Muscle models

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

1. Janssen I, Heymsfield SB, Wang ZM, and Ross R. Skeletal muscle mass and distribution in 468 men and women aged 18-88 yr. J Appl Physiol (1985). 2000 Jul;89(1):81-8. DOI:10.1152/jappl.2000.89.1.81 |
1. Janssen, I., Heymsfield, S.B., Wang, Z. and Ross, R., 2000. Skeletal muscle mass and distribution in 468 men and women aged 18–88 yr. Journal of applied physiology, 89(1), pp.81-88.
2. . Kunz, W.S., 2001. Control of oxidative phosphorylation in skeletal muscle. Biochimica et Biophysica Acta (BBA)-Bioenergetics, 1504(1), pp.12-19.
3. . Gonzalez‐Freire, M., Semba, R.D., Ubaida‐Mohien, C., Fabbri, E., Scalzo, P., Højlund, K., Dufresne, C., Lyashkov, A. and Ferrucci, L., 2017. The Human Skeletal Muscle Proteome Project: a reappraisal of the current literature. Journal of cachexia, sarcopenia and muscle, 8(1), pp.5-18.
4. . Li, Y., Lai, N., Kirwan, J.P. and Saidel, G.M., 2012. Computational model of cellular metabolic dynamics in skeletal muscle fibers during moderate intensity exercise. Cellular and molecular bioengineering, 5(1), pp.92-112.
5. . Li, Y., Solomon, T.P., Haus, J.M., Saidel, G.M., Cabrera, M.E. and Kirwan, J.P., 2010. Computational model of cellular metabolic dynamics: effect of insulin on glucose disposal in human skeletal muscle. American Journal of Physiology-Endocrinology and Metabolism, 298(6), pp.E1198-E1209.