PROJECT SUMMARY The major goal of the Mathematical Modeling Core is to develop mathematical models of biological processes that will help us better understand 1) the link between a Candida species/strain and its metabolic phenotype; 2), the role of commensal bacteria in resisting fungal colonization; and 3) the role of host immune effectors against candidemia (bloodstream infections). The models will integrate data produced by the experimental projects of this Program Project. We will collaborate closely with experimentalists in Projects 1-3 to test our models’ predictions, to use models in designing new experiments and to improve the quality of our models iteratively. As such, we will pursue three specific aims. In Aim 1, we will develop multi-strain genome-scale metabolic networks of Candida albicans (Ca) and Candida parapsilosis (Cp) to identify metabolic mechanisms related to morphological switching, gut colonization and virulence. We will utilize data (whole genome sequences, BIOLOG profiling, morphology and mouse colonization phenotypes from Ca and Cp) to generate multi-strain genome reconstructions of Candida using flux balance analysis to compute metabolic fluxes. Model predictions will be validated with fluxes measured experimentally by stable isotope tracing and refined iteratively. In Aim 2, we will develop a mechanistic model of intestinal microbiome ecology to identify microbiome mechanisms of resistance against Candida colonization. We will incorporate data generated from Project 3 (including metagenomic and metabolomic data from preclinical models and a human cohort) to create mechanistic models of gut microbiota cross-feeding that will be expanded using whole genome reconstruction networks of Lachnospiraceae to study the metabolites produced and consumed by and their role in resisting Ca/Cp colonization. In Aim 3, we will model the dynamics of host immune system to identify effectors against bloodstream translocation. In synergy with Project 2 (utilizing human cohort and preclinical data), we will generate two models: an immunity-microbiome interaction model and an immunity-Candida translocation model. The immunity-microbiome interaction model will use Bayesian inference with large patient datasets to pinpoint the microbiota components most relevant for immune effector dynamics. The immunity-Candida model will be developed using mouse data, by colonizing different levels of Ca and Cp to measure immune cell counts when translocation occurs or not. The two models will be combined to study the links between the gut microbiome and Candida translocation via the immune effector dynamics.