Analyzing of Molecular Networks for Human Diseases and Drug Discovery

Molecular networks represent the interactions and relations of genes/proteins, and also encode molecular mechanisms of biological processes, development and diseases. Among the molecular networks, protein-protein Interaction Networks (PINs) have become effective platforms for uncovering the molecular mechanisms of diseases and drug discovery. PINs have been constructed for various organisms and utilized to solve many biological problems. In human, most proteins present their complex functions by interactions with other proteins, and the sum of these interactions represents the human protein interactome. Especially in the research on human disease and drugs, as an emerging tool, the PIN provides a platform to systematically explore the molecular complexities of specific diseases and the references for drug design. In this review, we summarized the commonly used approaches to aid disease research and drug discovery with PINs, including the network topological analysis, identification of novel pathways, drug targets and sub-network biomarkers for diseases. With the development of bioinformatic techniques and biological networks, PINs will play an increasingly important role in human disease research and drug discovery.

[1]  Pablo Villoslada,et al.  A computational analysis of protein-protein interaction networks in neurodegenerative diseases , 2008, BMC Systems Biology.

[2]  Li Jin,et al.  A systematic characterization of genes underlying both complex and Mendelian diseases. , 2012, Human molecular genetics.

[3]  E. Wang,et al.  Dynamic modeling and analysis of cancer cellular network motifs. , 2011, Integrative biology : quantitative biosciences from nano to macro.

[4]  Diego Alonso-López,et al.  Human Interactomics: Comparative Analysis of Different Protein Interaction Resources and Construction of a Cancer Protein-Drug Bipartite Network. , 2018, Advances in protein chemistry and structural biology.

[5]  M. Carson,et al.  Network-based prediction and knowledge mining of disease genes , 2015, BMC Medical Genomics.

[6]  Edwin Wang,et al.  Network Analysis Reveals A Signaling Regulatory Loop in the PIK3CA-mutated Breast Cancer Predicting Survival Outcome , 2017, Genom. Proteom. Bioinform..

[7]  Kathryn E. Hentges,et al.  Defining the Role of Essential Genes in Human Disease , 2011, PloS one.

[8]  Edwin Wang,et al.  Cancer systems biology in the genome sequencing era: part 2, evolutionary dynamics of tumor clonal networks and drug resistance. , 2013, Seminars in cancer biology.

[9]  Edwin Wang,et al.  Dynamic rewiring of the androgen receptor protein interaction network correlates with prostate cancer clinical outcomes. , 2011, Integrative biology : quantitative biosciences from nano to macro.

[10]  Carl Kingsford,et al.  The power of protein interaction networks for associating genes with diseases , 2010, Bioinform..

[11]  André Schrattenholz,et al.  What does systems biology mean for drug development? , 2008, Current medicinal chemistry.

[12]  Edwin Wang,et al.  Understanding genomic alterations in cancer genomes using an integrative network approach. , 2013, Cancer letters.

[13]  Saman K. Halgamuge,et al.  The node-weighted Steiner tree approach to identify elements of cancer-related signaling pathways , 2017, BMC Bioinformatics.

[14]  M. Oti,et al.  The modular nature of genetic diseases , 2006, Clinical genetics.

[15]  Andrew Chatr-aryamontri,et al.  Structural and functional protein network analyses predict novel signaling functions for rhodopsin , 2011, Molecular systems biology.

[16]  Nahid Safari-Alighiarloo,et al.  Protein-protein interaction networks (PPI) and complex diseases , 2014, Gastroenterology and hepatology from bed to bench.

[17]  Susumu Goto,et al.  The commonality of protein interaction networks determined in neurodegenerative disorders (NDDs) , 2007, Bioinform..

[18]  Zhongming Zhao,et al.  A comparative study of cancer proteins in the human protein-protein interaction network , 2010, BMC Genomics.

[19]  Xing Chen,et al.  Long non-coding RNAs and complex diseases: from experimental results to computational models , 2016, Briefings Bioinform..

[20]  K. Gunsalus,et al.  Network modeling links breast cancer susceptibility and centrosome dysfunction. , 2007, Nature genetics.

[21]  K. Sachs,et al.  Causal Protein-Signaling Networks Derived from Multiparameter Single-Cell Data , 2005, Science.

