Translation of Genotype to Phenotype by a Hierarchy of Cell Subsystems

[1]  C. Waddington.  Canalization of Development and the Inheritance of Acquired Characters , 1942, Nature.

[2]  Philip Resnik,et al.  Using Information Content to Evaluate Semantic Similarity in a Taxonomy , 1995, IJCAI.

[3]  Masaru Tomita,et al.  E-CELL: software environment for whole-cell simulation , 1999, Bioinform..

[4]  F. Netter,et al.  Supplemental References , 2002, We Came Naked and Barefoot.

[5]  M. Adams,et al.  Defects in SPT16 or POB3 (yFACT) in Saccharomyces cerevisiae cause dependence on the Hir/Hpc pathway: polymerase passage may degrade chromatin structure. , 2002, Genetics.

[6]  Masaru Tomita,et al.  E-Cell 2: Multi-platform E-Cell simulation system , 2003, Bioinform..

[7]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[8]  Hector J. Levesque,et al.  Knowledge Representation and Reasoning , 2004 .

[9]  G. Church,et al.  Modular epistasis in yeast metabolism , 2005, Nature Genetics.

[10]  Sean R. Collins,et al.  A strategy for extracting and analyzing large-scale quantitative epistatic interaction data , 2006, Genome Biology.

[11]  T. Ideker,et al.  Systematic interpretation of genetic interactions using protein networks , 2005, Nature Biotechnology.

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

[13]  E. Ruppin,et al.  Multiple knockout analysis of genetic robustness in the yeast metabolic network , 2006, Nature Genetics.

[14]  A. Tong,et al.  Synthetic genetic array analysis in Saccharomyces cerevisiae. , 2006, Methods in molecular biology.

[15]  Mike Tyers,et al.  BioGRID: a general repository for interaction datasets , 2005, Nucleic Acids Res..

[16]  Kevin Struhl,et al.  Asf1 mediates histone eviction and deposition during elongation by RNA polymerase II. , 2006, Molecular cell.

[17]  Christopher R. Myers,et al.  Universally Sloppy Parameter Sensitivities in Systems Biology Models , 2007, PLoS Comput. Biol..

[18]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[19]  F. Bruggeman,et al.  The nature of systems biology. , 2007, Trends in microbiology.

[20]  Carol Friedman,et al.  Information theory applied to the sparse gene ontology annotation network to predict novel gene function , 2007, ISMB/ECCB.

[21]  Bernhard O. Palsson,et al.  Connecting Extracellular Metabolomic Measurements to Intracellular Flux States in Yeast , 2022 .

[22]  Trey Ideker,et al.  Functional Maps of Protein Complexes from Quantitative Genetic Interaction Data , 2008, PLoS Comput. Biol..

[23]  Wenyuan Li,et al.  Mapping Genetically Compensatory Pathways from Synthetic Lethal Interactions in Yeast , 2008, PloS one.

[24]  Robert P. St.Onge,et al.  The Chemical Genomic Portrait of Yeast: Uncovering a Phenotype for All Genes , 2008, Science.

[25]  R. Shamir,et al.  From E-MAPs to module maps: dissecting quantitative genetic interactions using physical interactions , 2008, Molecular systems biology.

[26]  J. Bader,et al.  Finding friends and enemies in an enemies-only network: a graph diffusion kernel for predicting novel genetic interactions and co-complex membership from yeast genetic interactions. , 2008, Genome research.

[27]  Phillip W. Lord,et al.  Semantic Similarity in Biomedical Ontologies , 2009, PLoS Comput. Biol..

[28]  Maksat Ashyraliyev,et al.  Systems biology: parameter estimation for biochemical models , 2009, The FEBS journal.

[29]  M. Vingron,et al.  Elucidating regulatory mechanisms downstream of a signaling pathway using informative experiments , 2009, Molecular systems biology.

[30]  William W. Chen,et al.  Classic and contemporary approaches to modeling biochemical reactions. , 2010, Genes & development.

