Connecting the Dots: Applications of Network Medicine in Pharmacology and Disease

In 2011, >2.5 million people died from only 15 causes in the United States. Ten of these involved complex or infectious diseases for which there is insufficient knowledge or treatment, such as heart disease, influenza, and Alzheimer's disease. 1 Complex diseases have been difficult to understand due to their multifarious genetic and molecular fingerprints, while certain infectious agents have evolved to elude treatment and prophylaxis. Network medicine provides a macroscopic approach to understanding and treating such illnesses. It integrates experimental data on gene, protein, and metabolic interactions with clinical knowledge of disease and pharmacology in order to extend the understanding of diseases and their treatments. The resulting “big picture” allows for the development of computational and mathematical methods to identify novel disease pathways and predict patient drug response, among others. In this review, we discuss recent advances in network medicine.

[1]  I. Nookaew,et al.  Integration of clinical data with a genome-scale metabolic model of the human adipocyte , 2013, Molecular systems biology.

[2]  Aiden Corvin,et al.  Network-assisted investigation of combined causal signals from genome-wide association studies in schizophrenia. , 2012 .

[3]  L. Jansson,et al.  Competing definitions of schizophrenia: what can be learned from polydiagnostic studies? , 2007, Schizophrenia bulletin.

[4]  P. Neuvonen,et al.  Drug interactions with lipid‐lowering drugs: Mechanisms and clinical relevance , 2006, Clinical pharmacology and therapeutics.

[5]  Yudong D. He,et al.  Gene expression profiling predicts clinical outcome of breast cancer , 2002, Nature.

[6]  A. Barabasi,et al.  Lethality and centrality in protein networks , 2001, Nature.

[7]  J. Dopazo,et al.  Assessing the Biological Significance of Gene Expression Signatures and Co-Expression Modules by Studying Their Network Properties , 2011, PloS one.

[8]  K. Goh,et al.  Exploring the human diseasome: the human disease network. , 2012, Briefings in functional genomics.

[9]  Susumu Goto,et al.  The KEGG resource for deciphering the genome , 2004, Nucleic Acids Res..

[10]  Katherine M Flegal,et al.  Prevalence of obesity in the United States, 2009-2010. , 2012, NCHS data brief.

[11]  M. DePamphilis,et al.  HUMAN DISEASE , 1957, The Ulster Medical Journal.

[12]  B. Al-Lazikani,et al.  Personalized Cancer Medicine: Molecular Diagnostics, Predictive biomarkers, and Drug Resistance , 2012, Clinical pharmacology and therapeutics.

[13]  A. Barabasi,et al.  The human disease network , 2007, Proceedings of the National Academy of Sciences.

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

[15]  Vincent Lotteau,et al.  When the human viral infectome and diseasome networks collide: towards a systems biology platform for the aetiology of human diseases , 2011, BMC Systems Biology.

[16]  Meeta Pradhan,et al.  Cliques for the identification of gene signatures for colorectal cancer across population , 2012, BMC Systems Biology.

[17]  Peilin Jia,et al.  Network-Assisted Investigation of Combined Causal Signals from Genome-Wide Association Studies in Schizophrenia , 2012, PLoS Comput. Biol..

[18]  Ben Y. Reis,et al.  Predicting Adverse Drug Events Using Pharmacological Network Models , 2011, Science Translational Medicine.

[19]  B. Spellberg,et al.  On the exoneration of Dr. William H. Stewart: debunking an urban legend , 2013, Infectious Diseases of Poverty.

[20]  Hugo Y. K. Lam,et al.  Personal Omics Profiling Reveals Dynamic Molecular and Medical Phenotypes , 2012, Cell.

[21]  K. N. Chandrika,et al.  Analysis of the human protein interactome and comparison with yeast, worm and fly interaction datasets , 2006, Nature Genetics.

[22]  A. Barabasi,et al.  Network biology: understanding the cell's functional organization , 2004, Nature Reviews Genetics.

