in silico
Phase 1 : In Silico Validation
- Objectif
- Determine whether the proposed QS-mediated negative feedback loop can theoretically stabilize population ratios and metabolite production under realistic gut simulator conditions, and identify parameter regimes where the system fails.
- Coût estimé
- €500-1500
- Durée estimée
- 4-6 weeks
- Critères de réussite
- Existence of a contiguous parameter region where CV < 0.15 and metabolite production is within bounds · At least 5% of the 10,000 parameter sets satisfy both criteria · (Count of parameter sets meeting both criteria, visualized as a 3D scatter plot)
- Robustness to QS degradation · At least one parameter set with CV < 0.15 when QS degradation rate = 0.2 h⁻¹ · (Simulation output for worst-case scenario (a))
- Robustness to growth inhibition strength · No parameter set leads to extinction of Strain B (density < 10⁴ CFU/mL) when growth inhibition factor = 0.9 · (Minimum Strain B density across all simulations for worst-case scenario (b))
- On continue si
- At least 5% of parameter sets satisfy both CV and production criteria, AND the system does not collapse entirely under worst-case scenarios (a) and (b).
- On arrête si
- No parameter set achieves CV < 0.15, OR the system collapses under all tested conditions (extinction of one strain).
- On réoriente si
- Only a narrow parameter region (<1% of sets) works, OR the system is highly sensitive to QS degradation (no working set at k_deg > 0.05 h⁻¹). Pivot: consider alternative QS molecules (e.g., AI-2 from Vibrio harveyi) or a different feedback mechanism (e.g., CRISPRi-based repression).
- Risques
- Model predictions are overly optimistic due to neglected spatial heterogeneity (mucus layer diffusion limitation).Probabilité : mediumParade : Include a simple diffusion-reaction term in the model (Fick’s law with D = 10⁻⁶ cm²/s) to estimate the effect of mucus layer on QS signal distribution. If signal gradient > 10% across 100 μm, flag as risk.
- Model parameters (Monod constants, growth rates) are not available for the specific strains under anaerobic conditions.Probabilité : mediumParade : Use conservative estimates (e.g., μ_max = 0.3 h⁻¹ for E. coli, 0.2 h⁻¹ for B. theta) and perform sensitivity analysis over a 2x range. If results are highly sensitive to these parameters, plan Phase 2 to measure them.
- Software or computational resources insufficient for 10,000 simulations.Probabilité : lowParade : Use cloud computing (Google Colab Pro, €10/month) or reduce to 2,000 parameter sets with Latin Hypercube sampling.