ReviewFrontiers in bioengineering and biotechnology2015
Analytics for Metabolic Engineering.
Review in Frontiers in bioengineering and biotechnology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 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
35 citing papers in PubMed, 103 citations in OpenAlex.
- Highly multiplexed mammalian metabolic engineering with a shotgun approach.Nature biotechnology · 2026Article
- Shotgun engineering fires up mammalian metabolic design.Nature biotechnology · 2026Article
- Review
- Designing fiber-gut microbiome interactions with active learning.Nature chemical biology · 2026Article
- Global impact on metabolic capacity of yeast cell factories by optogenetic control of the cAMP-PKA axis.Applied and environmental microbiology · 2026Article
- Machine learning-led semi-automated medium optimization reveals salt as key for flaviolin production in Pseudomonas putida.Communications biology · 2025Article
- Multiscale Mathematical Modeling in Systems Biology: A Framework to Boost Plant Synthetic Biology.Plants (Basel, Switzerland) · 2025Review
- State-of-the-art in engineering small molecule biosensors and their applications in metabolic engineering.SLAS technology · 2024Review
- Machine Learning and Deep Learning in Synthetic Biology: Key Architectures, Applications, and Challenges.ACS omega · 2024Review
- MoBioS: Modular Platform Technology for High-Throughput Construction and Characterization of Tunable Transcriptional Biological Sensors.Biosensors · 2023Article
- MYC2: A Master Switch for Plant Physiological Processes and Specialized Metabolite Synthesis.International journal of molecular sciences · 2023Review
- Alkaline-SDS cell lysis of microbes with acetone protein precipitation for proteomic sample preparation in 96-well plate format.PloS one · 2023Article
- Integration of physiologically relevant photosynthetic energy flows into whole genome models of light-driven metabolism.The Plant journal : for cell and molecular biology · 2022Article
- Development and application of CRISPR-based genetic tools in Bacillus species and Bacillus phages.Journal of applied microbiology · 2022Review
- Synthetic Biology Tools for Engineering Microbial Cells to Fight Superbugs.Frontiers in bioengineering and biotechnology · 2022Review
- A toolkit for enhanced reproducibility of RNASeq analysis for synthetic biologists.Synthetic biology (Oxford, England) · 2022Article
- Mapping enzyme catalysis with metabolic biosensing.Nature communications · 2021Article
- Metabolic Perturbation and Synthetic Biology Strategies for Plant Terpenoid Production-An Updated Overview.Plants (Basel, Switzerland) · 2021Review
- A metabolic and physiological design study of Pseudomonas putida KT2440 capable of anaerobic respiration.BMC microbiology · 2021Article
- Multiomics Data Collection, Visualization, and Utilization for Guiding Metabolic Engineering.Frontiers in bioengineering and biotechnology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Realizing the promise of metabolic engineering has been slowed by challenges related to moving beyond proof-of-concept examples to robust and economically viable systems. Key to advancing metabolic engineering beyond trial-and-error research is access to parts with well-defined performance metrics that can be readily applied in vastly different contexts with predictable effects. As the field now stands, research depends greatly on analytical tools that assay target molecules, transcripts, proteins, and metabolites across different hosts and pathways. Screening technologies yield specific information for many thousands of strain variants, while deep omics analysis provides a systems-level view of the cell factory. Efforts focused on a combination of these analyses yield quantitative information of dynamic processes between parts and the host chassis that drive the next engineering steps. Overall, the data generated from these types of assays aid better decision-making at the design and strain construction stages to speed progress in metabolic engineering research.
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.