Digestive System Dynamics in Molecular Communication Perspectives

TL;DR

This paper models the human digestive system as a molecular communication network, using advection-diffusion and reaction mechanisms to analyze starch digestion dynamics and individual physiological impacts.

Contribution

It introduces a novel molecular communication-based model of digestion, integrating communication metrics to analyze starch digestion and individual variability.

Findings

Long gastric emptying time increases digestion delay.

Higher enzyme activity reduces path loss in starch hydrolysis.

Simulation results suggest personalized digestion insights.

Abstract

Consumption of food in excess of the required optimal nutritional requirements has already resulted in a global crisis and this is from the perspective of human health, such as obesity, as well as food waste and sustainability. In order to minimize the impact of these issues, there is a need to develop novel innovative and effective solutions that can optimally match the food consumption to the demand. This requires accurate understanding of the food digestion dynamics and its impact on each individual's physiological characteristics. This study proposes a model to characterize digestive system dynamics by using concepts from the field of Molecular Communications (MC), and this includes integrating advection-diffusion and reaction mechanisms and its role in characterizing the digestion process as a communication system. The model is then used to explore starch digestion dynamics by using communication system metrics such as delay and path loss. Our simulations found that the long gastric emptying time increases the delay in starch digestion and in turn the glucose production and absorption into the blood stream. At the same time, the enzyme activity on the hydrolyzed starch directly impacts the path loss, as higher reaction rates and lower half saturation concentration of starch results in lower path loss. Our work can lead to provide insights formulated for each individuals by creating a digital twin digestion model