[23]  M Hucka,et al.  Evolving a lingua franca and associated software infrastructure for computational systems biology: the Systems Biology Markup Language (SBML) project. , 2004, Systems biology.

[24]  S. Lampel,et al.  The druggable genome: an update. , 2005, Drug discovery today.

[25]  Shan Zhao,et al.  Mining protein networks for synthetic genetic interactions , 2008, BMC Bioinformatics.

[26]  C. Sander,et al.  Mutual exclusivity analysis identifies oncogenic network modules. , 2012, Genome research.

[27]  Ross A Hammond,et al.  The economic impact of obesity in the United States , 2010, Diabetes, metabolic syndrome and obesity : targets and therapy.

[28]  Russ B. Altman,et al.  A novel signal detection algorithm for identifying hidden drug-drug interactions in adverse event reports , 2012, J. Am. Medical Informatics Assoc..

[29]  F. Azuaje,et al.  Analysis of a gene co-expression network establishes robust association between Col5a2 and ischemic heart disease , 2013, BMC Medical Genomics.

[30]  C. Seltzer,et al.  A REVIEW OF GENETIC AND CONSTITUTIONAL FACTORS IN HUMAN OBESITY * , 1966, Annals of the New York Academy of Sciences.

[31]  Martin Kuiper,et al.  BiNGO: a Cytoscape plugin to assess overrepresentation of Gene Ontology categories in Biological Networks , 2005, Bioinform..

[32]  Y. Lazebnik.  Can a biologist fix a radio? — or, what I learned while studying apoptosis , 2004, Biochemistry (Moscow).

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

[34]  Deok-Sun Lee,et al.  Viral Perturbations of Host Networks Reflect Disease Etiology , 2012, PLoS Comput. Biol..

[35]  Hailin Chen,et al.  A Semi-Supervised Method for Drug-Target Interaction Prediction with Consistency in Networks , 2013, PloS one.

[36]  Alexander E. Ivliev,et al.  Drug Target Prediction and Repositioning Using an Integrated Network-Based Approach , 2013, PloS one.

[37]  M. Gerstein,et al.  Annotation transfer between genomes: protein-protein interologs and protein-DNA regulogs. , 2004, Genome research.

[38]  S. Coulomb,et al.  Gene essentiality and the topology of protein interaction networks , 2005, Proceedings of the Royal Society B: Biological Sciences.

[39]  Hongzhe Li,et al.  A Markov random field model for network-based analysis of genomic data , 2007, Bioinform..

[40]  E. Feuer,et al.  SEER Cancer Statistics Review, 1975-2003 , 2006 .

[41]  Patrick Aloy,et al.  Structural systems pharmacology: the role of 3D structures in next-generation drug development. , 2013, Chemistry & biology.

[42]  Rahul C. Deo,et al.  Interpreting cancer genomes using systematic host perturbations by tumour virus proteins , 2012, Nature.

[43]  J. Chen,et al.  Predicting adverse drug reaction profiles by integrating protein interaction networks with drug structures , 2013, Proteomics.

[44]  M. Jarvelin,et al.  Highly interconnected genes in disease-specific networks are enriched for disease-associated polymorphisms , 2012, Genome Biology.

[45]  Jiaquan Xu,et al.  Deaths: preliminary data for 2011. , 2012 .

[46]  B. Snel,et al.  Predicting disease genes using protein–protein interactions , 2006, Journal of Medical Genetics.

[47]  Xiujie Chen,et al.  Network Characteristic Analysis of ADR-related Proteins and Identification of ADR-ADR Associations , 2013, Scientific Reports.

[48]  Hiroaki Kitano,et al.  The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models , 2003, Bioinform..

[49]  The UniProt Consortium,et al.  Update on activities at the Universal Protein Resource (UniProt) in 2013 , 2012, Nucleic Acids Res..

