Reconstruction of Biochemical Networks in Microbial Organisms

By Adam M. Feist, Markus J. Herrgård, Ines Thiele, Jennie L. Reed, Bernhard O. Palsson. Nature Reviews Microbiology, 2008

876 citations recorded by the source corpus.

Systems analysis of metabolic and growth functions in microbial organisms is rapidly developing and maturing. Such studies are enabled by reconstruction, at the genomic scale, of the biochemical reaction networks that underlie cellular processes. The network reconstruction process is organism specific and is based on an annotated genome sequence, high-throughput network-wide data sets and bibliomic data on the detailed properties of individual network components. Here we describe the process that is currently used to achieve comprehensive network reconstructions and discuss how these reconstructions are curated and validated. This Review should aid the growing number of researchers who are carrying out reconstructions for particular target organisms.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/cac06c5a8484232a8a162a1011b2f03bfbbb36a2

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

Using graph theory to analyze biological networks (2011, background): “Modern sequencing techniques allow the reconstruction of the network of biochemical reactions in many organisms, from bacteria to human [38,39].”
Prioritizing candidate disease genes by network-based boosting of genome-wide association data. (2011, background): “We therefore next asked whether the human gene network could predict genetic interactions , focusing on two large-scale RNAi screens performed in mammalian cell culture.”
Applications of genome-scale metabolic reconstructions (2009, methodology): “reconstructions”
Basic and applied uses of genome-scale metabolic network reconstructions of Escherichia coli (2013, background): “It should be emphasized that although this review has focused on the metabolic network, it is but one of several networks actively at work inside the cell (Feist et al , 2009).”
Flux balance analysis of biological systems: applications and challenges (2009, methodology): “The draft models obtained from these are refined and enriched by incorporating genetic, biochemical data and known metabolic functional data [5], the reconstructed models containing stoichiometric information for proteins in the metabolic networks.”
Insights from 20 years of bacterial genome sequencing (2015, positive): “Based on the framework above, several predictive GEMs of prokaryotes (>85 GEMs for Bacteria and >6 GEMs for Achaea) were built and are widely used (Feist et al. 2009).”
BiGG Models: A platform for integrating, standardizing and sharing genome-scale models (2015, background): “They contain descriptions of all the biochemical reactions, metabolites and genes in metabolism for a specific organism––a Bi ochemical, G enetic and G enomic (BiGG) knowledge D516 Nucleic Acids Research, 2016, Vol. 44, Database issue base (7).”
The Role of Soil Microorganisms in Plant Mineral Nutrition—Current Knowledge and Future Directions (2017, methodology): “Under the general term network-based models (Milo et al., 2002; Feist et al., 2009) we include different static methods that have in common the treatment of metabolic pathways as networks, where metabolites are connected to reactions either as substrates or products.”
Systematic Evaluation of Methods for Integration of Transcriptomic Data into Constraint-Based Models of Metabolism (2014, background): “The simplicity and scalability of this approach, coupled with the advances in genome sequencing, has made constraint-based modeling a popular framework within the scientific community, and led to an explosion in the number of genome-scale metabolic reconstructions currently available [5].”
BiGG: a Biochemical Genetic and Genomic knowledgebase of large scale metabolic reconstructions (2010, methodology): “A general bottom-up metabolic reconstruction process has been formulated and detailed in [16,17].”
Expanding the role of reactive transport models in critical zone processes (2017, methodology): “First, it requires expertise in microbial ecology and geochemical modeling along with omics methods and bioinformatics (Feist et al., 2009).”
Probabilistic integrative modeling of genome-scale metabolic and regulatory networks in Escherichia coli and Mycobacterium tuberculosis (2010, methodology): “…construction of an integrated metabolic-regulatory network using PROM requires the following: ( i ) reconstructed genome-scale metabolic network (19); ( ii ) regulatory network structure, consisting of transcription factors (TFs) and their targets (20); ( iii ) abundant gene expression data, in…”
MSEA: a web-based tool to identify biologically meaningful patterns in quantitative metabolomic data (2010, methodology): “Information contained in the HMDB was used to extract common names, synonyms, as well as ID used in nine major metabolomic databases [HMDB, PubChem (19), ChEBI (20), KEGG (21), BiGG (22), METLIN (23), BioCyc (24), Reactome (25), and Wikipedia].”
Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods (2012, background): “…condition-specific, mathematically described, computable derivative of a metabolic reconstruction, containing comprehensive knowledge of metabolism. from careful biochemical and genetic studies or inferred from related organisms, and then catalogued in metabolic reconstruction knowledge bases 1,2 .”
Structure-based Systems Biology for Analyzing Off-target Binding (2011, background): “In order to apply constraint-based modeling, which has been tremendously successful in studying microorganisms [118,119], to investigate the cellular response of drug perturbations in human, two big challenges are: (1) reconstruction of a functional sub-network; and (2) simulation of the state…”