Integration of metabolic, regulatory and signaling networks towards analysis of perturbation and dynamic responses

[1]  A. Perelson Network thermodynamics. An overview. , 1975, Biophysical journal.

[2]  J. Bailey,et al.  Optimization of regulatory architectures in metabolic reaction networks , 1996, Biotechnology and bioengineering.

[3]  윤호주,et al.  Structural and Functional Study of Yer067w, a New Protein Involved in Yeast Metabolism Control and Drug Resistance , 2010, PloS one.

[4]  Vassily Hatzimaikatis,et al.  Bioinformatics and functional genomics: challenges and opportunities , 2000 .

[5]  B. Palsson,et al.  Regulation of gene expression in flux balance models of metabolism. , 2001, Journal of theoretical biology.

[6]  H. Kitano Systems Biology: A Brief Overview , 2002, Science.

[7]  H. Kitano,et al.  A comprehensive pathway map of epidermal growth factor receptor signaling , 2005, Molecular systems biology.

[8]  Sanjay Mehrotra,et al.  A model-based optimization framework for the inference of regulatory interactions using time-course DNA microarray expression data , 2007, BMC Bioinformatics.

[9]  Nan Xiao,et al.  Integrating metabolic, transcriptional regulatory and signal transduction models in Escherichia coli , 2008, Bioinform..

[10]  Markus J. Herrgård,et al.  Network-based prediction of human tissue-specific metabolism , 2008, Nature Biotechnology.

[11]  Erwin P. Gianchandani,et al.  Dynamic Analysis of Integrated Signaling, Metabolic, and Regulatory Networks , 2008, PLoS Comput. Biol..

[12]  Erwin P. Gianchandani,et al.  Correction: Dynamic Analysis of Integrated Signaling, Metabolic, and Regulatory Networks , 2008, PLoS Computational Biology.

[13]  Bernhard O. Palsson,et al.  Identification of Potential Pathway Mediation Targets in Toll-like Receptor Signaling , 2009, PLoS Comput. Biol..

[14]  Ljubisa Miskovic,et al.  Production of biofuels and biochemicals: in need of an ORACLE. , 2010, Trends in biotechnology.

[15]  B. Palsson,et al.  A protocol for generating a high-quality genome-scale metabolic reconstruction , 2010 .

[16]  N. Price,et al.  Probabilistic integrative modeling of genome-scale metabolic and regulatory networks in Escherichia coli and Mycobacterium tuberculosis , 2010, Proceedings of the National Academy of Sciences.

[17]  Keng C. Soh,et al.  Network thermodynamics in the post-genomic era. , 2010, Current opinion in microbiology.

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

[19]  Calin Belta,et al.  Integration of large-scale metabolic, signaling, and gene regulatory networks with application to infection responses , 2011, IEEE Conference on Decision and Control and European Control Conference.

[20]  Adam M. Feist,et al.  A comprehensive genome-scale reconstruction of Escherichia coli metabolism—2011 , 2011, Molecular systems biology.

[21]  Marcel J. T. Reinders,et al.  Predicting Metabolic Fluxes Using Gene Expression Differences As Constraints , 2011, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[22]  Nathan D. Price,et al.  Reconstruction of genome-scale metabolic models for 126 human tissues using mCADRE , 2012, BMC Systems Biology.

[23]  Doraiswami Ramkrishna,et al.  Dynamic models of metabolism: Review of the cybernetic approach , 2012 .

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

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

[26]  Intawat Nookaew,et al.  Mapping condition-dependent regulation of metabolism in yeast through genome-scale modeling , 2013, BMC Systems Biology.

[27]  Keng C. Soh,et al.  Towards kinetic modeling of genome-scale metabolic networks without sacrificing stoichiometric, thermodynamic and physiological constraints. , 2013, Biotechnology journal.

[28]  Nathan D. Price,et al.  Metabolic Constraint-Based Refinement of Transcriptional Regulatory Networks , 2013, PLoS Comput. Biol..

