Unification of cell-scale metabolic activity with biofilm behavior by integration of advanced flow and reactive-transport modeling and microfluidic experiments
Bibliographic record
Abstract
Abstract The bacteria Geobacter sulfurreducens (GS) is a promising candidate for broad applications involving bioelectrochemical systems (BES), such as environmental bioremediation and energy production. To date, most GS studies have reported biofilm-scale metrics, which fail to capture the interactions between cells and their local environments via the complex metabolism at the cellular level. Moreover, the dominance of studies considering diffusion-only molecular mass transport models within the biofilm has ignored the role of internal advection though the biofilm in flow BES. Among other things, this incomplete picture of anode-adhered GS biofilms has led to missed opportunities in optimizing the operational parameters for BES. To address these gaps, we have modernized a GS genome-scale metabolic model (GEM) and complemented it with local flow and reactive-transport models (FRTM). We tuned certain interactions within the model that were critical to reproducing the experimental results from a pure-culture GS biofilm in a microfluidic bioelectrochemical cell under precisely controlled conditions. The model provided insights into the role of mass transport in determining the spatial availability of nutrient molecules within the biofilm. Thus, we verified that fluid advection within biofilms was significantly more important and complex than previously thought. Coupling these new transport mechanisms to GEM revealed adjustments in intracellular metabolisms based on cellular position within the biofilm. Three findings require immediate dissemination to the BES community: (i) Michaelis-Menten kinetics overestimate acetate conversion in biofilm positions where acetate concentration is high, whereas Coulombic efficiencies should be nearly 10% lower than is assumed by most authors; (ii) unification of the empirically observed flow sensitivity of biofilm-scale kinetic parameters and cell-scale values are finally achieved; and (iii) accounting for advection leads to estimations of diffusion coefficients which are much lower than proposed elsewhere in the literature. In conclusion, in-depth spatiotemporal understanding of mechanisms within GS biofilm across relevant size scales opens the door to new avenues for BES optimization, from fine-scale processes to large-scale applications, including improved techno-economic analyses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".