Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Modeling with Differential Equations01:25

Modeling with Differential Equations

Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...
Introduction to the Human Microbiota01:22

Introduction to the Human Microbiota

Microorganisms colonize various regions of the human body, including the mouth, nasal passages, throat, stomach, intestines, urogenital tract, and skin. The total number of microbial cells is estimated to range from 10¹³ to 10¹⁴—comparable to, or exceeding, the number of human somatic cells. This host–microbiome relationship has led to the conceptualization of humans as supraorganisms, wherein microbial communities perform vital roles in development, immunity, and disease...
Operon Model01:23

Operon Model

The operon model represents a fundamental mechanism of gene regulation in prokaryotes, enabling coordinated expression of genes involved in related metabolic or functional pathways. Operons consist of structural genes, a promoter, and an operator, with transcription regulated by repressors, activators, and small effector molecules.Structure and Function of OperonsAn operon is a cluster of structural genes transcribed together under the control of a single promoter. The promoter region...
Development of Human Microbiota01:30

Development of Human Microbiota

The human microbiota begins developing at birth and undergoes continual change as we age. Infancy marks a critical period of microbial sensitivity, offering a “window of opportunity” during which beneficial microbes help mature the immune system. By age three, children typically develop a more stable and diverse microbial community. Newborns acquire microbes from their immediate environment; vaginal delivery favors maternal vaginal microbes, while cesarean births favor microbes from the skin...
Marine Microbial Ecology01:30

Marine Microbial Ecology

Marine microbial ecosystems are shaped by distinct physicochemical limits, including high salinity, low nutrient availability, and fluctuating oxygen levels. These conditions favor smaller microbial cell sizes, which maximize their surface-to-volume ratio for efficient nutrient uptake.Microbial activity and community composition are closely linked to biogeochemical cycles, particularly in dynamic environments like estuaries, where halotolerant microbes thrive in response to variable salinity...
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

ADAM-1: An AI Reasoning and Bioinformatics Model for Alzheimer's Disease Detection and Microbiome-Clinical Data Integration.

IEEE access : practical innovations, open solutions·2025
Same author

Ec W: A Novel Narrow-Spectrum Class IIb Microcin from Escherichia coli.

Probiotics and antimicrobial proteins·2025
Same author

Novel class IIb microcins show activity against Gram-negative ESKAPE and plant pathogens.

eLife·2024
Same author

Succinate-producing microbiota drives tuft cell hyperplasia to protect against Clostridioides difficile.

The Journal of experimental medicine·2024
Same author

Commensal consortia decolonize Enterobacteriaceae via ecological control.

Nature·2024
Same author

Expanding the toolbox: Novel class IIb microcins show activity against Gram-negative ESKAPE and plant pathogens.

bioRxiv : the preprint server for biology·2024

Related Experiment Video

Updated: May 26, 2026

Updated Protocol for the Assembly and Use of the Minibioreactor Array (MBRA)
09:38

Updated Protocol for the Assembly and Use of the Minibioreactor Array (MBRA)

Published on: September 5, 2025

Dynamical Systems-Constrained Metabolic Modeling Enables Forecasting of Host-Microbiome Dynamics.

Hayden Gallo1, Vanni Bucci2

  • 1UMass Chan Medical School.

Biorxiv : the Preprint Server for Biology
|May 25, 2026
PubMed
Summary

We developed a new computational framework, Dynamical Systems Constrained Metabolic Modeling (DySCoMeMo), to predict microbial and metabolite dynamics in microbiome-host ecosystems. This method accurately forecasts community changes and identifies key species, advancing ecological and metabolic modeling.

Keywords:
Microbiomehost–microbe interactionsmathematical modeling

More Related Videos

Bioreactor Assembly for Continuous Culture of Complex Fecal Communities
09:37

Bioreactor Assembly for Continuous Culture of Complex Fecal Communities

Published on: April 25, 2025

Characterizing Microbiome Dynamics – Flow Cytometry Based Workflows from Pure Cultures to Natural Communities
09:57

Characterizing Microbiome Dynamics – Flow Cytometry Based Workflows from Pure Cultures to Natural Communities

Published on: July 12, 2018

Related Experiment Videos

Last Updated: May 26, 2026

Updated Protocol for the Assembly and Use of the Minibioreactor Array (MBRA)
09:38

Updated Protocol for the Assembly and Use of the Minibioreactor Array (MBRA)

Published on: September 5, 2025

Bioreactor Assembly for Continuous Culture of Complex Fecal Communities
09:37

Bioreactor Assembly for Continuous Culture of Complex Fecal Communities

Published on: April 25, 2025

Characterizing Microbiome Dynamics – Flow Cytometry Based Workflows from Pure Cultures to Natural Communities
09:57

Characterizing Microbiome Dynamics – Flow Cytometry Based Workflows from Pure Cultures to Natural Communities

Published on: July 12, 2018

Area of Science:

  • Microbiome research
  • Systems biology
  • Computational biology

Background:

  • Forecasting microbiome-host ecosystem dynamics at compositional and functional levels is challenging.
  • Dynamical systems models (DSMs) predict community composition, while constraint-based metabolic models (CBMMs) estimate metabolic fluxes.
  • Existing frameworks lack integration for time-resolved predictions of both microbial abundances and metabolite dynamics from ecological data alone.

Purpose of the Study:

  • Introduce the Dynamical Systems Constrained Metabolic Modeling (DySCoMeMo) framework.
  • Integrate ecological DSMs with CBMMs for mechanistically grounded, time-resolved forecasts.
  • Predict temporal dynamics of biomass and metabolites in microbial communities and hosts.

Main Methods:

  • DySCoMeMo integrates DSMs with CBMMs.
  • It uses parameters from DSMs applied to microbiome time series data to constrain metabolic modeling over time.
  • This bridges ecological interaction networks with genome-scale metabolic modeling.

Main Results:

  • DySCoMeMo predicts in vitro community and metabolite dynamics with superior or on-par accuracy compared to existing methods.
  • The framework generalizes to in vivo data, accurately forecasting dynamics during dietary perturbations, including host metabolism.
  • DySCoMeMo uniquely identifies keystone species by quantifying their metabolic contributions.

Conclusions:

  • DySCoMeMo establishes a generalizable, mechanistically grounded framework for time-resolved forecasting of microbiome-host microbial and metabolic dynamics.
  • It bridges ecological interaction inference with genome-scale metabolism of communities.
  • This work provides a novel approach for understanding and predicting complex ecosystem behaviors.