Automating population construction and parallel simulation of biophysical models for neuromuscular cells: An inverse approach

Abstract

Biophysical modeling and simulation help to promote a comprehensive understanding of the neuromuscular mechanisms underlying muscle force generation and control in normal and pathological states. However, this process is labor intensive and limited to special conditions due to the heterogeneity of neuromuscular cells and the variability in their organization across body parts and ages. We present a methodology to resolve this issue. First, we formulate a building-block approach with an inverse modeling framework for automated population construction and tractable hierarchical analysis under various physiological conditions. Second, we devise a network folder-based approach with a virtual environment technique for efficient parallel simulation that can operate on a multicore computer, a supercomputing system, or a computer network through the internet. Third, we implement the methodology by developing open-source command-line software called pNMS. Finally, we demonstrate that pNMS can replicate experimental and simulation results from different environments and predict the population behaviors of neuromuscular cells depending on their organization and muscle length. With an intuitive, flexible application programming interface, this software tool may offer a solution for promoting efficient investigation and an in-depth understanding of neuromuscular function at cellular resolution under realistic scenarios.