Hybrid Modeling of Engineered Biological Systems through Coupling Data-Driven Calibration of Kinetic Parameters with Mechanistic Prediction of System Performance

Mechanistic models can provide predictive insight into the design and optimization of engineered biological systems, but the kinetic parameters in these models need to be frequently calibrated and uniquely identified. This limitation can be addressed by hybrid modeling that integrates mechanistic models with data-driven approaches. Herein, we developed a hybrid modeling strategy using bioelectrochemical systems as a platform system. The data-driven component consisted of artificial neural networks (ANNs) trained with mechanistically derived kinetic parameters as outputs to compute error signals. The hybrid model was built using 148 samples from the literature. After 10-fold cross-validation, the model was tested with another 28 samples. Internal resistance was accurately predicted with a relative root-mean-square error (RMSE) of 3.9%. Microbial kinetic parameters were predicted using the data-driven component and fed into the mechanistic component to simulate the system performance. The R² values between predicted and observed organic removal and current for systems fed with a simple substrate were 0.90 and 0.94, respectively, significantly higher than those obtained from the stand-alone data-driven model (0.51 and 0) and mechanistic model (0.07 and 0.15). This strategy can potentially be applied to engineered biological systems for in silico system design and optimization.