[29]  R. Overbeek,et al.  Automated genome annotation and metabolic model reconstruction in the SEED and Model SEED. , 2013, Methods in molecular biology.

[30]  Ali R. Zomorrodi,et al.  Optimization-driven identification of genetic perturbations accelerates the convergence of model parameters in ensemble modeling of metabolic networks. , 2013, Biotechnology journal.

[31]  Intawat Nookaew,et al.  The RAVEN Toolbox and Its Use for Generating a Genome-scale Metabolic Model for Penicillium chrysogenum , 2013, PLoS Comput. Biol..

[32]  L. Hood,et al.  P4 medicine: how systems medicine will transform the healthcare sector and society. , 2013, Personalized medicine.

[33]  Ronan M. T. Fleming,et al.  A community-driven global reconstruction of human metabolism , 2013, Nature Biotechnology.

[34]  C. Kahn,et al.  Insulin receptor signaling in normal and insulin-resistant states. , 2014, Cold Spring Harbor perspectives in biology.

[35]  E. Lander,et al.  Genetic Screens in Human Cells Using the CRISPR-Cas9 System , 2013, Science.

[36]  Derek N. Macklin,et al.  The future of whole-cell modeling. , 2014, Current opinion in biotechnology.

[37]  Tunahan Çakır,et al.  Metabolic Network Discovery by Top-Down and Bottom-Up Approaches and Paths for Reconciliation , 2014, Front. Bioeng. Biotechnol..

[38]  Wei-Sheng Wu,et al.  YTRP: a repository for yeast transcriptional regulatory pathways , 2014, Database J. Biol. Databases Curation.

[39]  Max A. Horlbeck,et al.  Genome-Scale CRISPR-Mediated Control of Gene Repression and Activation , 2014, Cell.

[40]  Fangfang Xia,et al.  Genome-scale bacterial transcriptional regulatory networks: reconstruction and integrated analysis with metabolic models , 2014, Briefings Bioinform..

[41]  Y. A. Son,et al.  Reconstruction of the temporal signaling network in Salmonella -infected human cells , 2015 .

[42]  V. Hatzimanikatis,et al.  Rites of passage: requirements and standards for building kinetic models of metabolic phenotypes. , 2015, Current opinion in biotechnology.

[43]  Edward J. O'Brien,et al.  Using Genome-scale Models to Predict Biological Capabilities , 2015, Cell.

[44]  V. Hatzimanikatis,et al.  Integrative approaches for signalling and metabolic networks. , 2015, Integrative biology : quantitative biosciences from nano to macro.

[45]  Edda Klipp,et al.  Network reconstruction and validation of the Snf1/AMPK pathway in baker’s yeast based on a comprehensive literature review , 2015, npj Systems Biology and Applications.

[46]  Canglin Wu,et al.  RegNetwork: an integrated database of transcriptional and post-transcriptional regulatory networks in human and mouse , 2015, Database J. Biol. Databases Curation.

[47]  E. Ferreira,et al.  Reconstructing genome-scale metabolic models with merlin , 2015, Nucleic acids research.

[48]  Ludovic Cottret,et al.  FlexFlux: combining metabolic flux and regulatory network analyses , 2015, BMC Systems Biology.

[49]  Jens Nielsen,et al.  Metabolic Needs and Capabilities of Toxoplasma gondii through Combined Computational and Experimental Analysis , 2015, PLoS Comput. Biol..

[50]  J. Buhmann,et al.  Inferring causal metabolic signals that regulate the dynamic TORC1-dependent transcriptome , 2015, Molecular systems biology.

[51]  Ram Rup Sarkar,et al.  Comparison of human cell signaling pathway databases—evolution, drawbacks and challenges , 2015, Database J. Biol. Databases Curation.

[52]  Nathan D. Price,et al.  Data-driven integration of genome-scale regulatory and metabolic network models , 2015, Front. Microbiol..

