27 citations · 48 across the 5 of their papers we have counts for
5 papers
Reinforcement learning for efficient and robust multi-setpoint and multi-trajectory tracking in bioprocesses
Sebastián Espinel-Ríos, José L. Avalos, Ehecatl Antonio del Rio Chanona +1
Efficient and robust bioprocess control is essential for maximizing performance and adaptability in advanced biotechnological systems. In this work, we present a reinforcement-lear…
Enhancing reinforcement learning for population setpoint tracking in co-cultures
Sebastián Espinel-Ríos, Joyce Qiaoxi Mo, Dongda Zhang +2
Efficient multiple setpoint tracking can enable advanced biotechnological applications, such as maintaining desired population levels in co-cultures for optimal metabolic division…
Omics-driven hybrid dynamic modeling of bioprocesses with uncertainty estimation
Sebastián Espinel-Ríos, José Montaño López, José L. Avalos
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…
Hybrid physics-informed metabolic cybergenetics: process rates augmented with machine-learning surrogates informed by flux balance analysis
Sebastián Espinel-Ríos, José L. Avalos
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 contr…
Linking intra- and extra-cellular metabolic domains via neural-network surrogates for dynamic metabolic control
Sebastián Espinel-Ríos, José L. Avalos
We outline a modeling and optimization strategy for investigating dynamic metabolic engineering interventions. Our framework is particularly useful at the early stages of research…