Mechanistic systems modeling to guide drug discovery and development.

[1]  Y. Z. Ider,et al.  Quantitative estimation of insulin sensitivity. , 1979, The American journal of physiology.

[2]  R. Bergman,et al.  Physiologic evaluation of factors controlling glucose tolerance in man: measurement of insulin sensitivity and beta-cell glucose sensitivity from the response to intravenous glucose. , 1981, The Journal of clinical investigation.

[3]  D. D’Alessio,et al.  Enteral Enhancement of Glucose Disposition by Both Insulin-Dependent and Insulin-Independent Processes: A Physiological Role of Glucagon-Like Peptide I , 1995, Diabetes.

[4]  Cynthia L. Stokes,et al.  Biological systems modeling: Powerful discipline for biomedical e‐R&D , 2000 .

[5]  J. DiMasi New drug development in the United States from 1963 to 1999 , 2001, Clinical pharmacology and therapeutics.

[6]  J. Deisenhofer,et al.  Structural Mechanism for Statin Inhibition of HMG-CoA Reductase , 2001, Science.

[7]  C J Musante,et al.  Small- and large-scale biosimulation applied to drug discovery and development. , 2002, Drug discovery today.

[8]  R. Bergman,et al.  The evolution of β‐cell dysfunction and insulin resistance in type 2 diabetes , 2002, European journal of clinical investigation.

[9]  Patrick Poulin,et al.  Prediction of pharmacokinetics prior to in vivo studies. II. Generic physiologically based pharmacokinetic models of drug disposition. , 2002, Journal of pharmaceutical sciences.

[10]  R. W. Hansen,et al.  The price of innovation: new estimates of drug development costs. , 2003, Journal of health economics.

[11]  J. Nielsen,et al.  Integration of gene expression data into genome-scale metabolic models. , 2004, Metabolic engineering.

[12]  Ping Zhang,et al.  Guidelines for computer modeling of diabetes and its complications , 2004 .

[13]  Philip J. Barter,et al.  Intensive lipid lowering with atorvastatin in patients with stable coronary disease. , 2005, The New England journal of medicine.

[14]  Alex Bangs,et al.  Predictive biosimulation and virtual patients in pharmaceutical R and D. , 2005, Studies in health technology and informatics.

[15]  Nadine A Defranoux,et al.  In Silico Modeling and Simulation of Bone Biology: A Proposal , 2005, Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research.

[16]  Michael P Gulseth,et al.  Ximelagatran: an orally active direct thrombin inhibitor. , 2005, American journal of health-system pharmacy : AJHP : official journal of the American Society of Health-System Pharmacists.

[17]  J. Rullmann,et al.  Systems biology for battling rheumatoid arthritis: application of the Entelos PhysioLab platform. , 2005, Systems biology.

[18]  A. Kansal,et al.  Application of predictive biosimulation within pharmaceutical clinical development: examples of significance for translational medicine and clinical trial design. , 2005, Systems biology.

[19]  Van V. Brantner,et al.  Estimating the cost of new drug development: is it really 802 million dollars? , 2006, Health affairs.

[20]  Seth Michelson,et al.  In silico prediction of clinical efficacy. , 2006, Current opinion in biotechnology.

[21]  G. Clermont,et al.  MATHEMATICAL MODEL PREDICTING OUTCOMES OF SEPSIS PATIENTS TREATED WITH XIGRIS®: ENHANCE TRIAL , 2006 .

[22]  Hugo Kubinyi,et al.  Success Stories of Computer‐Aided Design , 2006 .

[23]  B. Palsson,et al.  Towards multidimensional genome annotation , 2006, Nature Reviews Genetics.

[24]  D. Kell Systems biology, metabolic modelling and metabolomics in drug discovery and development. , 2006, Drug discovery today.

[25]  Michael Hucka,et al.  SBMLToolbox: an SBML toolbox for MATLAB users , 2006, Bioinform..

