Build muscle (why exercise can build muscle?)
When we exercise, the higher the load, the more repetitions, the greater the muscle gain. However, why does exercise build muscle? Why does prolonged inactivity cause muscle atrophy?
These two questions seem simple, but most of the answers come from anecdotes, as well as a large amount of relevant experience and knowledge accumulated in the field of sports and rehabilitation medicine. But this knowledge fails to answer two key phenomena that lead to muscle hypertrophy and atrophy, namely, how does a muscle “know” that it is moving (spontaneous involuntary activity), and how does it signal a morphological response to *** tense or atrophic?
So in terms of muscle training, it seems that everyone can’t predict how hard he will work, how long he will practice, and how much effect he will achieve.
Muscles are highly ordered organs that can be divided into smaller and smaller substructures. The muscle knot is the most basic unit of muscle contraction and hypertrophy. Each sarcomere contains many myofilaments, which can be divided into thick and thin filaments. These filaments are only 1 micron long and less than 1 micron in diameter. They are smaller than muscle fibers (muscle cells).
There have been some previous studies on individual muscles or their individual muscle fibers, which obviously cannot answer the above questions. Therefore, it is necessary to find some explanation for muscle growth at the molecular level.
Recently, in a study published in the “Biophysical Journal”, Professor Eugene Terentjev and Neil Yi from the Cavendish Laboratory of the University of Cambridge, UK Neil Ibata (Nature at the age of 15) (almost half of the world’s Nobel Prize winners in physics come from this laboratory) developed a set of mathematical models using methods from theoretical biophysics. Ribosome dynamics combining intracellular mechanosensors, signaling chain pathways, and post-transcriptional synthesis reveals how muscles sense and respond to external loads by producing (or degrading) their contractile proteins. In this way, it can predict the best plan for “training muscles” according to different individuals. This model lays the foundation for mobile applications (Apps). In the future, users are expected to obtain their own better muscle-building programs by inputting personal physiological details.
How nutritional responses are organized through signal transduction within muscle cells is a central question in exercise science and the study of conditions that affect muscle homeostasis, including development and aging, as well as many diseases.
Iberta said: “The interplay between the major structural molecules in muscle was only pieced together about 50 years ago. Currently, it is not entirely clear how these small accessory proteins are integrated into the overall muscle growth process Medium. This is because the data are hard to come by. The physiology and behavior of individuals vary so widely that it is almost impossible to conduct controlled experiments on muscle size changes in real people.”
“You can extract muscle cells and individually, but then you ignore other factors such as oxygen and glucose levels during exercise,” Professor Terentjev said. Therefore, it is difficult to observe all factors together. “
A few years ago, they studied the mechanisms of mechanosensing, the ability of cells to sense mechanical cues from their environment. Three years ago, they began to study how the proteins in the muscle filaments changed under the external force of Youyou Resources Network, and found that the two main muscle proteins, actin and myosin, lacked binding sites for signaling molecules, so It must be Titin that is responsible for signaling the change in force.
Every muscle in the body has a better resistance training weight. Muscles can only bear weight close to their greater load for short periods of time. Over time, the integrated load can activate cellular signaling pathways that promote new muscle protein synthesis. But the sub-threshold load is not enough to generate a lot of signal transmission, so it must be multiplied by the time of motion to compensate. Thresholds may depend on individual physiological characteristics.
The researchers found that whenever a part of the molecule is under tension long enough, it switches to a different state, exposing previously hidden regions. If this region can bind to a small molecule involved in cell signaling, it activates the molecule, creating a chemical signaling chain. Titin is a huge protein, and when a muscle is stretched, a large part is stretched; but when a muscle contracts, a small part of it is also tensed. This part of troponin contains the so-called troponin kinase (TK) domain, which produces chemical signals that affect muscle growth.
If a molecule is subjected to a greater force or held under the same force for a longer period of time, it will be more likely to be opened. Both conditions increase the number of activated signaling molecules. These molecules then induce the synthesis of more messenger RNA (mRNA), which creates new muscle proteins and, at the same time, the cross-sectional area of the muscle cells increases.
This knowledge led to this new research work, and Ibata himself is very passionate about sports. “Considering that athletes can avoid inefficient athletic programs and maximize their potential through regular, high-quality training, significant time and resource savings can be achieved,” he said. It would be nice to have a better understanding of why and how to gain muscle. “
Terentjev and Ibata set out to develop this mathematical model, which can quantitatively predict muscle growth. They started with a simple model that documented the opening and initiation of signaling cascades by external forces, and used a microscope to data to determine the force-dependent probability that a single troponin kinase unit would turn on or off in response to an external force and activate a signaling molecule.
Later, they added additional information such as metabolic energy exchange, repetition and recovery time , making the model more complex, which was validated by previous long-term studies of muscle hypertrophy.
Terentjev said: “Our model provides support for the idea that muscle growth occurs mainly at the 70% of the heavier load. Physiological basis, which is also the theoretical support behind resistance training. Below this level, the opening rate of troponin kinase decreases dramatically, preventingOccurrence of mechanosensitive signals. Furthermore, our model already predicts that rapid fatigue hinders good outcomes.
Additionally, the model addresses the issue of muscle atrophy, which occurs when astronauts are bedridden for long periods of time or are exposed to microgravity. The model shows how soon the muscles begin to atrophy, what are the better recovery options.
In conclusion, this research advances our understanding of the mechanisms underlying how muscles sense and respond to load.
The researchers hope to develop a user A friendly app that provides personalized exercise programs for specific groups of people. They also hope to improve their model by expanding the analysis of detailed data for men and women, because Xu Youyou’s multi-sport research is heavily biased towards male athletes.