Table 2.
Constraint-based computational tools for modelling microbial communities.
| Computational Methods | Input data | Community Size | Objective Function | Predictions |
|---|---|---|---|---|
| Steady-state modelling | ||||
| cFBA | GEMs | Small | Maximisation of community growth rate | Predicts species abundances and identifies cross-feeding metabolites |
| OptCom | GEMs | Small | Multi-objective optimisation, where the inner problem is maximisation of species-level growth and the outer problem is maximisation of community growth | Predicts inter-species metabolite transfers |
| MMinte | Operational Taxonomic units (OTUs) & FASTA file with 16S rDNA sequences | Large | Maximisation of community growth rate | Reconstructs metabolic models and predicts growth rate Generates interaction networks |
| SteadyCom | GEMs | Large | Maximisation of community growth rate | Predict composition (species abundances) of microbial community in a given environment |
| RedCom | Elementary Flux Vectors (EFVs) | Large | Maximisation of community growth rate | Predicts feasible ranges for metabolite exchange rates and product yields |
| Microbiome Modelling Toolbox (MMT) | GEMs and microbial Metagenomic data | Large | Maximisation of community growth rate | Predicts metabolic profiles in pairwise as well as larger microbial communities |
| CarveMe | Genome FASTA files | Large | Maximisation of community growth rate | Reconstruction and gap-filling of single-species metabolic models. Generate microbial community models from single species |
| Dynamic modelling | ||||
| BacArena | GEMs | Greater than 2 species | Individual-based modelling with FBA where Biomass maximisation is objective | Predict cross-feeding interactions Metabolic turn over using metabolite concentrations as constraints |
| COMETS | GEMs, media and spatial structure simulation parameters such as mutRate that represent mutations | Greater than 2 species | Population based-modelling where maximisation of biomass is the objective | Outputs can be from all or selected time steps. Predicts biomass spatial distribution for each simulation grid cell. Tracks specific metabolites on the spatial grid |
| µbialSim | GEMs | Large | Dynamic FBA, both batch and chemostat operations are simulated | Simulation of microbiomes, where metabolite exchange is the primary means of interaction |
| FLYCOP | GEMs | Small | Multiple objectives such as maximise growth, yield, metabolite production, minimise time to reach stationary phase etc | Predict ideal consortium configuration depending on the optimisation goal |