Evidence map›Paper›PMID 42044106›Full record

ArticlePloS one2026

Systems modeling of a phenazine-producing Escherichia coli bio-battery anolyte.

Simon M King, Michael R King

Abstract read
In one paragraph

Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Simon M KingDepartment of Chemical and Biomolecular Engineering, Rice University, Houston, Texas, United States of America.
Michael R KingDepartment of Bioengineering, Rice University, Houston, Texas, United States of America.ORCID https://orcid.org/0009-0006-4643-3663

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Bioelectrochemical systems that combine living microbes with electrochemical devices are emerging as platforms for sustainable energy and chemical production. Phenazine-based organic redox flow batteries (RFBs) already achieve high-capacity, long-lived negolytes, but their performance is limited by electrolyte degradation. Recent work has demonstrated that genetically engineered, phenazine-producing Escherichia coli can regenerate degraded anolyte species in an operating RFB, suggesting a new class of "bio-batteries." However, there is little quantitative understanding of how microbial physiology, phenazine toxicity, and electrochemical operating conditions jointly constrain performance. Here we develop a zero-dimensional mechanistic model that couples an engineered E. coli chassis to a phenazine-based flow battery half-cell. The microbial module includes Monod-type substrate uptake, growth-associated phenazine biosynthesis, first-order phenazine degradation, and a Hill-type toxicity term informed by pyocyanin's micromolar-scale inhibitory effects. The electrochemical module computes Nernstian anode potentials from reduced/oxidized phenazine, adds a lumped area-specific resistance, and explicitly simulates charge discharge cycles with voltage cut-off criteria. We then sweep phenazine production strength, initial biomass loading, toxicity, and mediator stability to map the design space. The model predicts a strong trade-off between instantaneous current density and long-term capacity: high phenazine production and biomass can briefly reach 1.5 × 10-3 mA·cm ⁻ ² current densities but rapidly trigger toxicity-driven collapse, whereas more conservative designs deliver low current over an extended time period. When embedded in a simple RFB cycling protocol, moderate-production designs converge to a quasi-steady discharge capacity that is far below that of state-of-the-art phenazine RFBs, but exhibit partial self-regeneration of mediator capacity across cycles. Our results quantify the fundamental constraints of phenazine-based microbial anolytes and highlight engineering priorities, such as enhanced host tolerance and low-toxicity mediators, required for bio-batteries to become competitive or to occupy niche low-power, self-regenerating roles.

Indexed as

Bioelectric Energy SourcesElectric Power SuppliesEscherichia coliModels, BiologicalPhenazinesElectrodesOxidation-ReductionphenazinePhenazines

Identifiers

PMID42044106
PMCPMC13119959

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.