Mesoscopic dynamics of diffusion-influenced enzyme kinetics
Bibliographic record
Abstract
A particle-based mesoscopic model for enzyme kinetics is constructed and used to investigate the influence of diffusion on the reactive dynamics. Enzymes and enzyme-substrate complexes are modeled as finite-size soft spherical particles, while substrate, product, and solvent molecules are point particles. The system is evolved using a hybrid molecular dynamics-multiparticle collision dynamics scheme. Both the nonreactive and reactive dynamics are constructed to satisfy mass, momentum, and energy conservation laws, and reversible reaction steps satisfy detailed balance. Hydrodynamic interactions among the enzymes and complexes are automatically accounted for in the dynamics. Diffusion manifests itself in various ways, notably in power-law behavior in the evolution of the species concentrations. In accord with earlier investigations, regimes where the product production rate exhibits either monotonic or nonmonotonic behavior as a function of time are found. In addition, the species concentrations display both t(-1/2) and t(-3/2) power-law behavior, depending on the dynamical regime under investigation. For high enzyme volume fractions, cooperative effects influence the enzyme kinetics. The time dependent rate coefficient determined from the mass action rate law is computed and shown to depend on the enzyme concentration. Lifetime distributions of substrate molecules newly released in complex dissociation events are determined and shown to have either a power-law form for rebinding to the same enzyme from which they were released or an exponential form for rebinding to different enzymes. The model can be used and extended to explore a variety of issues related concentration effects and diffusion on enzyme kinetics.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".