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.
Candida albicans is an opportunistic fungus able to form pathogenic mucosal or titanium surface biofilms when growing in heterogeneous communities with commensal bacteria. It is an important cause of both topical and systemic fungal infection in humans, in particular immunocompromised patients. It is also responsible for 85-95% of all vaginal infections resulting in doctor visits. C. albicans biofilms form on mucosal surfaces (e.g., oral, GI tract, vaginal) but also on the surface of implantable medical devices, and are a major cause of nosocomial infections. These heterogeneous microbial biofilm communities are often difficult to eradicate due to resistance to antimicrobials and high incidence of recurrence. This project will develop, validate, and apply mathematical models and algorithms for the systematic design of interventions in the composition of these biofilms in order to control fungal growth.
Laubenbacher R, Niarakis A, Helikar T, An G, Shapiro B, Malik-Sheriff RS, Sego TJ, Knapp A, Macklin P, Glazier JA. Building digital twins of the human immune system: toward a roadmap. npj Digit. Med. 2022 May 20;5,64. doi: 10.1038/s41746-022-00610-z.
Sordo Vieira L, Laubenbacher R. Computational models in systems biology: standards, dissemination, and best practices. Curr Opin Biotechnol. 2022 Jun;75:102702. doi: 10.1016/j.copbio.2022.102702. Epub ahead of print.
Knapp AC, Sordo Vieira L, Laubenbacher R, Chifman J. SteadyCellPhenotype: A web-based tool for the modeling of biological networks with ternary logic. Bioinformatics. 2022 Feb 18. doi: 10.1093/bioinformatics/btac097. Epub ahead of print.
Koshy-Chenthittayil, Sherli, Pedro Mendes, and Reinhard Laubenbacher. 2021. “Optimization of Agent-Based Models Through Coarse-Graining”. Letters in Biomathematics8 (1), 167–178. https://lettersinbiomath.journals.publicknowledgeproject.org/index.php/lib/article/view/431.
Masison, J., J. Beezley, Y. Mei, H. a. L. Ribeiro, A. C. Knapp, L. Sordo Vieira, B. Adhikari, et al. “A Modular Computational Framework for Medical Digital Twins.” Proceedings of the National Academy of Sciences 118, no. 20 (May 18, 2021). https://doi.org/10.1073/pnas.2024287118.
Wooten, David J., Jorge Gómez Tejeda Zañudo, David Murrugarra, Austin M. Perry, Anna Dongari-Bagtzoglou, Reinhard Laubenbacher, Clarissa J. Nobile, and Réka Albert. “Mathematical Modeling of the Candida Albicans Yeast to Hyphal Transition Reveals Novel Control Strategies.” Preprint. Systems Biology, January 20, 2021. https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008690
L. Sordo Vieira, R. Laubenbacher, and D. Murrugarra, Control of Intracellular Molecular Networks Using Algebraic Methods, Bull. Math. Biol., 82(2), 2019.