Evidence map›Paper›PMID 38398756›Full record

ArticleLife (Basel, Switzerland)2024

Microbial Pathway Thermodynamics: Stoichiometric Models Unveil Anabolic and Catabolic Processes.

Oliver Ebenhöh, Josha Ebeling, Ronja Meyer, Fabian Pohlkotte, Tim Nies

Abstract read
In one paragraph

Article in Life (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

4 citing papers in PubMed.

  1. Microbial Metabolism: Primary and Secondary Metabolites.Progress in molecular and subcellular biology · 2026
    Review
  2. Integrative Thermodynamic Strategies in Microbial Metabolism.International journal of molecular sciences · 2025
    Article
  3. Article
  4. Article
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

5 authors.

Oliver EbenhöhInstitute of Quantitative and Theoretical Biology, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany.ORCID 0000-0002-7229-7398
Josha EbelingInstitute of Quantitative and Theoretical Biology, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany.ORCID 0000-0002-0634-1653
Ronja MeyerInstitute of Quantitative and Theoretical Biology, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany.ORCID 0009-0009-3595-1248
Fabian PohlkotteInstitute of Quantitative and Theoretical Biology, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany.ORCID 0009-0009-5161-6298
Tim NiesInstitute of Quantitative and Theoretical Biology, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany.ORCID 0000-0003-1587-2971

Funding

Deutsche Forschungsgemeinschaft 391465903/GRK 2466Deutsche Forschungsgemeinschaft 458090666/CRC1535/1Deutsche Forschungsgemeinschaft EXC-2048/1 - project ID 390686111
6 · The paper itself

Abstract

The biotechnological exploitation of microorganisms enables the use of metabolism for the production of economically valuable substances, such as drugs or food. It is, thus, unsurprising that the investigation of microbial metabolism and its regulation has been an active research field for many decades. As a result, several theories and techniques were developed that allow for the prediction of metabolic fluxes and yields as biotechnologically relevant output parameters. One important approach is to derive macrochemical equations that describe the overall metabolic conversion of an organism and basically treat microbial metabolism as a black box. The opposite approach is to include all known metabolic reactions of an organism to assemble a genome-scale metabolic model. Interestingly, both approaches are rather successful at characterizing and predicting the expected product yield. Over the years, macrochemical equations especially have been extensively characterized in terms of their thermodynamic properties. However, a common challenge when characterizing microbial metabolism by a single equation is to split this equation into two, describing the two modes of metabolism, anabolism and catabolism. Here, we present strategies to systematically identify separate equations for anabolism and catabolism. Based on metabolic models, we systematically identify all theoretically possible catabolic routes and determine their thermodynamic efficiency. We then show how anabolic routes can be derived, and we use these to approximate biomass yield. Finally, we challenge the view of metabolism as a linear energy converter, in which the free energy gradient of catabolism drives the anabolic reactions.

Indexed as

elementary conversion modesenergy converterenergy metabolismmetabolic networks

Identifiers

PMID38398756
PMCPMC10890395

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.