ReviewACS synthetic biology2025
Challenges and Opportunities in Smart Biosensing for Biomanufacturing.
Review in ACS synthetic biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Advances in amidases and urethanases as depolymerization biocatalysts.Nature chemical biology · 2026Review
- Design of a Novel Diamine Biosensor to Guide the Engineering of Escherichia coli for Cadaverine Overproduction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Biosynthesis and Microbial Production of Carminic Acid: From Pathway Elucidation to Synthetic Biology.Microorganisms · 2026Review
- Genetically encoded biosensors enabled high-throughput screening of microbial cell factories.Engineering microbiology · 2026Review
- A genetically encoded L-rhamnose biosensor for monitoring marine polysaccharide depolymerization.Applied microbiology and biotechnology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Traditional metabolic engineering has largely focused on the direct construction of synthetic metabolic pathways, often overlooking the critical role of regulation. In contrast, natural metabolic pathways are inherently tightly regulated, enabling robust performance in dynamic environments. Dynamic regulation of synthetic metabolic pathways enhances the reliability of cell factories by improving their performance and ensuring greater robustness, scalability, and stability. Therefore, modern approaches to metabolic engineering should embrace genetic circuits that incorporate dynamic regulatory mechanisms. Biosensors, as key components of these circuits, not only enable precise genetic regulation but also provide real-time monitoring and external interfacing capabilities with diverse signal modalities, including electrical and optical systems. By the incorporation of dynamic control mechanisms, synthetic pathways can be rendered more robust to environmental fluctuations during scale-up and more precisely regulated in therapeutic contexts, such as responsive drug delivery. These capabilities are critical to advancing the reliability and applicability of engineered metabolic systems. Furthermore, the potential for the external control of synthetic metabolic processes, guided by advanced algorithms, underscores the growing importance of machine learning and data-driven approaches. This perspective highlights the necessity of integrating regulation into synthetic pathways and leveraging biosensors to drive the next generation of scalable and adaptive metabolic engineering solutions.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.