# Population dynamic models of microbial interactions

> **NIH NIH P20** · UNIVERSITY OF IDAHO · 2020 · $163,515

## Abstract

The association between human microbiomes and health has garnered a great deal of scientific and popular
attention. By allowing rapid and inexpensive characterization of microbial community composition, modern
sequencing has uncovered enormous microbial diversity. Determining the presence versus absence of microbes
is insufficient however; we need to understand how dysfunctional microbiomes form and how to repair them. A
critical step toward the goal of promoting the assembly of microbial communities that support health is to predict
their temporal dynamics. There remains, however, a critical gap: untangling causation from correlation. Simply
stated, we are currently unable to interpret the biological and clinical relevance buried within the extreme
complexity of microbial communities. Our long-term goal is to advance microbiome research by a) developing
new models that capture causality in microbial interactions; and b) developing tools to interpret the relevance of
microbial interactions for human health. Before the development of data analysis pipelines, we need to establish
theoretical underpinnings upon which to base the methods. We have three aims focused on developing such
theory. (1) Develop molecule-mediated models of microbial interactions. Existing statistical approaches for
modeling the temporal dynamics of microbiomes are built on assumptions that are rarely valid for microbial
communities and thus can be profoundly misleading. Misspecified models may mislead researchers toward poor
prediction of dynamics, or worse, prescription of a misguided treatment that enhances rather than inhibits a
microbial species of interest—a major problem if the species of interest is a pathogen. We will assess the
predictive power of statistical time-series models given realistic molecule-mediated interactions in synthetic data
and develop new statistical methods that account for time-varying interactions. (2) Predict stability of a
microbiome. Even when interactions that govern microbial population dynamics are well estimated, these
interactions may not be directly relevant to human health. Rather, we may want to predict higher-level properties
of a microbiome such as its resilience. Resilience—the ability of a microbiome to maintain and recover function
in the face of perturbations such as by antibiotics or opportunistic pathogens—is related to the mathematical
concept of stability. We will develop new measures to capture the resilience of the microbiome. (3) Predict other
high-level microbiome properties. Often a property of the microbiome in its entirety is of interest, such as the
ability to regulate pH or metabolize a toxin. Borrowing from population genetic theory, we will develop novel
mathematical models to predict the temporal dynamics of traits associated with the microbiome. Together, these
aims will greatly enhance our understanding and interpretation of the temporal dynamics of microbial
communities, and lay the foundation for our capacity...

## Key facts

- **NIH application ID:** 10026005
- **Project number:** 2P20GM104420-06A1
- **Recipient organization:** UNIVERSITY OF IDAHO
- **Principal Investigator:** CHRISTOPHER HASKELL REMIEN
- **Activity code:** P20 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $163,515
- **Award type:** 2
- **Project period:** — → —

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10026005

## Citation

> US National Institutes of Health, RePORTER application 10026005, Population dynamic models of microbial interactions (2P20GM104420-06A1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10026005. Licensed CC0.

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