Mathematical Modeling of Microbial Community Dynamics

Microbial communities act as complex ecological networks, crucial in diverse natural and engineered settings. These communities consist of bacteria, fungi, viruses, and other microorganisms that interact with one another and their environment. They perform essential functions such as nutrient cycling, degradation of pollutants, and maintaining the health of plants, animals, and humans. Systems biology leverages mathematical models to study these communities in a systematic and quantitative manner. Our work concentrates on calibrating nonlinear models in Ordinary Differential Equations (ODEs), evaluating both structural and practical identifiability properties to verify if model parameters can be uniquely estimated from the available data. This is crucial to ensure model reliability, enhancing our understanding of how microbial ecosystems work, and providing us with tools for optimal design and management of microbial ecosystems for applications in health and environmental science.

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Publication Details

Published
2024-11-01
DOI
https://doi.org/10.17979/spudc.9788497498913.35
Citations
1
Primary Topic
Mathematical Biology Tumor Growth
Type
article
Field-Weighted Citation Impact
0.00

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article

Mathematical Modeling of Microbial Community Dynamics

Ángel Paredes, Eva Balsa‐Canto, Julio R. Banga
1 citations
Mathematical Biology Tumor Growth
article

Mathematical Modeling of Microbial Community Dynamics

Ángel Paredes, Eva Balsa‐Canto, Julio R. Banga
article en
1 citations

Abstract

Microbial communities act as complex ecological networks, crucial in diverse natural and engineered settings. These communities consist of bacteria, fungi, viruses, and other microorganisms that interact with one another and their environment. They perform essential functions such as nutrient cycling, degradation of pollutants, and maintaining the health of plants, animals, and humans. Systems biology leverages mathematical models to study these communities in a systematic and quantitative manner. Our work concentrates on calibrating nonlinear models in Ordinary Differential Equations (ODEs), evaluating both structural and practical identifiability properties to verify if model parameters can be uniquely estimated from the available data. This is crucial to ensure model reliability, enhancing our understanding of how microbial ecosystems work, and providing us with tools for optimal design and management of microbial ecosystems for applications in health and environmental science.

Agencia Estatal de Investigación
Openalex Percentile: Top 72%
Mathematical Biology Tumor Growth
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Mathematical Modeling of Microbial Community Dynamics — Ángel Paredes, Eva Balsa‐Canto, et al. · (2024) | TGRS Research Map | TGRS