Data Driven Modeling of the Reactive Oxygen Species Stimulated by Photon Energy in Light Therapies

Abstract

Light therapies can be used to treat fungal infections. A general mechanism is attributed to the generation of cytotoxic reactive oxygen species (ROS) due to light stimulation. The effectiveness of these therapies has been widely studied in the literature via conducting biological experiments, where fungi are exposed to light with various wavelengths and power. However, despite the large amount of work reporting the experimental results, few efforts have been given to build a mathematical model that describes the amount of generated ROS as a function of the photon energy and power of the stimulating light. The lack of such a model still hinders the optimization of the light doses. In this work, we propose a novel modeling method based on experimental data, so as to establish a mathematical relationship between the ROS concentration and the stimulating photon energy and light fluence (energy density). The anti-fungal experiments were performed on Candida albicans (C. albicans) using four LED light sources with different wavelengths ranging from 385nm to 450nm. Both the viability of the fungi and the ROS concentration therein were measured during the experiments. High fitting accuracy has been achieved by the model, which therefore demonstrates the effectiveness of the proposed modeling techniques.

Keywords

• Biomedical engineering
• data driven modeling
• light-emitting diodes
• light therapy
• parameter estimation