A stochastic particle-based biological system simulator
Bibliographic record
Abstract
The simulation and visualization of biological systems is expected to enhance our understanding of biological processes towards the development of effective therapeutic treatments. Biological systems are inherently stochastic at the molecular level, exhibit modified behavior under crowded conditions and may be affected by spatial locality. Common simulation approaches fail to account for these important aspects of biological systems, in part because they are computationally expensive. Here, we describe a stochastic, particle-based simulator that takes spatial locality into account. Each particle in the system is represented explicitly on a 3D grid where only one particle can occupy a grid location. The grid structure and stochastic approach removes the need for distance calculation and particle search. We demonstrate the effect of molecular crowding and spatial locality for a simple biological system. We anticipate that this system will be useful in examining more complex systems. Finally, this system is expected to be suitable for acceleration with parallel customizable hardware, a necessary requirement towards the simulation of an entire cell.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".