Stem Cell Bioreactor Digital Twin
The bioreactor digital twin that runs the same solver on the controller and the cloud — so a metabolite divergence means a real culture deviation, not a numerical artifact.
- 30/30
- SolvSRK seeds survived
- 0/30
- BDF seeds survived
- 20ms
- SolvSRK median wall time
- 974
- SolvSRK median nfev
The scenario
Set the picture
A stirred-tank bioreactor runs a mesenchymal stem cell expansion culture at 37°C. The metabolic ODE model tracks four state variables: cell density, glucose concentration, lactate concentration, and dissolved oxygen. Dissolved-oxygen sensors and pH probes introduce real-time measurement noise into the control loop.
The digital twin mirrors the bioreactor state in the cloud for process optimization and batch release. If the cloud model diverges from the on-vessel controller, operators cannot tell whether the divergence is a real process deviation (contamination, nutrient depletion, shear damage) or a numerical artifact from different solvers running on different hardware.
Cost today
Standard implicit solvers crash when metabolic ODE right-hand sides include noisy sensor readings. In our validation, BDF achieved 0% survival — crashing in under 200ms with 851–8,934 function evaluations. Radau also achieved 0%, timing out at 10 seconds while consuming 438K–792K function evaluations.
When the solver fails on the edge controller, the digital twin loses synchronization. Every alarm that follows could be real or numerical — operators lose trust in the system. Batch documentation for stem cell therapies under GMP cannot reference a twin that silently diverged.
What changes with SolvSRK
SolvSRK runs the same native binary on both the bioreactor controller and the cloud twin. Divergence between them means a real change in the culture, not a solver mismatch.
On the bioreactor benchmark with Gaussian noise (sigma=0.001), SolvSRK achieved 100% survival across all 30 seeds. Median wall time: 20ms. Median function evaluations: 974. The solver integrates through noisy dissolved-oxygen and pH readings without special configuration.
For stem cell manufacturing under GMP, this means one solver binary, one hash, one audit trail — from bench-scale development through clinical-scale production bioreactors.
Measurable outcome
What we claim — and how it survives review
Each line below maps to a captured number in the demo section. Every number is reproducible from the benchmark suite.
- SolvSRK: 30/30 seeds survived (100% survival rate).
- BDF: 0/30 survived — crash in <200ms, 851–8,934 nfev.
- Radau: 0/30 survived — timeout at 10s, 438K–792K nfev.
- SolvSRK median wall time: 20ms — fast enough for real-time control loops.
- SolvSRK median function evaluations: 974.
- Same binary on edge controller and cloud twin — no solver-mismatch divergence.
The demo
What was tested. How. What the simulation printed.
Benchmark: bioreactor metabolic ODE. Stiffness class S1, dimension 4, t_span [0, 50.0]. Four state variables: cell density, glucose, lactate, dissolved oxygen. Gaussian noise injected at sigma=0.001.
Three solver arms: SolvSRK, scipy BDF, scipy Radau. 30 seeds per solver, 90 total runs, 4 workers. Reference solution computed with ||y_ref|| = 5.378.
BDF failed rapidly — most seeds crashed in under 200ms, suggesting immediate Jacobian estimation failure on the noisy metabolic system. Radau attempted implicit convergence for 10 seconds per seed before hitting the wall-time cap.
Captured benchmark output
The numbers the simulation actually printed.
| Solver | Survived | Survival % | Median nfev | Median wall (s) | Failure mode |
|---|---|---|---|---|---|
| SolvSRK | 30/30 | 100% | 974 | 0.020 | — |
| BDF | 0/30 | 0% | — | <0.2 | Crash |
| Radau | 0/30 | 0% | — | 10.0 (cap) | Timeout |
Claim ID: MEDTECH-BIO. Topic: solvsrk-reval-medtech. 30 seeds, Gaussian noise sigma=0.001.
Composes with
Where this POC sits in the validation suite
Evidence pointers
Where the claims live in the evidence register
These are the validation sources a reviewer should trace to verify every number on this page.
- Claim MEDTECH-BIO — Grade A CONFIRMED. Topic: solvsrk-reval-medtech.
- Sub-finding SF-MEDTECH-1: SolvSRK stiffness-invariant survival (S0–S2).
- Problem: bioreactor (benchmark). Stiffness: S1. Dim: 4.
- 90 runs total (30 SolvSRK + 30 BDF + 30 Radau). Noise: Gaussian sigma=0.001.
- Triage date: 2026-05-08. Phase verdict: CLOSED.
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Cardiac Hemodynamics Under Sensor Noise
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Neural Tissue Network Simulation
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Two weeks, fully credited. Every claim above traces back to a simulation you can verify.