Batch-to-Batch Optimization with Model Adaptation Leveraging Gaussian Processes: The Case of Optogenetically Assisted Microbial Consortia

Sebastian Espinel-Rios, Rudolph Kok, Steffen Klamt, Jose L. Avalos, Rolf Findeisen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Microbial consortia are promising biotechnological production systems with the potential to divide complex metabolic pathways into smaller submodules, as well as make products and consume substrates that monocultures cannot. Maintaining optimal cell population levels and preventing mono culture formation challenge bioproduction by microbial consortia. Optogenetics allows for regulating the expression of key growth-regulatory genes through light to modulate cell population levels. Model-based dynamic optimization can determine optimal light trajectories and inoculum sizes that maximize product synthesis while satisfying system constraints, e.g., safety, economic, or technical aspects. Further-more, closed-loop dynamic optimization can address system uncertainty to a certain extent; however, its implementation is challenging due to limited online sensors. Alternatively, here we propose to perform open-loop optimization with batch-to-batch model adaptation based on Gaussian processes for maximizing bioproduction by optogenetically assisted consortia. The proposed approach enables knowledge transfer from existing to new models, improving predictability and optimization performance in each batch while avoiding costly and time-consuming modeling experiments. Compared to closed-loop optimization, this strategy is easier to implement as it does not rely on online monitoring, contributing to the state of the art in optimizing bioproduction by microbial consortia. We outline the applicability of the approach using simulation experiments of an optogenetically assisted consortium for the biosynthesis of the flavonoid naringenin, considering both parameter and model structure uncertainty.

Original languageEnglish (US)
Title of host publication23rd International Conference on Control, Automation and Systems, ICCAS 2023
PublisherIEEE Computer Society
Pages1292-1297
Number of pages6
ISBN (Electronic)9788993215274
DOIs
StatePublished - 2023
Event23rd International Conference on Control, Automation and Systems, ICCAS 2023 - Yeosu, Korea, Republic of
Duration: Oct 17 2023Oct 20 2023

Publication series

NameInternational Conference on Control, Automation and Systems
ISSN (Print)1598-7833

Conference

Conference23rd International Conference on Control, Automation and Systems, ICCAS 2023
Country/TerritoryKorea, Republic of
CityYeosu
Period10/17/2310/20/23

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Keywords

  • Batch-to-batch optimization
  • Gaussian processes
  • microbial consortia
  • model adaptation
  • optimal control
  • optogenetics

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