A Modular Micro-Chemostat System for High-Throughput Continuous Cultivations
Artikel i vetenskaplig tidskrift, 2026

Improving the fidelity of computational eukaryotic models will require an enormous amount of high quality and reproducible data from biological experiments. In this study we present and demonstrate a modular 12-channel micro-chemostat system. This micro-chemostat is designed for use by Genesis, a Robot Scientist developed to automate scientific research in yeast systems biology. We performed bioreactor characterization experiments to evaluate the performance of the micro-chemostat system. We also cultivated the Saccharomyces cerevisiae reference strain, BY4741, at three different dilution rates (0.1, 0.2, and 0.3 h−1) and under two aeration conditions (microaerobic and aerobic), resulting in six different conditions. The bioreactor performance fulfilled our requirements, and enabled substrate-limited continuous cultivation of yeast. Furthermore, the empirical results from the biological experiments were consistent with the literature, suggesting that results obtained from our system are comparable to those reported for other platforms in the field. Based on the evidence presented in this study, we conclude that the 12-channel micro-chemostat system is a suitable platform for yeast systems biology research. The modular design of the micro-chemostat system enables future scaling to hundreds or thousands of concurrent chemostats and facilitates integration with autonomous agents for automated optimization and improvement of eukaryotic model systems.

eukaryotic model refinement

microlitre-scale chemostat

Saccharomyces cerevisiae physiology

automation of science

high-throughput continuous cultivation

Författare

Erik Bjurström

Chalmers, Life sciences, Infrastrukturer

Filip Kronström

Chalmers, Data- och informationsteknik, Data Science och AI

Göteborgs universitet

Ronald S. Reiserer

Vanderbilt University

David K. Schaffer

Vanderbilt University

Dmitry A. Markov

Vanderbilt University

John P. Wikswo

Vanderbilt University

Ross King

University of Cambridge

Chalmers, Data- och informationsteknik, Data Science och AI

Göteborgs universitet

Ievgeniia Tiukova

Göteborgs universitet

Chalmers, Data- och informationsteknik, Data Science och AI

Kungliga Tekniska Högskolan (KTH)

Biotechnology and Bioengineering

0006-3592 (ISSN) 1097-0290 (eISSN)

Vol. In Press

Ämneskategorier (SSIF 2025)

Annan industriell bioteknik

DOI

10.1002/bit.70430

Mer information

Senast uppdaterat

2026-10-30