Dynamic-community-fba’s documentation
Welcome to the documentation of dynamic-community-fba. The documentation provides you with a comprehensive guidance on using the dynamic-community-fba Python package. The package was designed to conduct dynamic flux balance analysis (dFBA) in microbial communities and to easily analyze the results. DcFBA aims to accurately model and study the collaborative and competitive behaviors of organisms in a shared environment or community. The extended FBA methodologies incorporate dynamic elements by simulating metabolic exchanges and cross-feeding in microbial consortia over time. By integrating a temporal dimension, these approaches offer a more nuanced view of microbial ecosystems, capturing the evolving interactions and flux distributions that traditional FBA overlooks. This enhancement allows for a deeper understanding of the dynamic nature of microbial communities. By utilizing the Genome Scale Metabolic Models (GSMM’s) of organisms of interest, our package enables you to analyze and explore the intricate interactions among two or more organisms.
The documentation starts off with a quick introduction to CBMPy, providing an overview of the fundamentals for working with GSMMs in Python. With this knowledge we will first give an example on how to dynamically model single organisms using Dynamic FBA (dFBA). Ultimately, we will explore the three distinct approaches offered by this package for the dynamic modeling microbial communities:
One of the key features of dynamic-community-fba is the ability to construct and export a community model, which represents the joint stoichiometry matrix of the provided GSMM’s models. The documentation provides in-depth explanations on how to build and utilize this matrix to study the dynamics of multi-organism interactions and metabolic networks. Furthermore, we describe how you can export the Community Model to the standardized SBML format.
To ensure a smooth start, the documentation includes a section outlining the prerequisites and installation guide. We describe step-by-step instructions on installing the necessary dependencies and setting up the dynamic-community-fba package in their Python environment. Additionally, the documentation highlights the compatibility requirements and recommends best practices for a successful installation. By following the documentation, you will gain a comprehensive understanding of the dynamic-community-fba package and its capabilities.
This documentation serves as a valuable resource for researchers, scientists, and students working in the field of systems biology, metabolic engineering, and microbial ecology. It empowers effective modelling and analysis of dynamic flux balance analyses, facilitating a deeper understanding of the complex interactions between organisms in various biological systems.
This documentation is designed to cater to researchers, scientists, and students who are actively involved in the domains of systems biology, metabolic engineering, and microbial ecology. It serves as a comprehensive resource to kickstart your usage of the package. The primary goal of this package is to streamline the process of dynamic flux balance analysis, enabling more efficient modeling and analysis. Thereby we hope it will help in enhancing our comprehension of intricate interactions between organisms within diverse biological systems. We warmly invite you to delve into this documentation and uncover the ways it can empower your research and studies within these fields.