Machine Learning Predicts the Yeast Metabolome from the Quantitative Proteome of Kinase Knockouts

[1]  H. Akaike A new look at the statistical model identification , 1974 .

[2]  R. Sakia The Box-Cox transformation technique: a review , 1992 .

[3]  H. Kacser,et al.  The control of flux. , 1995, Biochemical Society transactions.

[4]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[5]  G. C. Johnston,et al.  A yeast glutamine tRNA signals nitrogen status for regulation of dimorphic growth and sporulation. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[6]  J. Pronk,et al.  Effect of Specific Growth Rate on Fermentative Capacity of Baker’s Yeast , 1998, Applied and Environmental Microbiology.

[7]  Ronald W. Davis,et al.  Functional characterization of the S. cerevisiae genome by gene deletion and parallel analysis. , 1999, Science.

[8]  D. Botstein,et al.  Genomic expression programs in the response of yeast cells to environmental changes. , 2000, Molecular biology of the cell.

[9]  D. Schomburg,et al.  Enzyme data and metabolic information: BRENDA, a resource for research in biology, biochemistry, and medicine , 2000 .

[10]  P. Herrero,et al.  The hexokinase 2 protein regulates the expression of the GLK1, HXK1 and HXK2 genes of Saccharomyces cerevisiae. , 2001, The Biochemical journal.

[11]  E. O’Shea,et al.  Global analysis of protein expression in yeast , 2003, Nature.

[12]  J. Selbig,et al.  Parallel analysis of transcript and metabolic profiles: a new approach in systems biology , 2003, EMBO reports.

[13]  H. Saito,et al.  Regulation of the osmoregulatory HOG MAPK cascade in yeast. , 2004, Journal of biochemistry.

[14]  C. Schilling,et al.  Flux coupling analysis of genome-scale metabolic network reconstructions. , 2004, Genome research.

[15]  A. Kudlicki,et al.  Logic of the Yeast Metabolic Cycle: Temporal Compartmentalization of Cellular Processes , 2005, Science.

[16]  J. Nielsen,et al.  Uncovering transcriptional regulation of metabolism by using metabolic network topology. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[17]  Gordon K. Smyth,et al.  limma: Linear Models for Microarray Data , 2005 .

[18]  J. Pronk,et al.  When transcriptome meets metabolome: fast cellular responses of yeast to sudden relief of glucose limitation , 2006, Molecular systems biology.

[19]  Gábor Csárdi,et al.  The igraph software package for complex network research , 2006 .

[20]  K. Beyenbach,et al.  The V-type H+ ATPase: molecular structure and function, physiological roles and regulation , 2006, Journal of Experimental Biology.

[21]  H. Kitano,et al.  Regulation of yeast oscillatory dynamics , 2007, Proceedings of the National Academy of Sciences.

[22]  Barbara M. Bakker,et al.  The fluxes through glycolytic enzymes in Saccharomyces cerevisiae are predominantly regulated at posttranscriptional levels , 2007, Proceedings of the National Academy of Sciences.

[23]  A. Hopper,et al.  Rapid and reversible nuclear accumulation of cytoplasmic tRNA in response to nutrient availability. , 2007, Molecular biology of the cell.

[24]  Markus J. Herrgård,et al.  A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology , 2008, Nature Biotechnology.

[25]  D. Koller,et al.  Activity motifs reveal principles of timing in transcriptional control of the yeast metabolic network , 2008, Nature Biotechnology.

[26]  Max Kuhn,et al.  Building Predictive Models in R Using the caret Package , 2008 .

[27]  Olga G. Troyanskaya,et al.  Coordinated Concentration Changes of Transcripts and Metabolites in Saccharomyces cerevisiae , 2009, PLoS Comput. Biol..

[28]  H. Sychrová,et al.  Saccharomyces cerevisiae BY4741 and W303-1A laboratory strains differ in salt tolerance. , 2010, Fungal biology.

[29]  Sanford Weisberg,et al.  An R Companion to Applied Regression , 2010 .

[30]  Zhaohui S. Qin,et al.  A Global Protein Kinase and Phosphatase Interaction Network in Yeast , 2010, Science.

[31]  Patrick G. A. Pedrioli,et al.  Phosphoproteomic Analysis Reveals Interconnected System-Wide Responses to Perturbations of Kinases and Phosphatases in Yeast , 2010, Science Signaling.