[53]  B. Kholodenko,et al.  The dynamic control of signal transduction networks in cancer cells , 2015, Nature Reviews Cancer.

[54]  David James Sherman,et al.  Pantograph: A template-based method for genome-scale metabolic model reconstruction , 2015, J. Bioinform. Comput. Biol..

[55]  Julio R. Banga,et al.  Exploring Design Principles of Gene Regulatory Networks via Pareto Optimality , 2016 .

[56]  Ali Ebrahim,et al.  Multi-omic data integration enables discovery of hidden biological regularities , 2016, Nature Communications.

[57]  Ljubisa Miskovic,et al.  iSCHRUNK--In Silico Approach to Characterization and Reduction of Uncertainty in the Kinetic Models of Genome-scale Metabolic Networks. , 2016, Metabolic engineering.

[58]  Eric Fanchon,et al.  HepatoDyn: A Dynamic Model of Hepatocyte Metabolism That Integrates 13C Isotopomer Data , 2016, PLoS Comput. Biol..

[59]  J. Keasling,et al.  The Need for Integrated Approaches in Metabolic Engineering. , 2016, Cold Spring Harbor perspectives in biology.

[60]  Anushya Muruganujan,et al.  PANTHER version 10: expanded protein families and functions, and analysis tools , 2015, Nucleic Acids Res..

[61]  O. Sawodny,et al.  Transition of an Anaerobic Escherichia coli Culture to Aerobiosis: Balancing mRNA and Protein Levels in a Demand-Directed Dynamic Flux Balance Analysis , 2016, PloS one.

[62]  Keng C. Soh,et al.  Identification of metabolic engineering targets for the enhancement of 1,4-butanediol production in recombinant E. coli using large-scale kinetic models. , 2016, Metabolic engineering.

[63]  Mark P. Styczynski,et al.  Multi-class and Multi-scale Models of Complex Biological Phenomena This Review Comes from a Themed Issue on Systems Biology Sciencedirect Classes and Scales of Computational Models Model Classification Molecular Protein Network Cellular , 2022 .

[64]  Rudiyanto Gunawan,et al.  Optimal design of gene knockout experiments for gene regulatory network inference , 2015, Bioinform..

[65]  Kumari Sonal Choudhary,et al.  EGFR Signal-Network Reconstruction Demonstrates Metabolic Crosstalk in EMT , 2016, PLoS Comput. Biol..

[66]  Steffen Klamt,et al.  MUFINS: multi-formalism interaction network simulator , 2016, npj Systems Biology and Applications.

[67]  R. P. Vivek-Ananth,et al.  Advances in the integration of transcriptional regulatory information into genome-scale metabolic models , 2016, bioRxiv.

[68]  Michael A. Saunders,et al.  solveME: fast and reliable solution of nonlinear ME models , 2016, BMC Bioinformatics.

[69]  Rachael P. Huntley,et al.  Gene regulation knowledge commons: community action takes care of DNA binding transcription factors , 2016, Database J. Biol. Databases Curation.

[70]  Nizamettin Aydin,et al.  Comprehensive review of association estimators for the inference of gene networks , 2016 .

[71]  U. Alon,et al.  Dynamical compensation in physiological circuits , 2016, Molecular systems biology.

[72]  J. Keasling,et al.  Synthetic and systems biology for microbial production of commodity chemicals , 2016, npj Systems Biology and Applications.

[73]  V. Hatzimanikatis,et al.  ATLAS of Biochemistry: A Repository of All Possible Biochemical Reactions for Synthetic Biology and Metabolic Engineering Studies. , 2016, ACS synthetic biology.

[74]  Anna Ritz,et al.  Pathways on demand: automated reconstruction of human signaling networks , 2016, npj Systems Biology and Applications.

[75]  B. Deplancke,et al.  Transcription factor proteomics—Tools, applications, and challenges , 2017, Proteomics.