can be inferred from data. We then review applications of data-driven
inference and machine learning approaches to heterogeneity in cell behaviour,
subcellular degrees of freedom, and to the collective dynamics of multicellular
systems. Across these applications, we emphasise how data-driven methods can be
integrated with physical active matter models of migrating cells, and help reveal
how underlying molecular mechanisms control cell behaviour. Together, these data-driven
approaches are a promising avenue for building physical models of cell migration
directly from experimental data, and for providing conceptual links between different
length-scales of description.
acknowledgement: This work was supported by the Deutsche Forschungsgemeinschaft (German
Research Foundation)—Project-ID 201269156—SFB 1032 (Project B12). D B B was supported
by an NOMIS Fellowship and an EMBO Fellowship (ALTF 343-2022). We thank Joachim
Rädler, Alexandra Fink, Erwin Frey, Pierre Ronceray, Ricard Alert, Edouard Hannezo,
Henrik Flyvbjerg, Ulrich Schwarz, Joshua Shaevitz, Greg Stephens, Andrea Cavagna,
Grzegorz Gradziuk, Fridtjof Brauns, Nikolas Claussen, Tom Brandstätter, Johannes
Flommersfeld, Christoph Schreiber, Nicolas Arlt, Matthew Schmitt, Joris Messelink,
Federico Gnesotto, Federica Mura, Bram Hoogland, Manon Wigbers, Isabella Graf, Jessica
Lober, and many others for inspiring discussions. We also thank Claudia Flandoli
for the artwork in figures 1, 5, 8 and 9.
article_number: '056601'
article_processing_charge: Yes (in subscription journal)
article_type: review
arxiv: 1
author:
- first_name: David
full_name: Brückner, David
id: e1e86031-6537-11eb-953a-f7ab92be508d
last_name: Brückner
orcid: 0000-0001-7205-2975
- first_name: Chase P.
full_name: Broedersz, Chase P.
last_name: Broedersz
citation:
ama: 'Brückner D, Broedersz CP. Learning dynamical models of single and collective
cell migration: a review. Reports on Progress in Physics. 2024;87(5). doi:10.1088/1361-6633/ad36d2'
apa: 'Brückner, D., & Broedersz, C. P. (2024). Learning dynamical models of
single and collective cell migration: a review. Reports on Progress in Physics.
IOP Publishing. https://doi.org/10.1088/1361-6633/ad36d2'
chicago: 'Brückner, David, and Chase P. Broedersz. “Learning Dynamical Models of
Single and Collective Cell Migration: A Review.” Reports on Progress in Physics.
IOP Publishing, 2024. https://doi.org/10.1088/1361-6633/ad36d2.'
ieee: 'D. Brückner and C. P. Broedersz, “Learning dynamical models of single and
collective cell migration: a review,” Reports on Progress in Physics, vol.
87, no. 5. IOP Publishing, 2024.'
ista: 'Brückner D, Broedersz CP. 2024. Learning dynamical models of single and collective
cell migration: a review. Reports on Progress in Physics. 87(5), 056601.'
mla: 'Brückner, David, and Chase P. Broedersz. “Learning Dynamical Models of Single
and Collective Cell Migration: A Review.” Reports on Progress in Physics,
vol. 87, no. 5, 056601, IOP Publishing, 2024, doi:10.1088/1361-6633/ad36d2.'
short: D. Brückner, C.P. Broedersz, Reports on Progress in Physics 87 (2024).
corr_author: '1'
date_created: 2024-04-14T22:01:01Z
date_published: 2024-04-04T00:00:00Z
date_updated: 2025-09-04T13:39:07Z
day: '04'
ddc:
- '530'
department:
- _id: EdHa
doi: 10.1088/1361-6633/ad36d2
external_id:
arxiv:
- '2309.00545'
isi:
- '001196692400001'
pmid:
- '38518358'
file:
- access_level: open_access
checksum: c5910078230ade20f4dd83592e862a72
content_type: application/pdf
creator: dernst
date_created: 2024-08-20T11:00:03Z
date_updated: 2024-08-20T11:00:03Z
file_id: '17451'
file_name: 2024_ReportPhysics_Brueckner.pdf
file_size: 4376898
relation: main_file
success: 1
file_date_updated: 2024-08-20T11:00:03Z
has_accepted_license: '1'
intvolume: ' 87'
isi: 1
issue: '5'
language:
- iso: eng
license: https://creativecommons.org/licenses/by/3.0/
month: '04'
oa: 1
oa_version: Published Version
pmid: 1
project:
- _id: 34e2a5b5-11ca-11ed-8bc3-b2265616ef0b
grant_number: ALTF 343-2022
name: A mechano-chemical theory for stem cell fate decisions in organoid development
publication: Reports on Progress in Physics
publication_identifier:
eissn:
- 1361-6633
issn:
- 0034-4885
publication_status: published
publisher: IOP Publishing
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'Learning dynamical models of single and collective cell migration: a review'
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/3.0/legalcode
name: Creative Commons Attribution 3.0 Unported (CC BY 3.0)
short: CC BY (3.0)
type: journal_article
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 87
year: '2024'
...