The virtual physiological human gets nerves! How to account for the action of the nervous system in multiphysics simulations of human organs

Abstract

This article shows how to couple multiphysics and artificial neural networks to design computer models of human organs that autonomously adapt their behaviour to environmental stimuli. The model simulates motility in the intestine and adjusts its contraction patterns to the physical properties of the luminal content. Multiphysics reproduces the solid mechanics of the intestinal membrane and the fluid mechanics of the luminal content; the artificial neural network replicates the activity of the enteric nervous system. Previous studies recommended training the network with reinforcement learning. Here, we show that reinforcement learning alone is not enough; the input–output structure of the network should also mimic the basic circuit of the enteric nervous system. Simulations are validated against in vivo measurements of high-amplitude propagating contractions in the human intestine. When the network has the same input–output structure of the nervous system, the model performs well even when faced with conditions outside its training range. The model is trained to optimize transport, but it also keeps stress in the membrane low, which is exactly what occurs in the real intestine. Moreover, the model responds to atypical variations of its functioning with ‘symptoms’ that reflect those arising in diseases. If the healthy intestine model is made artificially ill by adding digital inflammation, motility patterns are disrupted in a way consistent with inflammatory pathologies such as inflammatory bowel disease.

Simulating the Hydrodynamic Conditions of the Human Ascending Colon: A Digital Twin of the Dynamic Colon Model

“…For technical details and how it is applied to the large intestine, the reader is referred to Refs. [3,5,16].…”

“…Over the last few years, several in silico models of the human proximal colon have been developed [3,5,16] based on a computational technique called Discrete Multiphysics (DMP) [17,18]. Recently, this approach has been applied to the pharmaceutical field and used to model drug release from a solid dosage form under the influence of different in vivo motility patterns [4].…”

Computational models of autonomic regulation in gastric motility: Progress, challenges, and future directions

“…Another approach is a meshless smooth particle hydrodynamics approach as demonstrated by Alexiadis et al (2021) where intestinal contents were modeled. The particles representing intestinal contents interacted with a lattice of distinct particles that represented the intestinal wall.…”

Modelling Particle Agglomeration on through Elastic Valves under Flow

“…Discrete Multiphysics (DMP) is a computational approach on computational particles rather than computational mesh [15]. It links together different discrete models such as Smoothed Particle Hydrodynamics (SPH) [16], Lattice Spring Model (LSM) [17,18], Discrete Element Method (DEM) [19], and Peridynamics [20], which can be used for a range of applications ranging from biological to energy application [21][22][23][24]. In particular, DMP was previously used to simulate the flow in cardiovascular [25] and venous valves [11], including the presence of emboli in the blood flow circulation [18].…”