A numerical study of the effects of multiplicative noise on a supercritical delay induced Hopf bifurcation in a gene expression model /
Bibliographic record
Abstract
In the context of gene expression, we proposed a nonlinear stochastic delay differential equation as a mathematical model to study the effects of extrinsic noise on a delay induced Hopf bifurcation. We envisaged its direct numerical resolution. Following the example of the noisy oscillator, we first solved a linearized version of the equation, close to the Hopf bifurcation. The numerical scheme used is a combination of a standard algorithm to solve a deterministic delay differential equation and a stochastic Euler scheme. From our calculations we verified that the deterministic behaviour is fully recovered. For the stochastic case, we found that our solution is qualitatively accurate, in the sense that the noise induced shift in the critical value a, follows the trend the known analytic results predict. However, our numerical solution systematically overestimates the value of the shift. This is explained because the accuracy in the numerical estimation of the decay rate of a solution towards the stationary state value is a function of the control parameter a. We believe the mismatch between the numerical solution and the analytic results is due to a lack of convergence of our scheme, rather than to lack of accuracy. As our numerical scheme is an hybrid, the convergence problem can be improved, both at the deterministic and at the stochastic parts of the scheme. In this work we left our numerical results on the nonlinear case out, because before proceeding to the investigation of the nonlinear equation, the convergence must be assured in the linear case.
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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".