Speaker
Description
Microorganisms are among the most successful forms of life on our planet. They are omnipresent, even in and around our bodies. For example, the oral cavity is home to hundreds of indigenous species of microorganisms that form complex communities known as biofilms. These biofilms can cause infections, particularly when foreign objects, such as implants, are introduced into a patient’s body. Oral implants are especially vulnerable, as part of the implant must protrude into the oral cavity to provide a base for the artificial tooth. In the worst case, these biofilms can lead to peri-implantitis, an infection of the gums and bones, which can result in bone loss and subsequent implant failure.
To prevent such diseases, it is crucial to understand and model the behavior and interactions within the biofilm. In this context, in silico experiments are a valuable complement to in vitro and in vivo studies. In this work, we present a growth model capable of simulating a wide range of interactions between different species within the biofilm. Typically, in the literature, interactions between species are described by introducing additional state variables along with some ad-hoc expressions in the mathematical model. However, this approach results in a computationally expensive model. In contrast, the approach used in this study employs an interaction matrix that describes how species interact with one another, eliminating the need for extra variables. Furthermore, the model includes nutrients and antibiotics as input parameters. The interactions between different species can thus be investigated with a changing nutrient supply or in the case of a start or stop of treatment with antibiotics. The model hereby predicts the growth induced by the availability of nutrients and other species serving as such and the death due to either antibiotics, a scarcity of space, or the presence of other species.
The model is derived from the Hamiltonian principle and, as such, automatically satisfies the laws of thermodynamics. Using such a continuum framework for deriving governing equations provides a powerful and efficient way to model complex systems at a macroscopic level.
