ReviewTrends in microbiology2012
A metabolic network approach for the identification and prioritization of antimicrobial drug targets.
Review in Trends in microbiology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers.
What it found
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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
51 citing papers in PubMed, 112 citations in OpenAlex.
- Article
- Cryosphere: a frozen home of microbes and a potential source for drug discovery.Archives of microbiology · 2024Review
- Metabolic Connectome and Its Role in the Prediction, Diagnosis, and Treatment of Complex Diseases.Metabolites · 2024Review
- ICON-GEMs: integration of co-expression network in genome-scale metabolic models, shedding light through systems biology.BMC bioinformatics · 2023Article
- The transcriptional regulators of virulence forGenes & diseases · 2023Review
- A comprehensive review of artificial intelligence and network based approaches to drug repurposing in Covid-19.Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie · 2022Review
- Rapid-SL identifies synthetic lethal sets with an arbitrary cardinality.Scientific reports · 2022Article
- Genome-scale metabolic modeling in antimicrobial pharmacology.Engineering microbiology · 2022Review
- Article
- Antimicrobial Compounds from Microorganisms.Antibiotics (Basel, Switzerland) · 2022Review
- Bacterial metabolism and pathogenesis intimate intertwining: time for metabolic modelling to come into action.Microbial biotechnology · 2022Article
- Novel antimicrobial development using genome-scale metabolic model of Gram-negative pathogens: a review.The Journal of antibiotics · 2021Review
- A Systematic Strategy to Find Potential Therapeutic Targets forFrontiers in molecular biosciences · 2021Article
- Uncovering the Role of Metabolism in Oomycete-Host Interactions Using Genome-Scale Metabolic Models.Frontiers in microbiology · 2021Review
- Antibiotic resistance: Time of synthesis in a post-genomic age.Computational and structural biotechnology journal · 2021Review
- Article
- Genome-Scale Metabolic Model of the Human PathogenJournal of fungi (Basel, Switzerland) · 2020Article
- Nanoinformatics and biomolecular nanomodeling: a novel move en route for effective cancer treatment.Environmental science and pollution research international · 2020Review
- Metabolic Model of themBio · 2019Article
- Antibiotic Discovery: Where Have We Come from, Where Do We Go?Antibiotics (Basel, Switzerland) · 2019Review
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
For many infectious diseases, novel treatment options are needed in order to address problems with cost, toxicity and resistance to current drugs. Systems biology tools can be used to gain valuable insight into pathogenic processes and aid in expediting drug discovery. In the past decade, constraint-based modeling of genome-scale metabolic networks has become widely used. Focusing on pathogen metabolic networks, we review in silico strategies used to identify effective drug targets and highlight recent successes as well as limitations associated with such computational analyses. We further discuss how accounting for the host environment and even targeting the host may offer new therapeutic options. These systems-level approaches are beginning to provide novel avenues for drug targeting against infectious agents.
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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.