[26]  Alex Lawrence Bangs,et al.  Target Identification and Validation Using Human Simulation Models , 2006 .

[27]  A. Tall,et al.  The failure of torcetrapib: was it the molecule or the mechanism? , 2006, Arteriosclerosis, thrombosis, and vascular biology.

[28]  S. Klamt,et al.  GSMN-TB: a web-based genome-scale network model of Mycobacterium tuberculosis metabolism , 2007, Genome Biology.

[29]  M. Caulfield,et al.  Effects of torcetrapib in patients at high risk for coronary events. , 2007, The New England journal of medicine.

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

[31]  S. Nissen,et al.  Effect of rosiglitazone on the risk of myocardial infarction and death from cardiovascular causes. , 2007, The New England journal of medicine.

[32]  B. Palsson,et al.  Genome-scale Reconstruction of Metabolic Network in Bacillus subtilis Based on High-throughput Phenotyping and Gene Essentiality Data* , 2007, Journal of Biological Chemistry.

[33]  J. Trimmer,et al.  APPLICATION OF PREDICTIVE BIOSIMULATION TO THE STUDY OF ATHEROSCLEROSIS: DEVELOPMENT OF THE CARDIOVASCULAR PHYSIOLAB ® PLATFORM AND EVALUATION OF CETP INHIBITOR THERAPY , 2007 .

[34]  Bernhard O. Palsson,et al.  Investigating the metabolic capabilities of Mycobacterium tuberculosis H37Rv using the in silico strain iNJ661 and proposing alternative drug targets , 2007 .

[35]  Anthony Dowell,et al.  Main morbidities recorded in the women's international study of long duration oestrogen after menopause (WISDOM): a randomised controlled trial of hormone replacement therapy in postmenopausal women , 2007, BMJ : British Medical Journal.

[36]  O. Demin,et al.  The Edinburgh human metabolic network reconstruction and its functional analysis , 2007, Molecular systems biology.

[37]  Monica L. Mo,et al.  Global reconstruction of the human metabolic network based on genomic and bibliomic data , 2007, Proceedings of the National Academy of Sciences.

[38]  B. Palsson,et al.  Systems analysis of energy metabolism elucidates the affected respiratory chain complex in Leigh's syndrome. , 2007, Molecular genetics and metabolism.

[39]  Neema Jamshidi,et al.  A genome-scale, constraint-based approach to systems biology of human metabolism. , 2007, Molecular bioSystems.

[40]  John A Wagner,et al.  Effect of the cholesteryl ester transfer protein inhibitor, anacetrapib, on lipoproteins in patients with dyslipidaemia and on 24-h ambulatory blood pressure in healthy individuals: two double-blind, randomised placebo-controlled phase I studies , 2007, The Lancet.

[41]  Jason A. Papin,et al.  Systems analysis of metabolism in the pathogenic trypanosomatid Leishmania major , 2008, Molecular systems biology.

[42]  Bernhard O. Palsson,et al.  Context-Specific Metabolic Networks Are Consistent with Experiments , 2008, PLoS Comput. Biol..

[43]  Jason A. Papin,et al.  * Corresponding authors , 2006 .

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

[45]  W-C Hwang,et al.  Identification of Information Flow‐Modulating Drug Targets: A Novel Bridging Paradigm for Drug Discovery , 2008, Clinical pharmacology and therapeutics.

[46]  Bernhard O. Palsson,et al.  Constraint-based analysis of metabolic capacity of Salmonella typhimurium during host-pathogen interaction , 2009, BMC Systems Biology.

[47]  Jason A. Papin,et al.  Applications of genome-scale metabolic reconstructions , 2009, Molecular systems biology.

[48]  E. Ruppin,et al.  Predicting metabolic biomarkers of human inborn errors of metabolism , 2009, Molecular systems biology.

[49]  George Steiner,et al.  Safety and tolerability of dalcetrapib. , 2009, The American journal of cardiology.

