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Found 4 papers, 0 papers with code
Experimentally implemented dynamic optogenetic optimization of ATPase expression using knowledge-based and Gaussian-process-supported models
We implement optimal control constrained by knowledge-based and hybrid models for optogenetic ATPase expression in $\textit{Escherichia coli}$ with lactate as the main product.
Hybrid physics-informed metabolic cybergenetics: process rates augmented with machine-learning surrogates informed by flux balance analysis
In this study, we present an alternative hybrid physics-informed dynamic modeling framework for metabolic cybergenetics, aimed at simplifying optimization, control, and estimation tasks.
Linking intra- and extra-cellular metabolic domains via neural-network surrogates for dynamic metabolic control
Elucidating a priori optimal trajectories of manipulatable intracellular fluxes can guide the design of suitable control schemes, e. g., cyber(ge)netic or in-cell approaches, and the selection of appropriate actuators, e. g., at the transcriptional or post-translational levels.
Towards a modeling, optimization and predictive control framework for fed-batch metabolic cybergenetics
Furthermore, many bioprocesses are optimized empirically and implemented with little-to-no feedback control to counteract disturbances.
Cultural Vocal Bursts Intensity Prediction
Model Predictive Control