[31]  Nevan J Krogan,et al.  Quantitative genetic interaction mapping using the E-MAP approach. , 2010, Methods in enzymology.

[32]  Vipin Kumar,et al.  An Integrative Multi-Network and Multi-Classifier Approach to Predict Genetic Interactions , 2010, PLoS Comput. Biol..

[33]  A. Fraser,et al.  Predicting genetic modifier loci using functional gene networks. , 2010, Genome research.

[34]  Gary D Bader,et al.  Quantitative analysis of fitness and genetic interactions in yeast on a genome scale , 2010, Nature Methods.

[35]  H. Hakonarson,et al.  Analysing biological pathways in genome-wide association studies , 2010, Nature Reviews Genetics.

[36]  Robin D Dowell,et al.  Genotype to Phenotype: A Complex Problem , 2010, Science.

[37]  Bruce Tidor,et al.  Sloppy models, parameter uncertainty, and the role of experimental design. , 2010, Molecular bioSystems.

[38]  Gary D Bader,et al.  The Genetic Landscape of a Cell , 2010, Science.

[39]  Jeffrey D Orth,et al.  What is flux balance analysis? , 2010, Nature Biotechnology.

[40]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[41]  D. Hanahan,et al.  Hallmarks of Cancer: The Next Generation , 2011, Cell.

[42]  D. Pe’er,et al.  Principles and Strategies for Developing Network Models in Cancer , 2011, Cell.

[43]  Wolfgang Weiss,et al.  A Computational Systems Biology Software Platform for Multiscale Modeling and Simulation: Integrating Whole-Body Physiology, Disease Biology, and Molecular Reaction Networks , 2011, Front. Physio..

[44]  E. Marcotte,et al.  Prioritizing candidate disease genes by network-based boosting of genome-wide association data. , 2011, Genome research.

[45]  Gowtham Atluri,et al.  Putting genetic interactions in context through a global modular decomposition. , 2011, Genome research.

[46]  Lenore Cowen,et al.  Inferring Mechanisms of Compensation from E-MAP and SGA Data Using Local Search Algorithms for Max Cut , 2011, J. Comput. Biol..

[47]  Vittorio Cristini,et al.  Multiscale cancer modeling. , 2010, Annual review of biomedical engineering.

[48]  S. Oliver,et al.  An integrated approach to characterize genetic interaction networks in yeast metabolism , 2011, Nature Genetics.

[49]  Miguel Rocha,et al.  Modeling formalisms in Systems Biology , 2011, AMB Express.

[50]  E. Lander,et al.  The mystery of missing heritability: Genetic interactions create phantom heritability , 2012, Proceedings of the National Academy of Sciences.

[51]  A. Butte,et al.  Leveraging models of cell regulation and GWAS data in integrative network-based association studies , 2012, Nature Genetics.

[52]  Paul Pavlidis,et al.  “Guilt by Association” Is the Exception Rather Than the Rule in Gene Networks , 2012, PLoS Comput. Biol..

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

[54]  David Haussler,et al.  PARADIGM-SHIFT predicts the function of mutations in multiple cancers using pathway impact analysis , 2012, Bioinform..

[55]  P. Sullivan.  Puzzling over schizophrenia: Schizophrenia as a pathway disease , 2012, Nature Medicine.

[56]  Jason H. Moore,et al.  Pathway analysis of genomic data: concepts, methods, and prospects for future development. , 2012, Trends in genetics : TIG.

[57]  Jonathan R. Karr,et al.  A Whole-Cell Computational Model Predicts Phenotype from Genotype , 2012, Cell.

[58]  T. Ideker,et al.  Differential network biology , 2012, Molecular systems biology.

[59]  Jeffrey D. Orth,et al.  In silico method for modelling metabolism and gene product expression at genome scale , 2012, Nature Communications.

[60]  Gary D Bader,et al.  A travel guide to Cytoscape plugins , 2012, Nature Methods.