[50]  Sarah L. Kinnings,et al.  Novel computational approaches to polypharmacology as a means to define responses to individual drugs. , 2012, Annual review of pharmacology and toxicology.

[51]  G. Carman,et al.  Use of synthetic lethal mutants to clone and characterize a novel CTP synthetase gene in Saccharomyces cerevisiae , 1994, Molecular and General Genetics MGG.

[52]  Ruili Huang,et al.  The NCGC Pharmaceutical Collection: A Comprehensive Resource of Clinically Approved Drugs Enabling Repurposing and Chemical Genomics , 2011, Science Translational Medicine.

[53]  R. Albert.  Scale-free networks in cell biology , 2005, Journal of Cell Science.

[54]  L. DeLisi,et al.  Unique topology of language processing brain network: A systems-level biomarker of schizophrenia , 2012, Schizophrenia Research.

[55]  Lawrence J Appel,et al.  Prevalence of obesity in the United States. , 2014, JAMA.

[56]  Jun Wang,et al.  Prioritization of candidate disease genes by topological similarity between disease and protein diffusion profiles , 2013, BMC Bioinformatics.

[57]  Qianlan Yao,et al.  Subpathway-GM: identification of metabolic subpathways via joint power of interesting genes and metabolites and their topologies within pathways , 2013, Nucleic acids research.

[58]  Mark D. Huffman,et al.  Heart disease and stroke statistics--2013 update: a report from the American Heart Association. , 2013, Circulation.

[59]  Vicki L Burt,et al.  Prescription drug use continues to increase: U.S. prescription drug data for 2007-2008. , 2010, NCHS data brief.

[60]  Michael Schroeder,et al.  Google Goes Cancer: Improving Outcome Prediction for Cancer Patients by Network-Based Ranking of Marker Genes , 2012, PLoS Comput. Biol..

[61]  P. Khatri,et al.  Global functional profiling of gene expression ? ? This work was funded in part by a Sun Microsystem , 2003 .

[62]  María Martín,et al.  Activities at the Universal Protein Resource (UniProt) , 2013, Nucleic Acids Res..

[63]  A. Barabasi,et al.  Network medicine : a network-based approach to human disease , 2010 .

[64]  Ravi Iyengar,et al.  Systems pharmacology. , 2010, The Mount Sinai journal of medicine, New York.

[65]  A. Hopkins.  Network pharmacology: the next paradigm in drug discovery. , 2008, Nature chemical biology.

[66]  David Venet,et al.  Most Random Gene Expression Signatures Are Significantly Associated with Breast Cancer Outcome , 2011, PLoS Comput. Biol..

[67]  C. Shields,et al.  Schizophrenia: a review. , 2007, American family physician.

[68]  Natasa Przulj,et al.  Biological function through network topology: a survey of the human diseasome , 2012, Briefings in functional genomics.

[69]  Steven J. M. Jones,et al.  Comprehensive molecular portraits of human breast tumors , 2012, Nature.

[70]  P. Khatri,et al.  Global functional profiling of gene expression. , 2003, Genomics.

[71]  Teri A Manolio,et al.  Genomewide association studies and assessment of the risk of disease. , 2010, The New England journal of medicine.

[72]  Robert Clarke,et al.  Identifying protein interaction subnetworks by a bagging Markov random field-based method , 2012, Nucleic acids research.

[73]  Michael Schroeder,et al.  Network information improves cancer outcome prediction , 2014, Briefings Bioinform..

[74]  Ben Y. Reis,et al.  Pharmacointeraction Network Models Predict Unknown Drug-Drug Interactions , 2013, PloS one.

[75]  P. Bork,et al.  A method and server for predicting damaging missense mutations , 2010, Nature Methods.

[76]  D. Hunter.  Gene–environment interactions in human diseases , 2005, Nature Reviews Genetics.

[77]  Ge Zhang,et al.  From human genetics and genomics to pharmacogenetics and pharmacogenomics: past lessons, future directions. , 2008, Drug metabolism reviews.