Postdoc position for Agent-Based Simulations of mixed species biofilm formation.

Posted on: March 26, 2018.

A NIH grant was awarded to Mendes, Laubenbacher, and Dongari-Bagtzoglou to research formation of biofilms composed of Candida albicans and bacterial species using Agent-Based Simulation. We are now recruiting a modeler to work on this project.

POSTDOCTORAL RESEARCH POSITION IN AGENT-BASED MODELING OF MULTISPECIES MICROBIAL BIOFILMS

The Mendes and Laubenbacher Research Groups, at the Center for Quantitative Medicine at UConn Health, have an immediate opening for a postdoctoral researcher to join an interdisciplinary team focused on an NIH-funded project to develop novel approaches to the control of heterogeneous multispecies microbial biofilms. The project team combines expertise in mathematical modeling and control, biochemistry, and microbial ecology. The team includes the laboratory of Dr. Dongari-Bagtzoglou in the UConn School of Dental Medicine.

The position is for two years, with the possibility of an extension. The researcher to fill this position will play an important leadership role in the development of the mathematical modeling and control component of the project. More details of the project at the bottom of this page.

Required expertise for the position includes:

Desired expertise includes:

To apply for this position please email your full CV to Dr. Pedro Mendes pmendes@uchc.edu.

General information about Postdoc positions at UConn Health.

UConn Health is an affirmative action employer in addition to an EEO and M/F/V/PwD employer.

Project summary

The project addresses an important biomedical problem: how to control biofilms formed by Candida albicans, a dimorphic fungus that is an important cause of both topical and systemic fungal infection in humans, in particular immunocompromised patients. It is responsible for 85-95% of all vaginal infections resulting in doctor visits. C. albicans biofilms also form on the surface of implantable medical devices, and are a major cause of nosocomial infections. In recent years, it has been recognized that interactions with bacterial species integrated into biofilms can affect C. albicans virulence and other properties, It is therefore important to understand the interactions of C. albicans with bacterial species, in particular metabolic interactions. The next step then is to understand and, ultimately, control how varying compositions of the different microbial species affect their metabolic state and their ability to form biofilms. This project approaches the problem through model-based design of optimal compositions of the bacterial species for control of fungal growth. This will be accomplished through a combination of the construction of a novel computational model of a heterogeneous biofilm consisting of bacterial as well as fungal species, and novel mathematical tools for dimension reduction and optimization.

The outcome of the project will be a better understanding of the relationship between bacterial and fungal species in a biofilm and its therapeutic potential through the construction of a predictive agent-based computational model. Another outcome will be a mathematical tool that enables the use of mathematical models for the purpose of designing optimal controls for fungal growth in heterogeneous biofilms. The applicability of the results of this project extends far beyond biofilms into all areas of medicine and healthcare that are amenable to agent-based modeling, such as studies of the human microbiome.