[50]  Simon Daefler,et al.  Systems approach to investigating host-pathogen interactions in infections with the biothreat agent Francisella. Constraints-based model of Francisella tularensis , 2010, BMC Systems Biology.

[51]  Bernhard O. Palsson,et al.  BiGG: a Biochemical Genetic and Genomic knowledgebase of large scale metabolic reconstructions , 2010, BMC Bioinformatics.

[52]  Eytan Ruppin,et al.  Network-based prediction of metabolic enzymes' subcellular localization , 2009, Bioinform..

[53]  A Lawrence Gould,et al.  Design of the DEFINE trial: determining the EFficacy and tolerability of CETP INhibition with AnacEtrapib. , 2009, American heart journal.

[54]  Neema Jamshidi,et al.  Genome-scale network analysis of imprinted human metabolic genes , 2009, Epigenetics.

[55]  Philip E. Bourne,et al.  Drug Discovery Using Chemical Systems Biology: Identification of the Protein-Ligand Binding Network To Explain the Side Effects of CETP Inhibitors , 2009, PLoS Comput. Biol..

[56]  Daniel Bloomfield,et al.  Efficacy and safety of the cholesteryl ester transfer protein inhibitor anacetrapib as monotherapy and coadministered with atorvastatin in dyslipidemic patients. , 2009, American heart journal.

[57]  A. Barabasi,et al.  Targets Drug Genomes Identify Novel Antimicrobial Staphylococcus Aureus of Multiple Reconstruction and Flux Balance Analysis Comparative Genome-scale Metabolic Supplemental Material , 2009 .

[58]  Ravi Iyengar,et al.  Network analyses in systems pharmacology , 2009, Bioinform..

[59]  Jaques Reifman,et al.  A systems biology framework for modeling metabolic enzyme inhibition of Mycobacterium tuberculosis , 2009, BMC Systems Biology.

[60]  Steven B Waters,et al.  Treatment with Sitagliptin or Metformin Does Not Increase Body Weight despite Predicted Reductions in Urinary Glucose Excretion , 2009, Journal of diabetes science and technology.

[61]  J Sarkar,et al.  Mathematical modeling of community-acquired pneumonia patients , 2009, Critical Care.

[62]  V. Schachter,et al.  Genome-scale models of bacterial metabolism: reconstruction and applications , 2008, FEMS microbiology reviews.

[63]  Adam M. Feist,et al.  Reconstruction of biochemical networks in microorganisms , 2009, Nature Reviews Microbiology.

[64]  Nagasuma R. Chandra,et al.  Flux balance analysis of biological systems: applications and challenges , 2009, Briefings Bioinform..

[65]  Jean-Claude Tardif,et al.  Rationale and design of the dal-OUTCOMES trial: efficacy and safety of dalcetrapib in patients with recent acute coronary syndrome. , 2009, American heart journal.

[66]  Desmond S. Lun,et al.  Interpreting Expression Data with Metabolic Flux Models: Predicting Mycobacterium tuberculosis Mycolic Acid Production , 2009, PLoS Comput. Biol..

[67]  Bernhard O. Palsson,et al.  A detailed genome-wide reconstruction of mouse metabolism based on human Recon 1 , 2010, BMC Systems Biology.

[68]  Eytan Ruppin,et al.  iMAT: an integrative metabolic analysis tool , 2010, Bioinform..

[69]  Xiaobo Zhou,et al.  Predicting enzyme targets for cancer drugs by profiling human Metabolic reactions in NCI-60 cell lines , 2010, BMC Bioinformatics.

[70]  Eytan Ruppin,et al.  Integrating quantitative proteomics and metabolomics with a genome-scale metabolic network model , 2010, Bioinform..

[71]  Rajiv Mahajan,et al.  Food and drug administration’s critical path initiative and innovations in drug development paradigm: Challenges, progress, and controversies , 2010, Journal of pharmacy & bioallied sciences.

