Staphylococcus aureus and Pseudomonas aeruginosa are two species of bacteria that are involved in numerous conditions, including lung infections and chronic wound infections. The aim of this project was to study the short-term interactions that occur when P. aeruginosa first encounters an established S. aureus colony, which it then seeks to break apart whilst mixing with S. aureus. Limoli et al. have studied these interactions using experiments, and have thus identified several key aspects involved in these interactions, such as the mechanisms that P. aeruginosa employs to approach the S. aureus colony. The means by which we intended to study interactions between S. aureus and P. aeruginosa is a model that was made by previous members of the Idema group and that was based on the experiments by Limoli et al. In this report, we discuss this model and the biological background relevant to it. We also document the problems that we encountered while trying to run simulations using an existing implementation of this model.

Multivalent interactions are crucial mechanisms employed by cells to respond to their environment, often leading to superselectivity phenomena. Extensive experimental and theoretical efforts have been made to understand the variables controlling superselectivity, but challenges persist in achieving precise control over receptor and ligand numbers in biological systems. To address this, the Laan lab developed a model system using DNA origami, enabling precise manipulation of receptor and ligand numbers at the nanoscale. These nanoscopic (~15 nm) structures can mimic the receptors of a target surface, and the extracellular ligands are represented by a branched star-shaped DNA origami structure in solution. Both structures hold a fluorophore, allowing visualization using total internal reflection microscopy (TIRF) by measuring intensity values of the DNA nanostars absorbed into target surface. In this study, we investigated the effects of altering binding strength and flexibility in DNA nanostar structures on superselectivity. We found that experimental results for replicating previous experiments using the same DNA nanostars (Design A) exhibited minor variations within expected ranges, validating the reliability of the experimental protocol. However, sensitivity analysis highlighted the influence of data points on superselectivity interpretation, emphasizing the need for careful data analysis. Our study also evaluated the impact of introducing sequence mismatches on binding affinity (Design A*), revealing that modifications reducing binding affinity do not necessarily enhance superselective behavior for this system. Additionally, our investigation into the effects of increased flexibility (Design C) revealed unexpected behaviors in bound fraction and cluster formation, suggesting a potential relationship between cluster formation, intensity values registered, and the flexibility of the structure mediated by phase separation. These findings underscore the complexity of DNA nanostar behavior and stress the need for further research to elucidate the factors influencing superselectivity in DNA nanostars. By providing more understanding into how to develop highly selective particles, therapeutic molecules could sharply distinguish between healthy and corrupt cells, leading to customizable treatments with higher efficacy.

Weak multivalent interactions between receptors and ligands can achieve super selectivity due to enhanced binding from multiple arms, enabling them to differentiate between surfaces with varying receptor densities. However, the theoretical understanding of multivalent binding including the effect of receptor depletion, binding kinetics, and the effect of incorporating different ligands within a single nanostar, is limited. This complicates the interpretations of experiments and restricts the therapeutic potential of multivalent particles. This study implements the Gillespie algorithm, to systemically study their binding kinetics, depletion effects, and the effect of incorporating two different ligands within a single nanostar. These simulations demonstrate a significant reduction in coverage, selectivity, and average number of arms bound per particle in equilibrium at the receptor density regime where receptor depletion is significant. Additionally, receptor depletion causes a dynamic competition between bound and unbound nanostars, leading to a decrease in the average number of arms bound over time. The populations of nanostars with i arms bound have a higher population selectivity than the selectivity considering all bound nanostars. Furthermore, incorporating two types of ligands can enhance selectivity. These insights advance our understanding of multivalent systems, offering the potential for incorporating multivalent particles in therapeutic applications.