Biological network inference for drug discovery.

A better understanding of the pathophysiology should help deliver drugs whose targets are involved in the causative processes underlying a disease. Biological network inference uses computational methods for deducing from high-throughput experimental data, the topology and the causal structure of the interactions among the drugs and their targets. Therefore, biological network inference can support and contribute to the experimental identification of both gene and protein networks causing a disease as well as the biochemical networks of drugs metabolism and mechanisms of action. The resulting high-level networks serve as a foundational basis for more detailed mechanistic models and are increasingly used in drug discovery by pharmaceutical and biotechnology companies. We review and compare recent computational technologies for network inference applied to drug discovery.

[1]  A. Chiang,et al.  Systematic Evaluation of Drug–Disease Relationships to Identify Leads for Novel Drug Uses , 2009, Clinical pharmacology and therapeutics.

[2]  Mudita Singhal,et al.  SEBINI-CABIN: An Analysis Pipeline for Biological Network Inference, with a Case Study in Protein-Protein Interaction Network Reconstruction , 2007, ICMLA 2007.

[3]  R. Tagliaferri,et al.  Discovery of drug mode of action and drug repositioning from transcriptional responses , 2010, Proceedings of the National Academy of Sciences.

[4]  A. Barabasi,et al.  Drug—target network , 2007, Nature Biotechnology.

[5]  R. Sharan,et al.  PREDICT: a method for inferring novel drug indications with application to personalized medicine , 2011, Molecular systems biology.

[6]  Anuj Shah,et al.  SEBINI: Software Environment for BIological Network Inference , 2006, Bioinform..

[7]  Michael Schroeder,et al.  Old friends in new guise: repositioning of known drugs with structural bioinformatics , 2011, Briefings Bioinform..

[8]  Andreas Zell,et al.  SBMLsqueezer: A CellDesigner plug-in to generate kinetic rate equations for biochemical networks , 2008, BMC Systems Biology.

[9]  Frank Emmert-Streib,et al.  Organizational structure and the periphery of the gene regulatory network in B-cell lymphoma , 2012, BMC Systems Biology.

[10]  Alexander A. Morgan,et al.  Discovery and Preclinical Validation of Drug Indications Using Compendia of Public Gene Expression Data , 2011, Science Translational Medicine.

[11]  R. Russell,et al.  Illuminating drug discovery with biological pathways , 2005, FEBS letters.

[12]  Isobel Claire Gormley,et al.  Bi-directional gene set enrichment and canonical correlation analysis identify key diet-sensitive pathways and biomarkers of metabolic syndrome , 2010, BMC Bioinformatics.

[13]  Alexander A. Morgan,et al.  Supplementary Materials for Computational Repositioning of the Anticonvulsant Topiramate for Inflammatory Bowel Disease , 2011 .

[14]  Yoshihiro Yamanishi,et al.  Supervised enzyme network inference from the integration of genomic data and chemical information , 2005, ISMB.

[15]  Pablo Tamayo,et al.  Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[16]  A. Isaksson,et al.  Identification of molecular mechanisms for cellular drug resistance by combining drug activity and gene expression profiles , 2005, British Journal of Cancer.

[17]  Lani F. Wu,et al.  Multidimensional Drug Profiling By Automated Microscopy , 2004, Science.

[18]  Michael D. Schmidt,et al.  Automated refinement and inference of analytical models for metabolic networks , 2011, Physical biology.

[19]  Cédric Notredame,et al.  Upcoming challenges for multiple sequence alignment methods in the high-throughput era , 2009, Bioinform..

[20]  Anna R. Panchenko,et al.  Erratum to: State of the art: refinement of multiple sequence alignments , 2006, BMC Bioinformatics.

[21]  Frank Emmert-Streib,et al.  Inferring the conservative causal core of gene regulatory networks , 2010, BMC Systems Biology.

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

[23]  Lani F. Wu,et al.  Image-based multivariate profiling of drug responses from single cells , 2007, Nature Methods.

[24]  Age K. Smilde,et al.  Metabolic network discovery through reverse engineering of metabolome data , 2009, Metabolomics.

[25]  Toan Thang Phan,et al.  Insights gained from the reverse engineering of gene networks in keloid fibroblasts , 2011, Theoretical Biology and Medical Modelling.

[26]  Riet De Smet,et al.  Advantages and limitations of current network inference methods , 2010, Nature Reviews Microbiology.

[27]  D. di Bernardo,et al.  How to infer gene networks from expression profiles , 2007, Molecular systems biology.

