Trajectory-matching ABC-MCMC for simulating heterogeneous dynamics in mechanistic models
Bibliographic record
Abstract
The inherent heterogeneity of complex biological systems makes it difficult to experimentally and clinically explore individual outcomes within them. Mechanistic mathematical models are essential tools for studying such heterogeneity. Thus, there is increasing interest in integrating newer mechanistic model-based techniques, like virtual patient cohorts and virtual clinical trials, within experimental and regulatory pipelines to probe relationships that may be difficult or impossible to ascertain through traditional wetlab or clinical experimentation alone. Computational approaches like Approximate Bayesian Computation (ABC) are attractive methods for generating virtual patient cohorts and running virtual clinical trials. Among the various ABC approaches, ABC combined with Markov chain Monte Carlo (ABC-MCMC) is widely used to improve sampling efficiency and guarantee convergence to the approximate posterior. However, ABC-MCMC must meet the acceptance criteria of both approaches, which results in a high rejection rate. In response, we developed a model-based technique called trajectory-matching ABC-MCMC (TM-ABC-MCMC). TM-ABC-MCMC captures the variability of complex biological systems by constraining model trajectories between the upper and lower bounds of available data to generate heterogeneity in model parameters. By testing the method's performance on existing mechanistic models and comparing to existing ABC-MCMC algorithms, we show that TM-ABC-MCMC accurately reproduces the observed noise in biological systems of varying complexity, all while maintaining computational efficiency. Thus, TM-ABC-MCMC is a new approach for generating heterogeneity in mechanistic mathematical models with implications for model-based experimental design, virtual patient cohorts, and virtual clinical trials.
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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 | 0.001 |
| 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".