[72]  C. Gille,et al.  HepatoNet1: a comprehensive metabolic reconstruction of the human hepatocyte for the analysis of liver physiology , 2010, Molecular systems biology.

[73]  Jens Timmer,et al.  Systems biology of mammalian cells: a report from the Freiburg conference. , 2010, BioEssays : news and reviews in molecular, cellular and developmental biology.

[74]  Xiang-Sun Zhang,et al.  Drug Target Identification Based on Flux Balance Analysis of Metabolic Networks , 2010 .

[75]  Jennifer G. Robinson,et al.  Safety and tolerability of dalcetrapib (RO4607381/JTT-705): results from a 48-week trial , 2010, European heart journal.

[76]  Suhua Chang,et al.  Exploring the metabolic network of the epidemic pathogen Burkholderia cenocepacia J2315 via genome-scale reconstruction , 2011, BMC Systems Biology.

[77]  S. Lee,et al.  Genome-scale metabolic network analysis and drug targeting of multi-drug resistant pathogen Acinetobacter baumannii AYE. , 2010, Molecular bioSystems.

[78]  Christopher P Cannon,et al.  Safety of anacetrapib in patients with or at high risk for coronary heart disease. , 2010, The New England journal of medicine.

[79]  G. Mills,et al.  Future of personalized medicine in oncology: a systems biology approach. , 2010, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[80]  Philip E. Bourne,et al.  Drug Off-Target Effects Predicted Using Structural Analysis in the Context of a Metabolic Network Model , 2010, PLoS Comput. Biol..

[81]  Jean-Marie C. Bouteiller,et al.  Computational studies of NMDA receptors: differential effects of neuronal activity on efficacy of competitive and non-competitive antagonists. , 2010, Open access bioinformatics.

[82]  Arnold von Eckardstein,et al.  Mulling over the odds of CETP inhibition , 2010 .

[83]  James H. Doroshow,et al.  AACR-FDA-NCI Cancer Biomarkers Collaborative Consensus Report: Advancing the Use of Biomarkers in Cancer Drug Development , 2010, Clinical Cancer Research.

[84]  K Gadkar,et al.  The Type 1 Diabetes PhysioLab® Platform: a validated physiologically based mathematical model of pathogenesis in the non‐obese diabetic mouse , 2010, Clinical and experimental immunology.

[85]  B. Palsson,et al.  Insight into human alveolar macrophage and M. tuberculosis interactions via metabolic reconstructions , 2010, Molecular systems biology.

[86]  Adilson E Motter,et al.  Improved network performance via antagonism: From synthetic rescues to multi-drug combinations , 2010, BioEssays : news and reviews in molecular, cellular and developmental biology.

[87]  R Sarangapani,et al.  A SYSTEMS MODELING APPROACH TO UNDERSTANDING THE MECHANISMS OF RENAL PROTECTION OBSERVED IN THE AVOID STUDY: 5C.02 , 2010 .

[88]  E. Ruppin,et al.  Computational reconstruction of tissue-specific metabolic models: application to human liver metabolism , 2010, Molecular systems biology.

[89]  B. Palsson,et al.  Large-scale in silico modeling of metabolic interactions between cell types in the human brain , 2010, Nature Biotechnology.

[90]  Jennifer G. Robinson,et al.  Dalcetrapib: a review of Phase II data , 2010, Expert opinion on investigational drugs.

[91]  Neema Jamshidi,et al.  Mass action stoichiometric simulation models: incorporating kinetics and regulation into stoichiometric models. , 2010, Biophysical journal.

[92]  James A. Eddy,et al.  In silico models of cancer , 2010, Wiley interdisciplinary reviews. Systems biology and medicine.

[93]  C. Gallen,et al.  Strategic challenges in neurotherapeutic pharmaceutical development , 2011, NeuroRX.