[61]  Wei Niu,et al.  Coexpression Networks Implicate Human Midfetal Deep Cortical Projection Neurons in the Pathogenesis of Autism , 2013, Cell.

[62]  Andrew M. Gross,et al.  Network-based stratification of tumor mutations , 2013, Nature Methods.

[63]  T. Przytycka,et al.  Bridging the Gap between Genotype and Phenotype via Network Approaches , 2013, Front. Genet..

[64]  Kimberly Van Auken,et al.  A guide to best practices for Gene Ontology (GO) manual annotation , 2013, Database J. Biol. Databases Curation.

[65]  Edward J. O'Brien,et al.  Genome-scale models of metabolism and gene expression extend and refine growth phenotype prediction , 2013, Molecular systems biology.

[66]  S. Jenna,et al.  Genetic interaction networks: better understand to better predict , 2013, Front. Genet..

[67]  T. Ideker,et al.  A gene ontology inferred from molecular networks , 2012, Nature Biotechnology.

[68]  Shayn M Peirce,et al.  Multiscale computational models of complex biological systems. , 2013, Annual review of biomedical engineering.

[69]  Kerry Bloom,et al.  Systematic triple-mutant analysis uncovers functional connectivity between pathways involved in chromosome regulation. , 2013, Cell reports.

[70]  Ben Lehner.  Genotype to phenotype: lessons from model organisms for human genetics , 2013, Nature Reviews Genetics.

[71]  Vineet Bafna,et al.  Inferring gene ontologies from pairwise similarity data , 2014, Bioinform..

[72]  E Skafidas,et al.  Predicting the diagnosis of autism spectrum disorder using gene pathway analysis , 2012, Molecular Psychiatry.

[73]  Tony Sawford,et al.  Understanding how and why the Gene Ontology and its annotations evolve: the GO within UniProt , 2014, GigaScience.

[74]  Samantha A. Morris,et al.  CellNet: Network Biology Applied to Stem Cell Engineering , 2014, Cell.

[75]  T. Mackay.  Epistasis and quantitative traits: using model organisms to study gene–gene interactions , 2013, Nature Reviews Genetics.

[76]  T. Ideker,et al.  Siri of the Cell: What Biology Could Learn from the iPhone , 2014, Cell.

[77]  Natasa Przulj,et al.  Integration of molecular network data reconstructs Gene Ontology , 2014, Bioinform..

[78]  Susumu Goto,et al.  Data, information, knowledge and principle: back to metabolism in KEGG , 2013, Nucleic Acids Res..

[79]  Ilias Tagkopoulos,et al.  An integrative, multi-scale, genome-wide model reveals the phenotypic landscape of Escherichia coli , 2014, Molecular systems biology.

[80]  Gary D. Bader,et al.  Mapping the Cellular Response to Small Molecules Using Chemogenomic Fitness Signatures , 2014, Science.

[81]  Hyojin Kim,et al.  YeastNet v3: a public database of data-specific and integrated functional gene networks for Saccharomyces cerevisiae , 2013, Nucleic Acids Res..

[82]  M. Sánchez Del Pino,et al.  Unveiling novel interactions of histone chaperone Asf1 linked to TREX-2 factors Sus1 and Thp1 , 2014, Nucleus.

[83]  Benjamin J. Raphael,et al.  Pan-Cancer Network Analysis Identifies Combinations of Rare Somatic Mutations across Pathways and Protein Complexes , 2014, Nature Genetics.

[84]  Juancarlos Chan,et al.  Gene Ontology Consortium: going forward , 2014, Nucleic Acids Res..

[85]  Xiangxue Wang.  An Integrative Multi-Network and Multi-Classifier Approach to Predict Genetic Interactions , 2015 .

[86]  Jing Chen,et al.  NDEx, the Network Data Exchange. , 2015, Cell systems.

[87]  Daniel S. Himmelstein,et al.  Understanding multicellular function and disease with human tissue-specific networks , 2015, Nature Genetics.