[32]  J. Ludwig,et al.  grofit: Fitting Biological Growth Curves with R , 2010 .

[33]  P. Kemmeren,et al.  Functional Overlap and Regulatory Links Shape Genetic Interactions between Signaling Pathways , 2010, Cell.

[34]  Joerg M. Buescher,et al.  Ultrahigh performance liquid chromatography-tandem mass spectrometry method for fast and robust quantification of anionic and aromatic metabolites. , 2010, Analytical chemistry.

[35]  G. Węgrzyn,et al.  Is tRNA only a translation factor or also a regulator of other processes? , 2010, Journal of Applied Genetics.

[36]  Costas D. Maranas,et al.  Improving the iMM904 S. cerevisiae metabolic model using essentiality and synthetic lethality data , 2010, BMC Systems Biology.

[37]  Kiran Raosaheb Patil,et al.  Metabolic Network Topology Reveals Transcriptional Regulatory Signatures of Type 2 Diabetes , 2010, PLoS Comput. Biol..

[38]  T. Pan,et al.  Selective control of amino acid metabolism by the GCN2 eIF2 kinase pathway in Saccharomyces cerevisiae , 2010, BMC Biochemistry.

[39]  Joerg M. Buescher,et al.  Tradeoff between enzyme and metabolite efficiency maintains metabolic homeostasis upon perturbations in enzyme capacity , 2010, Molecular systems biology.

[40]  Tong Zhang,et al.  Adaptive Forward-Backward Greedy Algorithm for Learning Sparse Representations , 2011, IEEE Transactions on Information Theory.

[41]  Peer Bork,et al.  iPath2.0: interactive pathway explorer , 2011, Nucleic Acids Res..

[42]  Gary King,et al.  Amelia II: A Program for Missing Data , 2011 .

[43]  R. Shaw,et al.  The AMPK signalling pathway coordinates cell growth, autophagy and metabolism , 2011, Nature Cell Biology.

[44]  H. Lehrach,et al.  Pyruvate Kinase Triggers a Metabolic Feedback Loop that Controls Redox Metabolism in Respiring Cells , 2011, Cell metabolism.

[45]  U. Sauer,et al.  Regulation of yeast central metabolism by enzyme phosphorylation , 2012, Molecular systems biology.

[46]  Edith D. Wong,et al.  Saccharomyces Genome Database: the genomics resource of budding yeast , 2011, Nucleic Acids Res..

[47]  R. Aebersold,et al.  Quantitative Analysis of Fission Yeast Transcriptomes and Proteomes in Proliferating and Quiescent Cells , 2012, Cell.

[48]  Ludovic C. Gillet,et al.  Targeted Data Extraction of the MS/MS Spectra Generated by Data-independent Acquisition: A New Concept for Consistent and Accurate Proteome Analysis* , 2012, Molecular & Cellular Proteomics.

[49]  Andrew E. Jaffe,et al.  Bioinformatics Applications Note Gene Expression the Sva Package for Removing Batch Effects and Other Unwanted Variation in High-throughput Experiments , 2022 .

[50]  A. C. Douglas,et al.  Functional wiring of the yeast kinome revealed by global analysis of genetic network motifs. , 2012, Genome research.

[51]  U. Sauer,et al.  A prototrophic deletion mutant collection for yeast metabolomics and systems biology , 2012, Nature Biotechnology.

[52]  D. Broomhead,et al.  A model of yeast glycolysis based on a consistent kinetic characterisation of all its enzymes , 2013, FEBS letters.

[53]  K. Lilley,et al.  The beauty of being (label)-free: sample preparation methods for SWATH-MS and next-generation targeted proteomics , 2013, F1000Research.

[54]  J. Stelling,et al.  Transcriptional regulation is insufficient to explain substrate-induced flux changes in Bacillus subtilis , 2013, Molecular systems biology.

[55]  Eric Smith,et al.  The compositional and evolutionary logic of metabolism , 2012, Physical biology.

[56]  U. Sauer,et al.  Large-scale functional analysis of the roles of phosphorylation in yeast metabolic pathways , 2014, Science Signaling.

[57]  J. Faraway Linear Models with R , 2014 .

[58]  Malika Charrad,et al.  NbClust: An R Package for Determining the Relevant Number of Clusters in a Data Set , 2014 .

