Microbial communities play significant roles in biological systems, from the Earth's ecosystems to the human body, but the current understanding of the biological principles regarding the formation, structures, functions, and evolution of these communities is still lacking. Computational modeling of microbial communities aims to describe and predict the interactions within these communities as well as between these communities, other organisms, and the environment using mathematical frameworks. This allows the integration of experimental data and systematic validation of biological hypotheses. Ecological modeling and genome-scale metabolic (GSM) modeling are two primary approaches applied to modeling microbial communities. Ecological modeling focuses on species abundances and how they change over time given the ecological relationships between species inferred from metagenomic data. Important properties of the microbial communities such as stability and their dependence on system parameters such as the network structure of the communities can be analyzed. GSM modeling takes advantage of the huge amount of knowledge accumulated in biological databases over the last two decades regarding metabolites and metabolic reactions that constitute microbial metabolism. Microbial growth is represented by the ability of a cell to synthesize its own constituent molecules. Interactions between microbes can be predicted at the metabolic level.
|Original language||English (US)|
|Title of host publication||Systems Biology|
|Number of pages||27|
|State||Published - May 12 2017|
All Science Journal Classification (ASJC) codes
- Computer Science(all)