Fig 1.
Overview of steady-state, dynamic, and spatiotemporal tools to model microbial communities.
The division among steady-state, dynamic, and spatiotemporal tools is not firm since dynamic tools can be used to describe steady-state systems. Likewise, spatiotemporal tools can be used to describe dynamic and steady-state systems. However, most tools were specifically designed to be used for the highest dimensional cases.
Fig 2.
Qualitative assessment of the static tools/approaches.
The colored squares indicate the evaluation of the specified feature in every tool/approach. The color scale (upper right) goes from excellent (blue) to inadequate (red). When a feature does not apply to the specified tool/approach or the feature was not evaluated, it is indicated as NA (Not applicable; grey). The metrics contained in the figure were inspired by [47]. Colored squares with ‘*’ indicate features of OptCom evaluated using the OptCom function from MICOM. Colored squares with ‘**’ indicate that the evaluation is an assumption based on the given information. Scom: SteadyCom; MMT: Microbiome Modelling Toolbox. The tools/approaches are ordered by the year of the latest publication (e.g., MMT).
Fig 3.
Comparison of tool predictions to experimental data from the study of Diender et al. [9].
(A) Community and species growth rate and (B) Steady-state production rates of the main fermentation products obtained in the fermentation of CO by C. autoethanogenum and C. kluyveri with the assessed tools. cFBA shows the average fluxes and standard deviation of the samples used with Flux sampling.
Fig 4.
Qualitative assessment of the dynamic tools.
Colored squares indicate the evaluation of the specified feature in every tool. The color scale (upper right) goes from excellent (blue) to inadequate (red). When a feature does not apply to the specified tool or the feature was not evaluated, it is indicated as NA (Not applicable; grey). The metrics contained in the figure were inspired by [47]. The tools are ordered by year of publication.
Fig 5.
Quantitative assessment of the dynamic tools.
Experimental biomass concentration profiles of E. coli and S. cerevisiae (Panel A and C, respectively). Comparison of tool predictions of biomass concentration profiles with data for E. coli and S. cerevisiae (Panel B and D, respectively) from the study of Hanly and Henson. [73].
Fig 6.
Quantitative assessment of the dynamic tools.
Experimental extracellular metabolite concentration profiles of glucose, xylose, and ethanol (Panel A, C, and E, respectively). Comparison of tool predictions of extracellular metabolite concentration profiles with data for glucose, xylose, and ethanol (Panel B, D, and F, respectively) from the study of Hanly and Henson. [73].
Fig 7.
Qualitative assessment of the spatiotemporal tools.
Colored squares indicate the evaluation of the specified feature in every tool. The color scale (upper right) goes from excellent (blue) to inadequate (red). When a feature does not apply to the specified tool or the feature was not evaluated, it is indicated as NA (Not applicable; grey). The metrics contained in the figure were inspired by [47]. The tools are ordered by year of publication.
Fig 8.
Quantitative assessment of the spatiotemporal tools.
Abundance simulation profiles of E. coli and S. enterica from COMETS (Panel A) and BacArena (Panel B). Note biomasses used per individual organisms were 5 x 109 fg and 3 x 108 fg for E. coli and S. enterica, respectively. Experimental data used was from the study of Harcombe et al. [33].
Fig 9.
Quantitative assessment of the spatiotemporal tools.
Colony spatial distribution over time simulation profiles of E. coli and S. enterica from COMETS (Panel A) and BacArena (Panel B). For Panel B, the red dots represent E. coli and the black dots represent S. enterica. Experimental data used was from the study of Harcombe et al. [33].