ReviewMetabolites2014
Computational strategies for a system-level understanding of metabolism.
Review in Metabolites, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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
18 citing papers in PubMed.
- Current Advancements in Untargeted Metabolomics Analysis and Testing Driven by Machine Learning: Prospects for Artificial Intelligence in Patient-Centric Healthcare Transformation.Applied biochemistry and biotechnology · 2026Review
- Roles of chemical species transport and transformation in the biophysics of human pathophysiology.npj biological physics and mechanics · 2025Review
- The biological principles and advanced applications of DSB repair in CRISPR-mediated yeast genome editing.Synthetic and systems biotechnology · 2023Review
- Sensitivity analysis of biochemical systems using bond graphs.Journal of the Royal Society, Interface · 2023Article
- A Metabolic Model of Intestinal Secretions: The Link between Human Microbiota and Colorectal Cancer Progression.Metabolites · 2021Article
- Accelerated global sensitivity analysis of genome-wide constraint-based metabolic models.BMC bioinformatics · 2021Article
- On the Use of Topological Features of Metabolic Networks for the Classification of Cancer Samples.Current genomics · 2021Article
- Identifying Personalized Metabolic Signatures in Breast Cancer.Metabolites · 2020Article
- Review
- Kinetic modelling of quantitative proteome data predicts metabolic reprogramming of liver cancer.British journal of cancer · 2020Article
- Article
- Emerging ensembles of kinetic parameters to characterize observed metabolic phenotypes.BMC bioinformatics · 2018Article
- Advances in metabolome information retrieval: turning chemistry into biology. Part II: biological information recovery.Journal of inherited metabolic disease · 2018Review
- A metabolic core model elucidates how enhanced utilization of glucose and glutamine, with enhanced glutamine-dependent lactate production, promotes cancer cell growth: The WarburQ effect.PLoS computational biology · 2017Article
- popFBA: tackling intratumour heterogeneity with Flux Balance Analysis.Bioinformatics (Oxford, England) · 2017Article
- Computational Model Predicts the Effects of Targeting Cellular Metabolism in Pancreatic Cancer.Frontiers in physiology · 2017Article
- Clinical Metabolomics: The New Metabolic Window for Inborn Errors of Metabolism Investigations in the Post-Genomic Era.International journal of molecular sciences · 2016Review
- Pancreatic Beta Cell G-Protein Coupled Receptors and Second Messenger Interactions: A Systems Biology Computational Analysis.PloS one · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cell metabolism is the biochemical machinery that provides energy and building blocks to sustain life. Understanding its fine regulation is of pivotal relevance in several fields, from metabolic engineering applications to the treatment of metabolic disorders and cancer. Sophisticated computational approaches are needed to unravel the complexity of metabolism. To this aim, a plethora of methods have been developed, yet it is generally hard to identify which computational strategy is most suited for the investigation of a specific aspect of metabolism. This review provides an up-to-date description of the computational methods available for the analysis of metabolic pathways, discussing their main advantages and drawbacks. In particular, attention is devoted to the identification of the appropriate scale and level of accuracy in the reconstruction of metabolic networks, and to the inference of model structure and parameters, especially when dealing with a shortage of experimental measurements. The choice of the proper computational methods to derive in silico data is then addressed, including topological analyses, constraint-based modeling and simulation of the system dynamics. A description of some computational approaches to gain new biological knowledge or to formulate hypotheses is finally provided.
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.