# Unraveling the ecology of intestinal fungal expansion in immunocompromised patients through computational modeling and machine learning

> **NIH NIH K99** · SLOAN-KETTERING INST CAN RESEARCH · 2024 · $122,048

## Abstract

PROJECT SUMMARY/ABSTRACT
CANDIDATE: I am a Postdoctoral Research Associate at Memorial Sloan Kettering Cancer Center (MSKCC).
During my Ph.D. studies, I dedicated myself to developing biology-based mathematical models of bacterial
metabolism. My current research extends my interest from single organisms to microbial communities, with a
particular focus on the human intestinal microbiome. Since joining MSKCC, I have compiled a large
longitudinal microbiome dataset from hospitalized patients who have undergone allogeneic hematopoietic cell
transplantation (allo-HCT). I have also acquired bioinformatic skills and data-driven modeling techniques to
profile microbiome compositions and quantify their associations with clinical outcomes. Built upon this dataset,
my proposed research aligns well with my long-term career goal of establishing an independent laboratory to
elucidate mechanistic links between the intestinal microbiota and infectious diseases. To prepare for my
transition to independence and developing a competitive computational research program, I have developed a
focused career plan to enhance my computational skills in metagenomic/metabolomic data analyses,
community metabolic modeling, and development of neural network models. In parallel, I will improve my soft
skills, including presentation, networking, grantsmanship, mentorship, leadership, and teaching.
RESEARCH: Immunocompromised patients undergoing intensive antimicrobial therapy are at high risk for
developing invasive fungal bloodstream infections (BSIs). Between 2016 and 2020, C. parapsilosis was
responsible for the most breakthrough BSI cases among allo-HCT recipients at MSKCC. Typically, C.
parapsilosis BSI occurs subsequent to its intestinal expansion. This proposal will leverage my mathematical
modeling expertise and the vast microbiome dataset of our allo-HCT cohort to elucidate the ecological
mechanisms underlying intestinal expansion of C. parapsilosis. My central hypothesis is that altered intestinal
metabolic environment enables C. parapsilosis expansion. Specific Aim 1 will identify bacterial secreted
metabolites that inhibit C. parasilosis. In Specific Aim 2, I will investigate the impacts of genomic variations
across different C. parapsilosis isolates on their ability to utilize nutrients and grow in the human intestine. The
Specific Aim 3 will involve building a machine-learning-powered computational framework for the risk
assessment of C. parapsilosis expansion and the rational design of antifungal therapy to reduce the risk.
ENVIRONMENT: I will complete the K99 phase of this grant in the Computational & Systems Biology Program
at MSKCC, a state-of-the-art cancer research institute. My primary mentor, Dr. Joao Xavier, has a proven track
record in mathematical modeling of bacterial microbiomes, while my co-mentor, Dr. Tobias Hohl, is an expert in
fungal mycobiomes and infectious disease. The two labs will jointly provide a rich and complementary training
and research...

## Key facts

- **NIH application ID:** 10807677
- **Project number:** 1K99AI175599-01A1
- **Recipient organization:** SLOAN-KETTERING INST CAN RESEARCH
- **Principal Investigator:** Chen Liao
- **Activity code:** K99 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $122,048
- **Award type:** 1
- **Project period:** 2024-05-23 → 2026-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10807677

## Citation

> US National Institutes of Health, RePORTER application 10807677, Unraveling the ecology of intestinal fungal expansion in immunocompromised patients through computational modeling and machine learning (1K99AI175599-01A1). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10807677. Licensed CC0.

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