Uncovering transcriptional regulation of metabolism by using metabolic network topology.

By Kiran Raosaheb Patil, Jens Nielsen. Proceedings of the National Academy of Sciences of the United States of America, 2005

662 citations recorded by the source corpus.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/3958baf5b8b755ef9541ab17a2f101a0a6e246fe

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

Citations in the corpus

The model organism as a system: integrating 'omics' data sets (2006, methodology): “Furthermore, when viewed in light of genome-wide expression data, these models can be used to uncover the structure of transcription regulation in metabolic network”
Automated refinement and inference of analytical models for metabolic networks (2011, neutral): “[85].”
Applications of genome-scale metabolic reconstructions (2009, background): “Transcriptional control has also been studied by identification of ‘reporter metabolites,’ which represent the most highly transcriptionally regulated metabolites in a system (Patil and Nielsen, 2005; Raghevendran et al, 2006; David et al, 2008).”
Network integration of parallel metabolic and transcriptional data reveals metabolic modules that regulate macrophage polarization. (2015, methodology): “Overall, analytical strategies used for integration of metabolic and transcriptional data can be broadly divided into two major classes: modifications of constraint-based models (such as flux balance analysis) (Bordbar et al., 2012; Patil and Nielsen, 2005) and network-based approaches (Beisser et a”
Dynamics and Stabilization of the Human Gut Microbiome during the First Year of Life. (2015, methodology): “Differentially enriched KO pathways, modules, or CAZy families were identified according to their reporter score (Patil and Nielsen, 2005), from the Z scores of individual KOs (Supplemental Experimental Procedures).”
Metagenomic systems biology of the human gut microbiome reveals topological shifts associated with obesity and inflammatory bowel disease (2011, background): “regulation (24, 25), modularity (26, 27), essentiality and mutant viability (28), genetic and environmental robustness (29), adaptation (30, 31), and species interaction (32).”
Engineering Cellular Metabolism (2008, positive): “It is difficult to analyze multiple data types, but GEMs provide a good scaffold for analysis (Patil and Nielsen, 2005; Usaite et al., 2009).”
Constraint-based models predict metabolic and associated cellular functions (2014, methodology): “Initially, the metabolic network was used as a scaffold to interpret transcriptional changes 32,33 in a manner that is similar to pathway enrichment analysis (FIG.”
Reconstruction of Genome-Scale Active Metabolic Networks for 69 Human Cell Types and 16 Cancer Types Using INIT (2012, methodology): “(PDF) Table S7 List of Reporter Metabolites (p-value,10e-4).”
A comprehensive comparison of RNA-Seq-based transcriptome analysis from reads to differential gene expression and cross-comparison with microarrays: a case study in Saccharomyces cerevisiae (2012, methodology): “The reporter algorithm (48,49) was then employed to evaluate the functional enrichment level of each GO term.”
The RAVEN Toolbox and Its Use for Generating a Genome-scale Metabolic Model for Penicillium chrysogenum (2013, methodology): “The Reporter algorithm [47] was employed to integrate the transcriptome data with the reconstructed GEM to identify key metabolites in the network.”
The return of metabolism: biochemistry and physiology of the pentose phosphate pathway (2014, background): “A more disputed theory concerns the cross-feeding of cancer cells through lactate, termed the ‘Reverse Warburg effect’ (Pavlides et al., 2009).”