[22]  Xing Chen,et al.  MKRMDA: multiple kernel learning-based Kronecker regularized least squares for MiRNA–disease association prediction , 2017, Journal of Translational Medicine.

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

[24]  Hongdong Li,et al.  Multi-level reproducibility of signature hubs in human interactome for breast cancer metastasis , 2010, BMC Systems Biology.

[25]  Monirah A. Al-Ajlan,et al.  Integrated Genomic and Network-Based Analyses of Complex Diseases and Human Disease Network. , 2016, Journal of genetics and genomics = Yi chuan xue bao.

[26]  Zhong-Jun Wu,et al.  Constructing the HBV-human protein interaction network to understand the relationship between HBV and hepatocellular carcinoma , 2010, Journal of experimental & clinical cancer research : CR.

[27]  T. Ideker,et al.  Network-based classification of breast cancer metastasis , 2007, Molecular systems biology.

[28]  J. Satoh,et al.  Molecular network of the comprehensive multiple sclerosis brain-lesion proteome , 2009, Multiple sclerosis.

[29]  Xing Chen,et al.  PBMDA: A novel and effective path-based computational model for miRNA-disease association prediction , 2017, PLoS Comput. Biol..

[30]  Lars Juhl Jensen,et al.  Identification of Novel Type 1 Diabetes Candidate Genes by Integrating Genome-Wide Association Data, Protein-Protein Interactions, and Human Pancreatic Islet Gene Expression , 2012, Diabetes.

[31]  Simo V. Zhang,et al.  A map of human cancer signaling , 2007, Molecular systems biology.

[32]  Edwin Wang,et al.  Signaling network analysis of ubiquitin-mediated proteins suggests correlations between the 26S proteasome and tumor progression. , 2009, Molecular bioSystems.

[33]  David Warde-Farley,et al.  Dynamic modularity in protein interaction networks predicts breast cancer outcome , 2009, Nature Biotechnology.

[34]  I. Jurisica,et al.  Network-based characterization of drug-regulated genes, drug targets, and toxicity. , 2012, Methods.

[35]  Siqi Liu,et al.  Disease Biomarkers for Precision Medicine: Challenges and Future Opportunities , 2017, Genom. Proteom. Bioinform..

[36]  H. Lehrach,et al.  A Human Protein-Protein Interaction Network: A Resource for Annotating the Proteome , 2005, Cell.

[37]  Jinhui Tang,et al.  Multi-Grained Random Fields for Mitosis Identification in Time-Lapse Phase Contrast Microscopy Image Sequences , 2017, IEEE Transactions on Medical Imaging.

[38]  N. Katsanis,et al.  Functional modules, mutational load and human genetic disease. , 2010, Trends in genetics : TIG.

[39]  Ting Chen,et al.  Further understanding human disease genes by comparing with housekeeping genes and other genes , 2006, BMC Genomics.

[40]  Edward R. Dougherty,et al.  Identification of diagnostic subnetwork markers for cancer in human protein-protein interaction network , 2010, BMC Bioinformatics.

[41]  Péter Csermely,et al.  The efficiency of multi-target drugs: the network approach might help drug design. , 2004, Trends in pharmacological sciences.

[42]  Lina Chen,et al.  Predicting Candidate Genes Based on Combined Network Topological Features: A Case Study in Coronary Artery Disease , 2012, PloS one.

[43]  A. Barabasi,et al.  Interactome Networks and Human Disease , 2011, Cell.

[44]  H. Kitano A robustness-based approach to systems-oriented drug design , 2007, Nature Reviews Drug Discovery.

[45]  P. Matthews,et al.  Pathway and network-based analysis of genome-wide association studies in multiple sclerosis , 2009, Human molecular genetics.

[46]  Edwin Wang,et al.  Cancer modeling and network biology: accelerating toward personalized medicine. , 2015, Seminars in cancer biology.

[47]  Edward C Stites,et al.  Network Analysis of Oncogenic Ras Activation in Cancer , 2007, Science.

[48]  A. Fliri,et al.  Cause-effect relationships in medicine: a protein network perspective. , 2010, Trends in pharmacological sciences.

[49]  Chih-yuan Chiang,et al.  A Human MAP Kinase Interactome , 2010, Nature Methods.

