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Record W4401813315 · doi:10.1101/2024.08.21.609071

Cell Geometry and Membrane Protein Crowding Constrain <i>Escherichia coli</i> Growth Rate, Overflow Metabolism, Respiration, and Maintenance Energy

2024· preprint· en· W4401813315 on OpenAlexaff
Ross P. Carlson, Tomáš Gedeon, Mauricio Garcia Benitez, William R. Harcombe, Radhakrishnan Mahadevan, Ashley E. Beck

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2024
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrobial Metabolic Engineering and Bioproduction
Canadian institutionsUniversity of Toronto
FundersAir Force Office of Scientific ResearchU.S. Department of AgricultureNational Institute of Food and AgricultureNational Institutes of HealthNational Science Foundation
KeywordsRespirationEnergy metabolismCrowdingCell biologyBiophysicsMetabolismGeometryChemistryBiologyBiochemistryBotanyMathematicsNeuroscience

Abstract

fetched live from OpenAlex

K-12 strains MG1655 and NCM3722. The strains are genetically similar but differ in surface area to volume (SA:V) ratios by up to 30%, maximum growth rates on glucose media by 40%, and overflow-inducing growth rates by 80%. The predictions were tested against experimental evidence including phenomics data, membrane proteomics data, and MG1655 SA:V mutant growth rates. The predictions were remarkably consistent with experimental data and provided a membrane-centric explanation for maximum growth rate, maintenance energy generation, respiration chain efficiency (P/O number), and optimal biomass yield of a strain. These analyses did not consider cytosolic macromolecular crowding, highlighting the distinct properties of the presented theory and gaps in current cell biology literature. Cell geometry and membrane protein crowding are significant biophysical constraints and consideration of both provide a more complete theoretical framework for improved understanding and control of cell biology.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.005
GPT teacher head0.178
Teacher spread0.173 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2024
Admission routes1
Has abstractyes

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