in silico
Phase 1: In Silico Validation
- Objective
- To rapidly test the core mechanistic assumptions of the HRC using computational models, predicting VEGF gradient formation, circuit dynamics, and potential for aberrant vascularization before any wet-lab experiment.
- Estimated cost
- €0-1500 (software licenses if COMSOL needed; open-source alternatives are free)
- Estimated duration
- 4-6 weeks
- Success criteria
- HRC Dynamic Range · Model predicts a VEGF secretion fold-change ≥8 between 1% and 21% O2. · (From ODE simulation output, calculate (VEGF_max @1%O2) / (VEGF_min @21%O2).)
- Gradient Therapeutic Window · Model predicts a VEGF concentration >10 ng/mL (EC50 for migration) within a 500 μm radius, and <2 ng/mL beyond 1000 μm. · (Extract concentration vs. distance profile from the spatial model at peak secretion.)
- Safety Margin · Predicted peak local VEGF concentration from the HRC remains below the model-identified “danger zone” threshold by a factor of ≥2. · (Compare simulated max [VEGF] from the HRC model to the critical threshold from the agent-based model.)
- Go if
- All three success criteria are met. The model supports a robust, spatially-restricted therapeutic signal.
- No-go if
- Model predicts dynamic range <5, no functional gradient formation, or peak VEGF consistently within the “danger zone” even with parameter tuning. The core hypothesis is computationally falsified.
- Pivot if
- Dynamic range is adequate (≥8) but gradient is too shallow. Pivot to Phase 2 with an added experimental goal: co-express a VEGF trap (sFlt1) under a normoxia-sensitive promoter to sharpen the gradient.
- Risks
- Model parameters are inaccurate due to lack of cell-type (MSC) specific data.Probability: mediumMitigation: Use parameter ranges from literature and perform global sensitivity analysis (e.g., Sobol indices) to identify which parameters most affect predictions. Focus Phase 2 on measuring these key parameters.
- The model fails to capture complex in vivo tissue heterogeneity and feedback.Probability: highMitigation: Explicitly state model limitations. Use the simulation to generate falsifiable, quantitative predictions (e.g., expected gradient slope) for direct testing in Phase 2.