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Central Metabolism-Responsive Biosensors for Monitoring, Screening, and Engineering in Microbial Production

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Review Open Access

Central Metabolism-Responsive Biosensors for Monitoring, Screening, and Engineering in Microbial Production

Jianli Zhang 1 Xinyu Gong 1 Qi Gan 1 Yingyue Yu 1 Yuan Dou 1 Renjie Shang 2 Nicolas Lopez 1 Yajun Yan 1,*

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1
School of Chemical, Materials and Biomedical Engineering, College of Engineering, The University of Georgia, Athens, GA 30602, USA
2
Department of Genetics, The University of Georgia, Athens, GA 30602, USA
*
Authors to whom correspondence should be addressed.

Received: 13 April 2026 Revised: 28 April 2026 Accepted: 26 May 2026 Published: 10 June 2026

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© 2026 The authors. This is an open access article under the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).

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Synth. Biol. Eng. 2026, 4(2), 10007; DOI: 10.70322/sbe.2026.10007
ABSTRACT: Central metabolism includes essential pathways such as glycolysis, the pentose phosphate pathway, and the tricarboxylic acid (TCA) cycle. Beyond the canonical pathways, it also involves byproduct formation, amino acid metabolism, fatty acid metabolism, and cofactor homeostasis, forming the metabolic backbone that supports cellular growth and biosynthesis. Conventional analytical methods often fail to provide real-time information in living cells, limiting their utility for guiding metabolic engineering. In this context, biosensor-assisted approaches have emerged as powerful tools for the real-time, non-destructive detection of intracellular metabolites and metabolic fluxes, while also enabling dynamic regulation of metabolic networks. In this review, we summarize recent advances in biosensors targeting key metabolites, cofactors, and regulatory nodes across central metabolism, with an emphasis on their design principles and applications in metabolic monitoring, high-throughput screening, and dynamic regulation for improved bioproduction. We also discuss current challenges related to sensor performance and implementation, and highlight the possibilities of integrating biosensors with omics, metabolic modules, and artificial intelligence (AI) to provide insights into future opportunities for biosensor development.
Keywords: Biosensor; Central metabolism; Metabolic engineering; Transcription factor; Dynamic regulation; Bioproduction

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