Diffusion NMR and Rheology of a Model Polymer in Bacterial Cell Lysate Crowders
Bibliographic record
Abstract
The intracellular milieu is crowded and heterogeneous, and this can have profound consequences for biomolecule motions and biochemical kinetics. Macromolecular crowding has been traditionally studied in artificial crowders like Ficoll and dextran or globular proteins such as bovine serum albumin. It is, however, not clear if the effects of artificial crowders on such phenomena are the same as the crowding that is experienced in a heterogeneous biological environment. Bacterial cells, for example, are composed of heterogeneous biomolecules with different sizes, shapes, and charges. Using crowders composed of one of three different pretreatments of bacterial cell lysate (unmanipulated, ultracentrifuged, and anion exchanged), we examine the effects of crowding on the diffusivity of a model polymer. We measure the translational diffusivity, via diffusion NMR, of the test polymer polyethylene glycol (PEG) in these bacterial cell lysates. We show that the small ( R g ∼ 5 nm) test polymer shows a modest decrease in self-diffusivity with increasing crowder concentration for all lysate treatments. The corresponding self-diffusivity decrease in the artificial Ficoll crowder is much more pronounced. Moreover, a comparison of the rheological response of biological and artificial crowders shows that while the artificial crowder Ficoll exhibits a Newtonian response even at high concentrations, the bacterial cell lysate is markedly non-Newtonian; it behaves like a shear-thinning fluid with a yield stress. While at any concentration the rheological properties are sensitive to both lysate pretreatment and batch-to-batch variations, the PEG diffusivity is nearly unaffected by the type of lysate pretreatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".