[94]  Jason A. Papin,et al.  TIGER: Toolbox for integrating genome-scale metabolic models, expression data, and transcriptional regulatory networks , 2011, BMC Systems Biology.

[95]  E. Ruppin,et al.  Predicting selective drug targets in cancer through metabolic networks , 2011, Molecular systems biology.

[96]  Roded Sharan,et al.  Genome-Scale Metabolic Modeling Elucidates the Role of Proliferative Adaptation in Causing the Warburg Effect , 2011, PLoS Comput. Biol..

[97]  Jason A. Papin,et al.  Functional integration of a metabolic network model and expression data without arbitrary thresholding , 2011, Bioinform..

[98]  Xiang-Sun Zhang,et al.  Two-stage flux balance analysis of metabolic networks for drug target identification , 2011, BMC Systems Biology.

[99]  Anna Georgieva,et al.  Using a Systems Biology Approach to Explore Hypotheses Underlying Clinical Diversity of the Renin Angiotensin System and the Response to Antihypertensive Therapies , 2011 .

[100]  Aarash Bordbar,et al.  iAB-RBC-283: A proteomically derived knowledge-base of erythrocyte metabolism that can be used to simulate its physiological and patho-physiological states , 2011, BMC Systems Biology.

[101]  Aarash Bordbar,et al.  A multi-tissue type genome-scale metabolic network for analysis of whole-body systems physiology , 2011, BMC Systems Biology.

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

[103]  Gabriela Kalna,et al.  Haem oxygenase is synthetically lethal with the tumour suppressor fumarate hydratase , 2011, Nature.

[104]  Russ B. Altman,et al.  Bioinformatics challenges for personalized medicine , 2011, Bioinform..

[105]  Ronan M. T. Fleming,et al.  Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0 , 2007, Nature Protocols.

[106]  C. Allaart,et al.  Identification of CXCL13 as a marker for rheumatoid arthritis outcome using an in silico model of the rheumatic joint. , 2011, Arthritis and rheumatism.

[107]  Karim Wahba,et al.  Abstract 9560: Clinical Trial Simulations of Dyslipidemic Patients in a Mechanistic Model of Cardiovascular Disease Predict Little Impact on CHD Events by CETP Inhibitors , 2011 .

[108]  Jaques Reifman,et al.  Modeling synergistic drug inhibition of Mycobacterium tuberculosis growth in murine macrophages. , 2011, Molecular bioSystems.

[109]  Seth I. Berger,et al.  Role of systems pharmacology in understanding drug adverse events , 2011, Wiley interdisciplinary reviews. Systems biology and medicine.

[110]  Christoph Kaleta,et al.  In Silico Evidence for Gluconeogenesis from Fatty Acids in Humans , 2011, PLoS Comput. Biol..

[111]  F. Pammolli,et al.  The productivity crisis in pharmaceutical R&D , 2011, Nature Reviews Drug Discovery.

[112]  S. Lee,et al.  Integrative genome-scale metabolic analysis of Vibrio vulnificus for drug targeting and discovery , 2011, Molecular systems biology.

[113]  J. DiMasi The Value of Improving the Productivity of the Drug Development Process , 2012, PharmacoEconomics.

[114]  Jason A. Papin,et al.  Metabolic network analysis predicts efficacy of FDA-approved drugs targeting the causative agent of a neglected tropical disease , 2012, BMC Systems Biology.

[115]  A. Bordbar,et al.  Using the reconstructed genome‐scale human metabolic network to study physiology and pathology , 2012, Journal of internal medicine.

[116]  Ernesto S. Nakayasu,et al.  Model-driven multi-omic data analysis elucidates metabolic immunomodulators of macrophage activation , 2012, Molecular systems biology.

[117]  Natapol Pornputtapong,et al.  Reconstruction of Genome-Scale Active Metabolic Networks for 69 Human Cell Types and 16 Cancer Types Using INIT , 2012, PLoS Comput. Biol..