Systems analysis of biological networks in skeletal muscle function. (27th November 2012) - Smith, Lucas R.; Meyer, Gretchen; Lieber, Richard L.
Abstract: Skeletal muscle function depends on the efficient coordination among subcellular systems. These systems are composed of proteins encoded by a subset of genes, all of which are tightly regulated. In the cases where regulation is altered because of disease or injury, dysfunction occurs. To enable objective analysis of muscle gene expression profiles, we have defined nine biological networks whose coordination is critical to muscle function. We begin by describing the expression of proteins necessary for optimal neuromuscular junction function that results in the muscle cell action potential. That action potential is transmitted to proteins involved in excitation–contraction coupling enabling Ca 2+ release. Ca 2+ then activates contractile proteins supporting actin and myosin cross‐bridge cycling. Force generated by cross‐bridges is transmitted via cytoskeletal proteins through the sarcolemma and out to critical proteins that support the muscle extracellular matrix. Muscle contraction is fueled through many proteins that regulate energy metabolism. Inflammation is a common response to injury that can result in alteration of many pathways within muscle. Muscle also has multiple pathways that regulate size through atrophy or hypertrophy. Finally, the isoforms associated with fast muscle fibers and their corresponding isoforms in slow muscle fibers are delineated. These nine networks represent important biological systems that affect skeletal muscle function. Combining high‐throughput systems analysis with advanced networking software will allow researchers to use these networks to objectively study skeletal muscle systems. WIREs Syst Biol Med 2013, 5:55–71. doi: 10.1002/wsbm.1197 This article is categorized under: Models of Systems Properties and Processes > Mechanistic Models Physiology > Mammalian Physiology in Health and Disease Laboratory Methods and Technologies > RNA Methods