Knowing how much to train to gain muscle without exercising too much: this proposes a new mathematical model of muscle
Exercise is something healthy and there are many ways to get away from the sofa to burn kilocalories and exercise our systems and muscles, but there are those who also look for the aesthetic touch of that certain muscular hypertrophy that denotes that the gym (and a more specific diet) are inherent to our existence. And the latest mathematical model on bodybuilding that have been developed by researchers may be of interest to this sector of the population that seeks to show off biceps, quadriceps and other muscles.
This is an investigation by a group from the University of Cambridge, who through theoretical biophysical methods have found a mathematical model for, they say, be able to indicate in an optimal way the intensity and the necessary time of exercise for each case. And the starting point, or the base of the model, is a protein (previously already studied) related to muscle growth.
That can be used in a mobile app
As a reminder or a brief notion of biology (somewhat advanced), muscle cells, called fibers, are highly specialized and move away from that typical sphere shape and structure that is often used as a generic model. They are multinucleated threads with a protein skeleton that, thanks to the interactions between one and the other (due to the intervention of calcium, the nervous impulse and other elements) causes the contraction and relaxation of the fibers, which on a large scale is what the muscle experiences when, for example, we lift a dumbbell .
These proteins are organized into units called sarcomeres, finding proteins such as actin, myosin or tropomyosin. In this case, the work, published in the Biophysical Journal, part of a previous work by the same team in which they already observed that another protein, the titina, is responsible for generating certain chemical signals that are related to muscle growth.
Titin has a part (the kinase domain) that is considered a candidate to participate in mechanical signaling, as they explain. On this basis, the quantitative mathematical model describes the kinetics of this signaling to predict changes in response to exercise and rehabilitation.
As they saw, tintine signaling increases considerably after intense weight-bearing exercise, compared to a lower-intensity exercise. Hence is related to muscle growth, acting (according to these researchers) as the ultimate responsible for increasing muscle size, associated with the appropriate combination of time and speed, usually starting from series of few repetitions to prioritize weight gain.
Eugene Terentjev and Neil Ibata, the authors of the model, began to record the activation (opening) of titin molecules in an effort, thus initiating that signaling. With this, they studied the probability (dependent on effort) that a titin kinase unit opened or closed and said “growth need” signaling was activated.
After this first model, they added additional information such as metabolic energy exchange, duration of repetitions and recovery. According to Terentjev, his model “offers a physiological basis with the idea that the muscle grows mainly when there is a 70% charge, which is the idea behind resistance training “, pointing out that below this percentage” the opening rate of titin falls precipitously and the signals do not take place “.
According to the model, rapid exhaustion is an obstacle to a good result, so the model proposes an activity threshold so that training favors muscle hypertrophy without excess energy expenditure. It also calculates the required recovery so that it is done in a way that is convenient for the purpose.
The English Institute of Sport has also participated in the work, a private British institution focused on research on the performance of athletes and other aspects. According to Fionn MacPartlin, from the institute’s team of coaches, the model would give them more information on how the muscles respond to the load and thus design more specific strategies, especially for elite athletes.
The idea is that this model can be used in some kind of software (like a mobile app) in which users can create their exercise tables according to the mathematical model and the physiology of each individual. We will see if this ends up being the case and, above all, if it works, especially for those who do not profess so much love for the repetitions necessary to achieve that muscle growth and thus do not have to do more.