Image Data driven Modeling of Candida Infection Dynamics in Mouse Skin with a Simplified Immune Response

Candida is a common fungal pathogen, presenting a significant challenge in clinical treatment. Understanding its infection dynamics in host tissue can deepen our knowledge of this pathogen, and facilitate developing counteractive strategies. Despite the existing literatures on modeling fungal growth, modeling the infection deep into host tissue under the intervention of immune responses has not yet received sufficient attention. To fulfill this task, this study proposes a mathematical model that can be fitted to the image data extracted from the skin tissue sections of experimental mice.

To enable this modeling based on the limited information extractable from tissue section images, an important as sumption is made on the densities of neutrophils and T cells, which are homogeneous at various depths of a tissue section. This simplifies the complex interactions between the fungi and host immune defenses, and avoids measuring the densities of different immune cells at various depths, which are challenging in specific cell staining.

Tissue section images were obtained from mouse models infected with Candida albicans (C. albicans) on their back skin. These images were processed to calculate the fungal densities at various depths from the skin surface. These density data were used in estimating the model parameters via particle swarm optimization. With the fitted model, the fungal infection over time at different depths in the host tissue was simulated and compared with the experimental data.

The simulation results demonstrated the effectiveness of the proposed model in replicating the infection dynamics of especially C. albicans.

It is the first attempt of building a model based on tissue section images to describe fungal infection deep into epidermis, under the interaction of a simplified immune function. This model can be usedto predict infection processes, and so as to facilitate the development therapeutic strategies against localized fungal infection.