Evidence map›Paper›PMID 40843963›Full record

ReviewACS synthetic biology2025

Challenges and Opportunities in Smart Biosensing for Biomanufacturing.

Mara Pisani, Pablo Carbonell

Abstract readReview
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Review
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Mara PisaniSynthetic and Systems Biology Lab for Biomedicine, Instituto Italiano di Tecnologia-IIT, Largo Barsanti e Matteucci, 80125 Naples, Italy.
Pablo CarbonellInstitute for Integrative Systems Biology I2SysBio, Universitat de València-CSIC, Escardino Street 9, 46980 Paterna, València, Spain.ORCID 0000-0002-0993-5625

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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

Biosensing TechniquesMetabolic EngineeringMachine LearningMetabolic Networks and PathwaysSynthetic Biologybiosensorcomputer-in-the-loopdynamic regulationhigh-throughput screening

Identifiers

PMID40843963
PMCPMC12455640

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.