Task-dependent recruitment across ankle extensor muscles and between mechanical demands is driven by the metabolic cost of muscle contraction

Abstract

The nervous system is faced with numerous strategies for recruiting a large number of motor units within and among muscle synergists to produce and control body movement. This is challenging, considering multiple combinations of motor unit recruitment may result in the same movement. Yet vertebrates are capable of performing a wide range of movement tasks with different mechanical demands. In this study, we used an experimental human cycling paradigm and musculoskeletal simulations to test the theory that a strategy of prioritizing the minimization of the metabolic cost of muscle contraction, which improves mechanical efficiency, governs the recruitment of motor units within a muscle and the coordination among synergist muscles within the limb. Our results support our hypothesis, for which measured muscle activity and model-predicted muscle forces in soleus—the slower but stronger ankle plantarflexor—is favoured over the weaker but faster medial gastrocnemius (MG) to produce plantarflexor force to meet increased load demands. However, for faster-contracting speeds induced by faster-pedalling cadence, the faster MG is favoured. Similar recruitment patterns were observed for the slow and fast fibres within each muscle. By contrast, a commonly used modelling strategy that minimizes muscle excitations failed to predict force sharing and known physiological recruitment strategies, such as orderly motor unit recruitment. Our findings illustrate that this common strategy for recruiting motor units within muscles and coordination between muscles can explain the control of the plantarflexor muscles across a range of mechanical demands.

Resource Allocation in Mammalian Systems

“…To manage these trade-offs, mammals have evolved various strategies for efficient muscle use. This includes tight regulation of concentration homeostasis for maximal bioenergy metabolism (Glancy et al, 2013;Glancy and Balaban, 2021), cell-type specialization (Schiaffino and Reggiani, 2011) and tissue heterogeneity (Lai et al, 2021;Liu et al, 2012) optimized for distinct mechanical tasks, and use of energy-conserving motions (Miller and Hamill, 2015;Rues et al, 2008;Schindler et al, 2007;Umberger et al, 2003).…”

Implementation and acceleration of optimal control for systems biology

“…Across the life sciences, we encounter systems over which we wish to exert control. Whether we consider outbreak control in epidemiology [1,2], chemotherapy in oncology [3][4][5], muscle contraction and gait regulation in biomechanics [6][7][8], engineering cellular processes in synthetic biology [9,10], cell population growth in tissue engineering [11,12], or biodiversity and invasive species management in ecology [13][14][15], we face decisions around how a particular intervention should be applied to best achieve desired outcomes. Using mathematical models of such systems, optimal control theory provides insight into these resource allocation decisions.…”

Implementation and acceleration of optimal control for systems biology

“…Across the life sciences, we encounter systems over which we wish to exert control. Whether we consider outbreak control in epidemiology [1,51], chemotherapy in oncology [8,18,70], muscle contraction and gait regulation in biomechanics [29,42,58], engineering cellular processes in synthetic biology [25,36], or biodiversity and invasive species management in ecology [9,19,21], we face decisions around how a particular intervention should be applied to best achieve desired outcomes. Using mathematical models of such systems, optimal control theory provides insight into these resource allocation decisions.…”