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

被引:14
|
作者
Martin, Kyle S. [1 ]
Virgilio, Kelley M. [1 ]
Peirce, Shayn M. [1 ]
Blennker, Silvia S. [1 ]
机构
[1] Univ Virginia, Dept Biomed Engn, Charlottesville, VA USA
基金
美国国家科学基金会;
关键词
Agent-based model; Computational model; Inflammation; Muscle regeneration; GROWTH-FACTOR-I; SATELLITE CELL ADDITION; SKELETAL-MUSCLE; REPAIR CONSTRUCT; MACROPHAGES; INFLAMMATION; INJURY; SECRETION; DIFFERENTIATION; PROLIFERATION;
D O I
10.1159/000443635
中图分类号
R602 [外科病理学、解剖学]; R32 [人体形态学];
学科分类号
100101 ;
摘要
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. (C) 2016 S. Karger AG, Basel
引用
收藏
页码:250 / 266
页数:17
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