in silico
Phase 1 : In Silico Validation
- Objectif
- To computationally predict the optimal scaffold parameters (fiber diameter, alignment, stiffness) that maximize follicle survival and oocyte quality, and to assess the feasibility of the proposed mechanism before experimental investment.
- Coût estimé
- €500-2000
- Durée estimée
- 4-6 weeks
- Critères de réussite
- Model predictive accuracy · R^2 > 0.7 when comparing model predictions to at least 10 published experimental data points · (Correlation coefficient between model outputs and literature data)
- Optimal parameter identification · A distinct region in parameter space where predicted survival rate increase >20% and oocyte quality score increase >15% · (Contour plots of predicted outcomes over parameter ranges)
- On continue si
- Model predictions show a clear optimal parameter region with predicted improvements meeting or exceeding the thresholds (20% survival increase, 15% oocyte quality increase) and model validation R^2 > 0.7.
- On arrête si
- Model predicts no parameter combination achieves the desired improvements, or model validation fails (R^2 < 0.5).
- On réoriente si
- Model suggests that the mechanism is plausible but requires different parameter ranges (e.g., fiber diameter >600 nm) or additional factors (e.g., biochemical cues) to achieve improvements; then adjust hypothesis or proceed to Phase 2 with revised parameters.
- Risques
- Insufficient published data for model calibrationProbabilité : mediumParade : Use data from similar tissue engineering studies (e.g., neural stem cells on fibers) and extrapolate; collaborate with experimental labs to obtain preliminary data.
- Model oversimplification of biological complexityProbabilité : mediumParade : Incorporate multiple pathways and validate against multiple datasets; keep model modular to allow updates.
- Computational cost too high for parameter sweepProbabilité : lowParade : Use surrogate modeling (e.g., Gaussian process) to reduce simulations; use HPC resources.