Development of a Computational Model of Abscess Formation

Abstract

In some bacterial infections, the immune system cannot eliminate the invading pathogen. In these cases, the invading pathogen is successful in establishing a favorable environment to survive and persist in the host organism. For example, S. aureus bacteria survive in organ tissues employing a set of mechanisms that work in a coordinated and highly regulated way allowing: (1) efficient impairment of the immune response; and (2) protection from the immune cells and molecules. S. aureus secretes several proteins including coagulases and toxins that drive abscess formation and persistence. Unless staphylococcal abscesses are surgically drained and treated with antibiotics, disseminated infection and septicemia produce a lethal outcome. Within this context, this paper develops a simple mathematical model of abscess formation incorporating characteristics that we judge important for an abscess to be formed. Our aim is to build a mathematical model that reproduces some characteristics and behaviors that are observed in the process of abscess formation.

Mathematical Models for Immunology: Current State of the Art and Future Research Directions

“… 2012 ), or the formation of micro-abscesses following bacterial infection (Pigozzo et al. 2012 ). Next we discuss in more detail two mathematical models that emphasise the lack of data (at tissue level) to parametrise models, and the potential use of mathematical techniques (e.g., asymptotic analysis) to gain a deeper understanding of the transitions between different regimes in the dynamics of a biological system.…”

A Validated Mathematical Model of the Cytokine Release Syndrome in Severe COVID-19

“…We did not include in the model infected and non-infected epithelial cells because the model would be more complex, with more constants to adjust and, the worst, without data available to validate these cell populations along time. The use of implicit antigen replication does not affect the quality of the result, more specifically those related to the virus population, as our previous works have demonstrated (Bonin et al, 2018;Pigozzo et al, 2018;Bonin et al, 2019;Reis et al, 2019).…”