Literature DB >> 27825162

Computational Modeling of Muscle Regeneration and Adaptation to Advance Muscle Tissue Regeneration Strategies.

Kyle S Martin, Kelley M Virgilio, Shayn M Peirce, Silvia S Blemker.   

Abstract

Skeletal muscle has an exceptional ability to regenerate and adapt following injury. Tissue engineering approaches (e.g. cell therapy, scaffolds, and pharmaceutics) aimed at enhancing or promoting muscle regeneration from severe injuries are a promising and active field of research. Computational models are beginning to advance the field by providing insight into regeneration mechanisms and therapies. In this paper, we summarize the contributions computational models have made to understanding muscle remodeling and the functional implications thereof. Next, we describe a new agent-based computational model of skeletal muscle inflammation and regeneration following acute muscle injury. Our computational model simulates the recruitment and cellular behaviors of key inflammatory cells (e.g. neutrophils and M1 and M2 macrophages) and their interactions with native muscle cells (muscle fibers, satellite stem cells, and fibroblasts) that result in the clearance of necrotic tissue and muscle fiber regeneration. We demonstrate the ability of the model to track key regeneration metrics during both unencumbered regeneration and in the case of impaired macrophage function. We also use the model to simulate regeneration enhancement when muscle is primed with inflammatory cells prior to injury, which is a putative therapeutic intervention that has not yet been investigated experimentally. Computational modeling of muscle regeneration, pursued in combination with experimental analyses, provides a quantitative framework for evaluating and predicting muscle regeneration and enables the rational design of therapeutic strategies for muscle recovery.
© 2016 S. Karger AG, Basel.

Entities:  

Mesh:

Year:  2016        PMID: 27825162     DOI: 10.1159/000443635

Source DB:  PubMed          Journal:  Cells Tissues Organs        ISSN: 1422-6405            Impact factor:   2.481


  8 in total

1.  Fibroblasts: Diverse Cells Critical to Biomaterials Integration.

Authors:  Riley T Hannan; Shayn M Peirce; Thomas H Barker
Journal:  ACS Biomater Sci Eng       Date:  2017-06-13

2.  Agent-based model illustrates the role of the microenvironment in regeneration in healthy and mdx skeletal muscle.

Authors:  Kelley M Virgilio; Kyle S Martin; Shayn M Peirce; Silvia S Blemker
Journal:  J Appl Physiol (1985)       Date:  2018-08-02

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Authors:  Marco Quarta; Melinda J Cromie Lear; Justin Blonigan; Patrick Paine; Robert Chacon; Thomas A Rando
Journal:  NPJ Regen Med       Date:  2018-10-10

4.  Modelling multi-scale cell-tissue interaction of tissue-engineered muscle constructs.

Authors:  Ryo Torii; Rallia-Iliana Velliou; David Hodgson; Vivek Mudera
Journal:  J Tissue Eng       Date:  2018-08-13       Impact factor: 7.813

5.  Multiscale Coupling of an Agent-Based Model of Tissue Fibrosis and a Logic-Based Model of Intracellular Signaling.

Authors:  S Michaela Rikard; Thomas L Athey; Anders R Nelson; Steven L M Christiansen; Jia-Jye Lee; Jeffrey W Holmes; Shayn M Peirce; Jeffrey J Saucerman
Journal:  Front Physiol       Date:  2019-12-17       Impact factor: 4.566

6.  A data-driven computational model enables integrative and mechanistic characterization of dynamic macrophage polarization.

Authors:  Chen Zhao; Thalyta X Medeiros; Richard J Sové; Brian H Annex; Aleksander S Popel
Journal:  iScience       Date:  2021-01-29

Review 7.  Key concepts in muscle regeneration: muscle "cellular ecology" integrates a gestalt of cellular cross-talk, motility, and activity to remodel structure and restore function.

Authors:  Judy E Anderson
Journal:  Eur J Appl Physiol       Date:  2021-12-20       Impact factor: 3.078

8.  Applying optimization algorithms to tuberculosis antibiotic treatment regimens.

Authors:  Joseph M Cicchese; Elsje Pienaar; Denise E Kirschner; Jennifer J Linderman
Journal:  Cell Mol Bioeng       Date:  2017-08-30       Impact factor: 2.321

  8 in total

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