A Computational Tool for Monte Carlo Simulations of Biomolecular Reaction Networks Modeled on Physical Principles
Bibliographic record
Abstract
Deciphering and designing complex biomolecular networks in the cell are the goals of systems and synthetic biology, respectively. The effects of localization, spatial heterogeneity, and molecular fluctuations in biomolecular networks are not well understood. We present a theoretical approach based on physical principles to accurately simulate biomolecular networks using the Monte Carlo method. Incorporating this theory, a computational tool named Monte Carlo biomolecular simulator (MBS) was developed, enabling studies of biomolecular kinetics with both spatial and temporal resolutions. The accuracy of MBS was verified by comparison against the classical deterministic approaches. Furthermore, the effects of localization, spatial heterogeneity, and molecular fluctuations were studied in three simulated model systems, showing their impact on the overall reaction kinetics. This work demonstrates the unique insights that can be discovered by considering the subtle effects that can be created by the spatial and temporal kinetics of biomolecular reaction networks.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".