Coupling Flux Balance Analysis with Reactive Transport Modeling through Machine Learning for Rapid and Stable Simulation of Microbial Metabolic Switching¶

Hyun-Seob Song^1,2,*, Firnaaz Ahamed^1,†, Joon-Yong Lee^3,†, Christopher C. Henry⁴, Janaka N. Edirisinghe⁴, William C. Nelson³, Xingyuan Chen³, J. David Moulton⁵, Timothy D. Scheibe^3,*

1. Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE
2. Department of Food Science and Technology, Nebraska Food for Health Center, University of Nebraska-Lincoln, Lincoln, NE
3. Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA
4. Division of Mathematics and Computer Science, Argonne National Laboratory, Argonne, IL
5. Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM

Metabolic Network Model of S. oneidensis MR-1¶

We used a genome-scale metabolic network model of the S. oneidensis MR-1 strain, referred to as iMR1_799 in the literature (Ong et al., 2014). The version of this model we used was loaded into KBase by translating all model reactions and compounds to ModelSEED IDs. This translation was done to ensure interoperability with other KBase models, with the ultimate goal of simulating multi-species communities.

The model in KBase was modified in several ways to ensure proper functionality:

1. Reaction Removal: Three reactions, R_EX_TWEEN_20_E, R_EX_GEL_E, and R_EX_CAS_E, were removed from the model due to their large mass influx without balance within KBase.
2. Metabolite Name Change: The metabolite "glycogen" in the model was renamed to "glycogenmonomer" to prevent collision with multimer versions of glycogen in the KBase namespace. This change avoids mass imbalanced reactions in the translated model.

3. Revision of Biomass Production Equation: We updated the biomass production equation by including ATP hydrolysis, which was missing in the original model. The revised equation is:

   $$ \text{c ATP} + \text{c H}_2\text{O} \leftrightarrow \text{c ADP} + \text{c Pi} + \text{c H}^+ $$

where $ \text{c}$ representsrepresents the stoichiometric coefficients of the compounds. Initially, a default value of 40.11 was used for $ \text {c}$ , which was later adjusted during FBA simulations to better fit experimentally measured yield data.

We also changed the name of the "glycogen" species in the model to "glycogenmonomer" to avoid matching the single-unit "glycogen" with the 6-unit glycogen species in KBase, which resulted in mass imbalanced reactions.

The genome associated with this model can be found in this Narrative and the gene_ids that were mapped to biochemical reactions in the model can found under Genes tab in the model table diplayed below.

This model can be downloaded in SBML, tsv, Excel or in JSON format from Narrative data panel¶

There are eight tabs for browsing the data in the model: Overview, Reactions, Compounds, Genes, Compartments, Biomass, Gapfilling, and Pathways.

• Overview — Summary of key information about the model, including the associated genome, number of reactions, and number of compounds.
• Reactions — Detailed reaction information, including reaction ID, name, biochemical equation, the associated gene IDs, and whether or not the reaction was added by the gapfilling stage.
• Compounds — Information about compounds in the model, including chemical formula and charge.
• Genes — Gene IDs and associated reaction IDs.
• Compartments — Subcellular localization of the compounds and enzymes. Typically, there are three types of compartments in microbes: Cytosol (c), -Periplasm (p), and Extracellular (e). Reactions and compounds belonging to each compartment are identified using compartment notation (e.g., rxn00001[c0], cpd00001[c0]). The integer associated with the compartment (e.g., the 0 in c0) represents the index number of the model. For a single-species model, this number will always be zero, but if individual models are merged into a community model, each sub-model will then be assigned a distinct index.
• Biomass — Biomass composition of the model. Typically biomass is represented in the model as an equation where biomass compounds and ATP would make 1 gram of biomass. After clicking on the Biomass tab, the coefficients of each biomass component are listed in the Coefficient column. Negative coefficients represent the compounds on the left side of the biomass equation, and positive coefficients represent the compounds on the right side of the equation.
• Gapfilling — Reactions that were added to fill metabolic gaps resulting from missing or inconsistent annotations. During the gapfilling process, an optimization algorithm adds a minimal number of reactions and compounds to make the biochemical network generate biomass. Currently, this tab does not show anything because gapfilling indiciation was moved to the Reactions tab.
• Pathways — KEGG maps that represent the metabolic network of the model. Click on the name of a map (e.g., TCA cycle) to see the presence or absence of the reactions (blue).

Genome S. oneidensis MR-1¶

Citations¶

Ong, W.K., Vu, T.T., Lovendahl, K.N. et al. Comparisons of Shewanella strains based on genome annotations, modeling, and experiments. BMC Syst Biol 8, 31 (2014). https://doi.org/10.1186/1752-0509-8-31