[28]  P. Bork,et al.  Drug discovery in the age of systems biology: the rise of computational approaches for data integration. , 2012, Current opinion in biotechnology.

[29]  John A. Tallarico,et al.  Integrating high-content screening and ligand-target prediction to identify mechanism of action. , 2008, Nature chemical biology.

[30]  Yoshihiro Yamanishi,et al.  Prediction of drug–target interaction networks from the integration of chemical and genomic spaces , 2008, ISMB.

[31]  Ben van Ommen,et al.  Transcriptomic Coordination in the Human Metabolic Network Reveals Links between n-3 Fat Intake, Adipose Tissue Gene Expression and Metabolic Health , 2011, PLoS Comput. Biol..

[32]  Francesco Falciani,et al.  Mapping Drug Physico-Chemical Features to Pathway Activity Reveals Molecular Networks Linked to Toxicity Outcome , 2010, PloS one.

[33]  Paul A Clemons,et al.  The Connectivity Map: Using Gene-Expression Signatures to Connect Small Molecules, Genes, and Disease , 2006, Science.

[34]  Diego di Bernardo,et al.  Computational Biology and Drug Discovery: From Single-Target to Network Drugs , 2006 .

[35]  H. Yabuuchi,et al.  Analysis of multiple compound–protein interactions reveals novel bioactive molecules , 2011, Molecular systems biology.

[36]  Ziv Bar-Joseph,et al.  DREM 2.0: Improved reconstruction of dynamic regulatory networks from time-series expression data , 2012, BMC Systems Biology.

[37]  William Stafford Noble,et al.  A new pairwise kernel for biological network inference with support vector machines , 2007, BMC Bioinformatics.

[38]  Age K Smilde,et al.  Reverse engineering of metabolic networks, a critical assessment. , 2011, Molecular bioSystems.

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

[40]  Xing-Ming Zhao,et al.  Prediction of Drug Combinations by Integrating Molecular and Pharmacological Data , 2011, PLoS Comput. Biol..

[41]  Richard Bonneau,et al.  The Inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo , 2006, Genome Biology.

[42]  Alexander A. Morgan,et al.  Computational Repositioning of the Anticonvulsant Topiramate for Inflammatory Bowel Disease , 2011, Science Translational Medicine.

[43]  Joel Dudley,et al.  Network-Based Elucidation of Human Disease Similarities Reveals Common Functional Modules Enriched for Pluripotent Drug Targets , 2010, PLoS Comput. Biol..

[44]  Gert R. G. Lanckriet,et al.  Classification of a large microarray data set: algorithm comparison and analysis of drug signatures. , 2005, Genome research.

[45]  C J Oates,et al.  Network Inference and Biological Dynamics. , 2011, The annals of applied statistics.

[46]  Frank Emmert-Streib,et al.  Bagging Statistical Network Inference from Large-Scale Gene Expression Data , 2012, PloS one.

[47]  Wei E Huang,et al.  When single cell technology meets omics, the new toolbox of analytical biotechnology is emerging. , 2012, Current opinion in biotechnology.

[48]  J. Rothberg,et al.  A quantitative genomic expression analysis platform for multiplexed in vitro prediction of drug action , 2005, The Pharmacogenomics Journal.

[49]  Mudita Singhal,et al.  Biological Network Inference and analysis using SEBINI and CABIN. , 2009, Methods in molecular biology.

[50]  Ilya Nemenman,et al.  Mass Conservation and Inference of Metabolic Networks from High-Throughput Mass Spectrometry Data , 2011, J. Comput. Biol..

[51]  Xiaohui Liu,et al.  Mining pathway signatures from microarray data and relevant biological knowledge , 2007, J. Biomed. Informatics.

[52]  Doheon Lee,et al.  Inferring Pathway Activity toward Precise Disease Classification , 2008, PLoS Comput. Biol..

[53]  Robert V. Demicco,et al.  Elevated Eocene Atmospheric CO2 and Its Subsequent Decline , 2006, Science.

[54]  S. Sleigh,et al.  Repurposing Strategies for Therapeutics , 2010, Pharmaceutical Medicine.

[55]  Joel Dudley,et al.  Exploiting drug-disease relationships for computational drug repositioning , 2011, Briefings Bioinform..

[56]  Guanghui Hu,et al.  Human Disease-Drug Network Based on Genomic Expression Profiles , 2009, PloS one.

[57]  Chuang Liu,et al.  Prediction of Drug-Target Interactions and Drug Repositioning via Network-Based Inference , 2012, PLoS Comput. Biol..

[58]  Chris Wiggins,et al.  ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context , 2004, BMC Bioinformatics.