[Paper Review] Computation Of Microbial Ecosystems in Time and Space (COMETS): An open source collaborative platform for modeling ecosystems metabolism

Ilija Dukovski, Djordje Bajić+11 more|arXiv (Cornell University)|Sep 3, 2020

Microbial Metabolic Engineering and Bioproduction101 references15 citations

TL;DR

COMETS 2 is an open-source, user-friendly platform for simulating microbial ecosystems in time and space using genome-scale metabolic modeling enhanced with spatial diffusion, biomass expansion, evolutionary dynamics, and extracellular enzyme activity. It integrates with COBRA tools via Python and MATLAB interfaces, enabling researchers to model complex microbial community behaviors across diverse biomes with high biological fidelity and scalability.

ABSTRACT

Genome-scale stoichiometric modeling of metabolism has become a standard systems biology tool for modeling cellular physiology and growth. Extensions of this approach are also emerging as a valuable avenue for predicting, understanding and designing microbial communities. COMETS (Computation Of Microbial Ecosystems in Time and Space) was initially developed as an extension of dynamic flux balance analysis, which incorporates cellular and molecular diffusion, enabling simulations of multiple microbial species in spatially structured environments. Here we describe how to best use and apply the most recent version of this platform, COMETS 2, which incorporates a more accurate biophysical model of microbial biomass expansion upon growth, as well as several new biological simulation modules, including evolutionary dynamics and extracellular enzyme activity. COMETS 2 provides user-friendly Python and MATLAB interfaces compatible with the well-established COBRA models and methods, and comprehensive documentation and tutorials, facilitating the use of COMETS for researchers at all levels of expertise with metabolic simulations. This protocol provides a detailed guideline for installing, testing and applying COMETS 2 to different scenarios, with broad applicability to microbial communities across biomes and scales.

Motivation & Objective

• To develop a scalable, open-source platform for simulating microbial ecosystems that accounts for spatial organization and dynamic metabolic interactions.
• To extend dynamic flux balance analysis by incorporating realistic biomass expansion and molecular diffusion in structured environments.
• To integrate evolutionary dynamics and extracellular enzyme activity into a unified metabolic modeling framework.
• To provide accessible, well-documented interfaces in Python and MATLAB for researchers of all expertise levels.
• To enable broad application across microbial communities in diverse ecological and biotechnological contexts.

Proposed method

• Extends dynamic flux balance analysis (dFBA) with spatially resolved diffusion of metabolites and cells in a 2D or 3D grid-based environment.
• Models microbial biomass expansion using a biophysically accurate representation of cell growth and mechanical constraints.
• Incorporates extracellular enzyme secretion and activity, enabling simulation of substrate degradation and nutrient release in space.
• Introduces evolutionary dynamics via mutation and selection in a population-level framework, allowing adaptation to environmental conditions.
• Uses COBRA-compatible metabolic models as input, ensuring compatibility with established genome-scale models.
• Provides modular, extensible architecture with Python and MATLAB APIs for simulation setup, execution, and analysis.

Experimental results

Research questions

• RQ1 How can genome-scale metabolic models be extended to simulate microbial communities in spatially structured environments?
• RQ2 What role does spatial organization play in shaping metabolic interactions and community stability in microbial ecosystems?
• RQ3 How do extracellular enzyme activities influence nutrient availability and community composition over time?
• RQ4 To what extent can evolutionary dynamics be integrated into dynamic metabolic simulations to predict adaptation in microbial communities?
• RQ5 Can a unified, open-source platform enable reproducible and scalable modeling of microbial ecosystems across diverse biomes?

Key findings

• COMETS 2 successfully simulates microbial community dynamics in spatially structured environments with realistic biomass expansion and diffusion.
• The inclusion of extracellular enzyme activity enables accurate modeling of substrate utilization and nutrient cycling in heterogeneous environments.
• Evolutionary dynamics in COMETS 2 allow for the emergence of adaptive traits, such as enzyme secretion, under selective pressure.
• The platform demonstrates compatibility with COBRA models and supports reproducible simulations across diverse microbial community configurations.
• User-friendly interfaces and comprehensive documentation significantly lower the barrier to entry for researchers without advanced programming expertise.
• The platform enables simulation of complex, multi-species ecosystems at biologically relevant scales, supporting applications in synthetic ecology and environmental microbiology.