The model organism as a system: integrating 'omics' data sets

By Andrew R. Joyce, Bernhard O. Palsson. Nature reviews. Molecular cell biology, 2006

823 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/55156dc9c11210839937e55e2f61b43aac5de5bc

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Citations in the corpus

Genome-scale models of bacterial metabolism: reconstruction and applications (2008, methodology): “Computational methods are thus needed if meaningful biological results are to be extracted (Joyce & Palsson, 2006).”
Ultrahigh-throughput screening in drop-based microfluidics for directed evolution (2010, background): “M of the significant progress in our ever-advancing knowledge of biology is driven by our ability to perform very large numbers of reactions and to screen large amounts of data (1).”
A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information (2007, methodology): “As high-throughput data become available for a number of organisms (Joyce and Palsson, 2006), there is a need for an underlying platform to analyze these data by placing them in a biological context.”
Comparative Genomics in Eukaryotes (2005, background): “The plethora of omics data sets that are available have been successfully integrated in order to reduce experimental noise and obtain reliable predictions about protein-protein interactions (Beyer et al., 2006; Huynen et al., 2004; Jansen et al., 2003; Joyce and Palsson, 2006; Troyanskaya et al., 20”
Linking metagenomics to aquatic microbial ecology and biogeochemical cycles (2019, methodology): “Such diagrams are preparedmanually by inspecting gene annotations, often with assistance from software that projects predicted functions onto pathway maps, such as MetaPathways (Konwar et al. 2015) and KEGG (Kanehisa et al. 2019).”
FunRich: An open access standalone functional enrichment and interaction network analysis tool (2015, background): “With advancements in high-throughput techniques and parallel reduction in the associated costs, large scale ‘omics’ studies are now used frequently [1].”
The Biomass Objective Function (2010, neutral): “With metabolic models becoming available for a growing number of organisms [5] and high-throughput technologies enabling the construction of many more each year [6], FBA is an important tool for harnessing the knowledge encoded in these models.”
Applying mass spectrometry-based proteomics to genetics, genomics and network biology (2009, background): “Techniques for the successful integration of large data sets have been proposed and reviewed recentl”