Difference between revisions of "Muscle models"
(→Introduction) |
|||
| Line 6: | Line 6: | ||
===References=== | ===References=== | ||
<biblio> | <biblio> | ||
| − | #1 pmid=10904038 <!-- Janssen et al | + | #1 pmid=10904038 <!-- Janssen et al 2000 --> |
| − | # | + | #2 pmid=11239481 <!-- Kunz 2001 --> |
| − | + | #3 pmid=27897395 <!-- Gonzalez‐Freire et al 2017 --> | |
| − | #3 | + | #4 pmid=22942911 <!-- Li et al 2012 --> |
| − | #4 | + | #5 pmid=20332360 <!-- Li et al 2010 --> |
| − | #5 | + | |
</biblio> | </biblio> | ||
Revision as of 15:30, 28 September 2018
Introduction
Skeletal muscle is one of the most abundant tissues in mammals, accounting for up to 40% of the total mass ofthe human body (Janssen et al., 2000)[1]. The contraction–relaxation cycle in muscle requires energy that is mostly generated aerobically by mitochondria particularly abundant in adult muscle fibres. It is worth to note that skeletal muscle can maintain ATP concentration constant during the transition from rest to exercise, whereas metabolic reaction rates may increase substantially (Kunz, 2001) [2]. Although it is well known that skeletal muscle adaptations to exercise depend on duration, intensity, and frequency, changes in muscle proteins associated with different types of exercise have not been well characterized (Gonzalez‐Freire et al., 2017) [3]. Moreover, the quantitative contributions of different fiber types to the energy demand and detailed dynamics of metabolic responses of the skeletal muscle in response to different exercise intensities are unknown. Indeed, accurate measurements to quantify the recruitment and metabolic activation of muscle fibers in vivo have not been possible to date (Li et al., 2012) [4]. So due to a shortage of dynamic in vivo human data, the regulatory mechanisms of functioning of the skeletal muscle metabolism are poorly understood. To quantitatively interpret the limited data, a physiologically based mathematical modeling approach can be applied (Li et al., 2010) [5].
References
Error fetching PMID 11239481:
Error fetching PMID 27897395:
Error fetching PMID 22942911:
Error fetching PMID 20332360:
- Error fetching PMID 10904038:
- Error fetching PMID 11239481:
- Error fetching PMID 27897395:
- Error fetching PMID 22942911:
- Error fetching PMID 20332360:
