Evidence map›Paper›PMID 38828844›Full record

ArticleeLife2024

Agent-based model demonstrates the impact of nonlinear, complex interactions between cytokinces on muscle regeneration.

Megan Haase, Tien Comlekoglu, Alexa Petrucciani, Shayn M Peirce, Silvia S Blemker

Abstract read
In one paragraph

Article in eLife, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Megan HaaseUniversity of Virginia, Charlottesville, United States.ORCID https://orcid.org/0000-0002-5221-4495
Tien ComlekogluUniversity of Virginia, Charlottesville, United States.
Alexa PetruccianiPurdue University, West Lafayette, United States.
Shayn M PeirceUniversity of Virginia, Charlottesville, United States.ORCID https://orcid.org/0000-0001-5857-5606
Silvia S BlemkerUniversity of Virginia, Charlottesville, United States.ORCID https://orcid.org/0000-0002-2019-1153

Funding

Interdisciplinary Training in Systems & Biomolecular Data ScienceT32GM145443 · NIGMS · UNIVERSITY OF VIRGINIA · PI Kevin A Janes, Jason Papin · 2022 to 2026
$1.5M
Modeling to design optimized estrogen-specific muscle regeneration treatmentR21AR080415 · NIAMS · UNIVERSITY OF VIRGINIA · PI BLEMKER, SILVIA SALINAS, PEIRCE-COTTLER, SHAYN · 2022 to 2023
$369k
National Science Foundation 1842490NIAMS NIH HHS R21 AR080415NIGMS NIH HHS T32 GM145443NIH HHS R21AR080415Wu-Tsai Foundation Agility Project Funding
6 · The paper itself

Abstract

Muscle regeneration is a complex process due to dynamic and multiscale biochemical and cellular interactions, making it difficult to identify microenvironmental conditions that are beneficial to muscle recovery from injury using experimental approaches alone. To understand the degree to which individual cellular behaviors impact endogenous mechanisms of muscle recovery, we developed an agent-based model (ABM) using the Cellular-Potts framework to simulate the dynamic microenvironment of a cross-section of murine skeletal muscle tissue. We referenced more than 100 published studies to define over 100 parameters and rules that dictate the behavior of muscle fibers, satellite stem cells (SSCs), fibroblasts, neutrophils, macrophages, microvessels, and lymphatic vessels, as well as their interactions with each other and the microenvironment. We utilized parameter density estimation to calibrate the model to temporal biological datasets describing cross-sectional area (CSA) recovery, SSC, and fibroblast cell counts at multiple timepoints following injury. The calibrated model was validated by comparison of other model outputs (macrophage, neutrophil, and capillaries counts) to experimental observations. Predictions for eight model perturbations that varied cell or cytokine input conditions were compared to published experimental studies to validate model predictive capabilities. We used Latin hypercube sampling and partial rank correlation coefficient to identify in silico perturbations of cytokine diffusion coefficients and decay rates to enhance CSA recovery. This analysis suggests that combined alterations of specific cytokine decay and diffusion parameters result in greater fibroblast and SSC proliferation compared to individual perturbations with a 13% increase in CSA recovery compared to unaltered regeneration at 28 days. These results enable guided development of therapeutic strategies that similarly alter muscle physiology (i.e. converting extracellular matrix [ECM]-bound cytokines into freely diffusible forms as studied in cancer therapeutics or delivery of exogenous cytokines) during regeneration to enhance muscle recovery after injury.

Indexed as

Muscle, SkeletalRegenerationAnimalsCytokinesFibroblastsMacrophagesMiceModels, BiologicalCytokinesagent-based modelcell biologycomputational biologycytokine dynamicsmousemuscle regenerationskeletal musclesystems biology

Identifiers

PMID38828844
PMCPMC11147512

What Socratic 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 Socratic graph.