Network-based prediction of human tissue-specific metabolism

By Tomer Shlomi, M. Cabili, Markus J. Herrgård, Bernhard O. Palsson, Eytan Ruppin. Nature Biotechnology, 2008

681 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/5d5162e8c8286bad287d538b70f929202c7a9eda

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In Molecular Biology

Citations in the corpus

Enhancing the Prioritization of Disease-Causing Genes through Tissue Specific Protein Interaction Networks (2012, background): “Of note, the lack of tissue specific PPI networks stands in marked difference from the existence of many tissue- and cellspecific variants of other types of biological networks, such as regulatory networks [30–32], functional linkage networks [33,34] and metabolic networks [35–37].”
Applications of genome-scale metabolic reconstructions (2009, methodology): “For instance, multiple data types were used in concert with a metabolic GENRE to determine tissue-specific metabolic activities in H. sapiens (Shlomi et al, 2008) and to compare the filamentous-form versus the yeast form of S. cerevisiae (Prinz et al, 2004).”
Using Genome-Scale Models to Predict Biological Capabilities (2015, positive): “This model summarizes the experimental data in a self-consistent and integrated format and forms the starting point for further computational and biological inquiry (Agren et al., 2012; Shlomi et al., 2008) (see Figures 5D and 5E).”
Network-based machine learning and graph theory algorithms for precision oncology (2017, methodology): “Signaling pathway impact analysis (SPIA) 60 and mixed integer programming (MILP) 61 are two examples of earlier pathway-based methods for genomic data analysis.”
Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods (2012, methodology): “106) using transcriptomic data from three types of M. tuberculosis infection.”
Structure-based Systems Biology for Analyzing Off-target Binding (2011, background): “Recent advances in the proteome-wide reconstruction of human metabolic networks [115] and algorithms for context-specific modeling [116,117] make it possible to simulate drug induced perturbations in human.”
Integrative approaches for finding modular structure in biological networks (2013, methodology): “For example, a genome-scale reconstruction of a human metabolic network (curated from published data) was constrained using quantitative measures of gene and protein expression to predict tissue-specific metabolic uptake and release 91 .”
Reconstruction of Genome-Scale Active Metabolic Networks for 69 Human Cell Types and 16 Cancer Types Using INIT (2012, methodology): “. These reconstructions, however, are not tissue specific, which prevents their applicability to the study of particular human cell types or diseases. Tissue specific transcription profiles were used to generate tissue specific models for 10 different human tissues”
Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review (2012, background): “However, it should be kept in mind that metabolic network data are often still incomplete, and often reflect optimal growth conditions (Edwards & Palsson, 2000a; Förster et al., 2003; Duarte et al., 2007; Ma et al., 2007; Shlomi et al., 2008; Shlomi et al., 2009; Folger et al., 2011; Holme, 2011; Ch”
Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0 (2007, neutral): “More recently, transcriptome data have been used to reduce the size of the set of computed feasible state”
iMAT: an integrative metabolic analysis tool (2010, neutral): “Utilizing gene and protein expression to predict metabolic flux is a challenging task due to the complex mapping between the two.”
Modeling Oncogenic Signaling in Colon Tumors by Multidirectional Analyses of Microarray Data Directed for Maximization of Analytical Reliability (2010, background): “This assumption may be correct for most metabolic pathways that rely on the mass-action law [48] but may not hold true for signaling pathways.”
Systematic Evaluation of Methods for Integration of Transcriptomic Data into Constraint-Based Models of Metabolism (2014, methodology): “The integrative metabolic analysis tool (iMAT) [16] implements a method proposed by Shlomi and coworkers for finding tissue-specific activity in the human metabolic reconstruction [21].”
Constraint-based models predict metabolic and associated cellular functions (2014, methodology): “…reaction bounds on the basis of mRNA and protein expression data, either by setting the bounds to zero for reactions that correspond to absent transcripts and proteins 34,35 (see the figure, part a ) or by linearly adjusting the bounds on the basis of transcript and protein abundances 103 .”
Probabilistic integrative modeling of genome-scale metabolic and regulatory networks in Escherichia coli and Mycobacterium tuberculosis (2010, background): “(22) and use these constraints as cues to determine the most likely flux through a particular enzyme.”
Tissue-Specific Functional Networks for Prioritizing Phenotype and Disease Genes (2012, background): “functionality is the modulation of gene expression levels between tissues [16,17,18].”
Interpreting Expression Data with Metabolic Flux Models: Predicting Mycobacterium tuberculosis Mycolic Acid Production (2009, positive): “turns off genes whose expression is below a given threshold level. If this constrained model is incapable of achieving a given objective, genes are turned back on until the objective is reachable. The method of Shlomi et al.”