[59]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[60]  Kiran Raosaheb Patil,et al.  Contribution of Network Connectivity in Determining the Relationship between Gene Expression and Metabolite Concentration Changes , 2014, PLoS Comput. Biol..

[61]  Nagarjuna Nagaraj,et al.  Global proteome turnover analyses of the Yeasts S. cerevisiae and S. pombe. , 2014, Cell reports.

[62]  M. Keller,et al.  Non‐enzymatic glycolysis and pentose phosphate pathway‐like reactions in a plausible Archean ocean , 2014, Molecular systems biology.

[63]  M. Mann,et al.  Minimal, encapsulated proteomic-sample processing applied to copy-number estimation in eukaryotic cells , 2014, Nature Methods.

[64]  M. Keller,et al.  The widespread role of non-enzymatic reactions in cellular metabolism , 2015, Current opinion in biotechnology.

[65]  Davide Heller,et al.  STRING v10: protein–protein interaction networks, integrated over the tree of life , 2014, Nucleic Acids Res..

[66]  Achim Zeileis,et al.  Diagnostic Checking in Regression Relationships , 2015 .

[67]  James A. Glazier,et al.  libRoadRunner 2.0: a high performance SBML simulation and analysis library , 2022, Bioinformatics.

[68]  Brendan MacLean,et al.  Building high-quality assay libraries for targeted analysis of SWATH MS data , 2015, Nature Protocols.

[69]  Antje Chang,et al.  BRENDA in 2015: exciting developments in its 25th year of existence , 2014, Nucleic Acids Res..

[70]  A. Zelezniak,et al.  Functional Metabolomics Describes the Yeast Biosynthetic Regulome , 2016, Cell.

[71]  Hadley Wickham,et al.  R for Data Science: Import, Tidy, Transform, Visualize, and Model Data , 2014 .

[72]  Henning Hermjakob,et al.  The Reactome pathway Knowledgebase , 2015, Nucleic acids research.

[73]  John D. Storey,et al.  Systems-level analysis of mechanisms regulating yeast metabolic flux , 2016, Science.

[74]  Kate Campbell,et al.  Saccharomyces cerevisiae single-copy plasmids for auxotrophy compensation, multiple marker selection, and for designing metabolically cooperating communities , 2016, F1000Research.

[75]  E. Hovig,et al.  Methods that remove batch effects while retaining group differences may lead to exaggerated confidence in downstream analyses , 2015, Biostatistics.

[76]  José A. Dianes,et al.  2016 update of the PRIDE database and its related tools , 2016, Nucleic Acids Res..

[77]  Minoru Kanehisa,et al.  KEGG as a reference resource for gene and protein annotation , 2015, Nucleic Acids Res..

[78]  R. Schwarz,et al.  The metabolic background is a global player in Saccharomyces gene expression epistasis , 2016, Nature Microbiology.

[79]  José A. Dianes,et al.  2016 update of the PRIDE database and its related tools , 2016, Nucleic Acids Res..

[80]  Pedro Beltrão,et al.  Systematic Analysis of Transcriptional and Post-transcriptional Regulation of Metabolism in Yeast , 2016, bioRxiv.

[81]  B. Palsson,et al.  Metabolic Models of Protein Allocation Call for the Kinetome. , 2017, Cell systems.

[82]  Pedro Mendes,et al.  Metabolic regulation is sufficient for global and robust coordination of glucose uptake, catabolism, energy production and growth in Escherichia coli , 2017, PLoS Comput. Biol..

[83]  M. Keller,et al.  The self-inhibitory nature of metabolic networks and its alleviation through compartmentalization , 2017, Nature Communications.

[84]  Benjamín J. Sánchez,et al.  Absolute Quantification of Protein and mRNA Abundances Demonstrate Variability in Gene-Specific Translation Efficiency in Yeast. , 2017, Cell systems.

[85]  A. González,et al.  Nutrient sensing and TOR signaling in yeast and mammals , 2017, The EMBO journal.

[86]  Christoph B. Messner,et al.  DIA-NN: Deep neural networks substantially improve the identification performance of Data-independent acquisition (DIA) in proteomics , 2018, bioRxiv.

[87]  Christoph B. Messner,et al.  DIA-NN: Neural networks and interference correction enable deep coverage in high-throughput proteomics , 2018 .

[88]  Roland Bruderer,et al.  Cost-effective generation of precise label-free quantitative proteomes in high-throughput by microLC and data-independent acquisition , 2018, Scientific Reports.