[50]  Carlos Prieto,et al.  Protein interactions: mapping interactome networks to support drug target discovery and selection. , 2012, Methods in molecular biology.

[51]  L. Furlong Human diseases through the lens of network biology. , 2013, Trends in genetics : TIG.

[52]  Patrick Aloy,et al.  From protein interaction networks to novel therapeutic strategies , 2012, IUBMB life.

[53]  M. Moran,et al.  The human phosphotyrosine signaling network: Evolution and hotspots of hijacking in cancer , 2012, Genome research.

[54]  Jing Yang,et al.  The human disease network in terms of dysfunctional regulatory mechanisms , 2015, Biology Direct.

[55]  P. Aloy,et al.  A network medicine approach to human disease , 2009, FEBS letters.

[56]  Xing Chen,et al.  EGBMMDA: Extreme Gradient Boosting Machine for MiRNA-Disease Association prediction , 2018, Cell Death & Disease.

[57]  Jinan Wang,et al.  Systems approaches and polypharmacology for drug discovery from herbal medicines: an example using licorice. , 2013, Journal of ethnopharmacology.

[58]  Lei Zhang,et al.  Detecting pathway relationship in the context of human protein-protein interaction network and its application to Parkinson's disease. , 2017, Methods.

[59]  Edwin Wang,et al.  Cancer systems biology in the genome sequencing era: part 1, dissecting and modeling of tumor clones and their networks. , 2013, Seminars in cancer biology.

[60]  P. Aloy,et al.  Interactome mapping suggests new mechanistic details underlying Alzheimer's disease. , 2011, Genome research.

[61]  J. Whisstock,et al.  Prediction of protein function from protein sequence and structure , 2003, Quarterly Reviews of Biophysics.

[62]  Petter Holme,et al.  Network Properties of Complex Human Disease Genes Identified through Genome-Wide Association Studies , 2009, PloS one.

[63]  Edwin Wang,et al.  Signaling network assessment of mutations and copy number variations predict breast cancer subtype-specific drug targets. , 2013, Cell reports.

[64]  Xing Chen,et al.  NLLSS: Predicting Synergistic Drug Combinations Based on Semi-supervised Learning , 2016, PLoS Comput. Biol..

[65]  E. Wang,et al.  Predictive genomics: a cancer hallmark network framework for predicting tumor clinical phenotypes using genome sequencing data. , 2014, Seminars in cancer biology.

[66]  P. Aloy,et al.  Unveiling the role of network and systems biology in drug discovery. , 2010, Trends in pharmacological sciences.

[67]  Qian Wang,et al.  Reconstruction and Application of Protein–Protein Interaction Network , 2016, International journal of molecular sciences.

[68]  R. Sharan,et al.  Protein networks in disease. , 2008, Genome research.

[69]  Sandra D'Alfonso,et al.  Network-based multiple sclerosis pathway analysis with GWAS data from 15,000 cases and 30,000 controls. , 2013, American journal of human genetics.

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

[71]  Paul A. Bates,et al.  Global topological features of cancer proteins in the human interactome , 2006, Bioinform..

[72]  Shiwen Zhao,et al.  Network-Based Relating Pharmacological and Genomic Spaces for Drug Target Identification , 2010, PloS one.

[73]  Xing Chen,et al.  MicroRNAs and complex diseases: from experimental results to computational models , 2019, Briefings Bioinform..

[74]  S. Gabriel,et al.  Advances in understanding cancer genomes through second-generation sequencing , 2010, Nature Reviews Genetics.

[75]  Roded Sharan,et al.  Optimally Orienting Physical Networks , 2011, RECOMB.

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

[77]  Mohan S. Kankanhalli,et al.  Hierarchical Clustering Multi-Task Learning for Joint Human Action Grouping and Recognition , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[78]  Lan V. Zhang,et al.  Evidence for dynamically organized modularity in the yeast protein–protein interaction network , 2004, Nature.

[79]  André Schrattenholz,et al.  Systems biology approaches and tools for analysis of interactomes and multi-target drugs. , 2010, Methods in molecular biology.

[80]  Yongdong Zhang,et al.  Drug-target interaction prediction: databases, web servers and computational models , 2016, Briefings Bioinform..