Multi-scale Modeling And Simulation Of Skeletal Muscle Based On Regulation Mechanism Of Neural System And Immune System

The research on multi-scale modeling of skeletal muscle has been going on for a long time,which has also spawned various modeling methods.Multiscale refers to the analysis of the relationship between different levels through the coupling of modeling at different levels at multiple scales,such as simple macro and micro,physiology and physics,so as to obtain a more comprehensive and systematic model,namely multi-scale model.Multi-scale model plays an important role in solving the problems of skeletal muscle.This paper models the physiological and mechanical properties of skeletal muscle at different scales based on neural regulation and immune regulation.In a practical sense,it mainly aims at how to recover from the injury of stroke patients and how to control muscle movement through neural regulation.This paper is divided into the following contents.First of all,for stroke patients,muscle damage is easy to occur due to the reduction of regenerative capacity.In this part,an immune based skeletal muscle regeneration model is established.It includes interactions between the immune system,healthy and damaged nuclear cells,and satellite cells.21 specific parameters were considered,including those of the interaction between damaged and dead muscle nuclei,immune system and satellite cells.An important assumption of the model abstracts the specific situation of muscle injury as the change of population number in the muscle nucleus and dead or damaged muscle nucleus.Then,based on the physiological process of skeletal muscle regeneration and experimental data,a mathematical model was established and the sensitivity of parameters was analyzed.Secondly,based on the optimal control theory of the neural system,the optimal performance of the musculoskeletal arm during stretching is studied.The initial and final states(position and velocity)are the only known data for the response trajectory.Two biomechanical objective functions are considered,the quadratic function of muscle stress(or force)and the quadratic function of total exercise time plus muscle stress.Under the premise of considering arm motion constraints and muscle force constraints,a two degree of freedom nonlinear musculoskeletal arm model with six muscle drivers and four state variables is used to evaluate the proposed optimal strategy.The extreme value variation method is used to solve the nonlinear differential equation of the optimal control problem.For the second objective function,an improved VE method is used.Finally,the overall model is simulated and verified,and compared with the experimental data to verify the accuracy of the model.Combined with muscle regeneration,the accuracy of the model for predicting arm movement in stroke patients was explored.This has guiding significance for the treatment of stroke patients.