STL robustness is used as a differentiable loss to train biomolecular neural networks for regression and closed-loop control in silico.
Construction of an escherichia coli strain to degrade phenol completely with two modified metabolic modules,
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STL-based Optimization of Biomolecular Neural Networks for Regression and Control
STL robustness is used as a differentiable loss to train biomolecular neural networks for regression and closed-loop control in silico.