# Mathematical Modeling Core

> **NIH NIH P01** · SLOAN-KETTERING INST CAN RESEARCH · 2024 · $398,250

## Abstract

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.

## Key facts

- **NIH application ID:** 10763695
- **Project number:** 1P01AI179406-01
- **Recipient organization:** SLOAN-KETTERING INST CAN RESEARCH
- **Principal Investigator:** Joao Xavier
- **Activity code:** P01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $398,250
- **Award type:** 1
- **Project period:** 2024-04-01 → 2029-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10763695

## Citation

> US National Institutes of Health, RePORTER application 10763695, Mathematical Modeling Core (1P01AI179406-01). Retrieved via AI Analytics 2026-06-22 from https://api.ai-analytics.org/grant/nih/10763695. Licensed CC0.

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