Hybrid physics-informed metabolic cybergenetics: process rates augmented with machine-learning surrogates informed by flux balance analysis
S Espinel-Ríos, JL Avalos - Industrial & Engineering Chemistry …, 2024 - ACS Publications
Metabolic cybergenetics is a promising concept that interfaces gene expression and cellular
metabolism with computers for real-time dynamic metabolic control. The focus is on control …
metabolism with computers for real-time dynamic metabolic control. The focus is on control …
[HTML][HTML] Experimentally implemented dynamic optogenetic optimization of ATPase expression using knowledge-based and Gaussian-process-supported models
Optogenetic modulation of adenosine triphosphatase (ATPase) expression represents a
novel approach to maximize bioprocess efficiency by leveraging enforced adenosine …
novel approach to maximize bioprocess efficiency by leveraging enforced adenosine …
Batch-to-batch optimization with model adaptation leveraging Gaussian processes: the case of optogenetically assisted microbial consortia
S Espinel-Ríos, R Kok, S Klamt… - … and Systems (ICCAS …, 2023 - ieeexplore.ieee.org
Microbial consortia are promising biotechnological production systems with the potential to
divide complex metabolic pathways into smaller submodules, as well as make products and …
divide complex metabolic pathways into smaller submodules, as well as make products and …
Linking intra-and extra-cellular metabolic domains via neural-network surrogates for dynamic metabolic control
S Espinel-Ríos, JL Avalos - IFAC-PapersOnLine, 2024 - Elsevier
We outline a modeling and optimization strategy for investigating dynamic metabolic
engineering interventions. Our framework is particularly useful at the early stages of …
engineering interventions. Our framework is particularly useful at the early stages of …
Efficient and simple Gaussian process supported stochastic model predictive control for bioreactors using HILO-MPC
Abstract Model-based control of biotechnological processes is, in general, challenging.
Often the processes are complex, nonlinear, and uncertain. Hence modeling tends to be …
Often the processes are complex, nonlinear, and uncertain. Hence modeling tends to be …
Monitoring intracellular metabolite concentrations by moving horizon estimation based on kinetic modeling
The biotechnology industry can significantly benefit from new paradigms such as smart
manufacturing, digitalization and quality-by-design to render more competitive and robust …
manufacturing, digitalization and quality-by-design to render more competitive and robust …
Variable-Frequency Model Learning and Predictive Control for Jumping Maneuvers on Legged Robots
Achieving both target accuracy and robustness in dynamic maneuvers with long flight
phases, such as high or long jumps, has been a significant challenge for legged robots. To …
phases, such as high or long jumps, has been a significant challenge for legged robots. To …
Omics-driven hybrid dynamic modeling of bioprocesses with uncertainty estimation
This work presents an omics-driven modeling pipeline that integrates machine-learning
tools to facilitate the dynamic modeling of multiscale biological systems. Random forests and …
tools to facilitate the dynamic modeling of multiscale biological systems. Random forests and …
Stochastic model predictive control utilizing Bayesian neural networks
Integrating measurements and historical data can enhance control systems through learning-
based techniques, but ensuring performance and safety is challenging. Robust model …
based techniques, but ensuring performance and safety is challenging. Robust model …
Adaptive-Frequency Model Learning and Predictive Control for Dynamic Maneuvers on Legged Robots
Achieving both target accuracy and robustness in dynamic maneuvers with long flight
phases, such as high or long jumps, has been a significant challenge for legged robots. To …
phases, such as high or long jumps, has been a significant challenge for legged robots. To …