Novel approaches to develop community-built biological network models for potential drug discovery

ABSTRACT Introduction: Hundreds of thousands of data points are now routinely generated in clinical trials by molecular profiling and NGS technologies. A true translation of this data into knowledge is not possible without analysis and interpretation in a well-defined biology context. Currently, there are many public and commercial pathway tools and network models that can facilitate such analysis. At the same time, insights and knowledge that can be gained is highly dependent on the underlying biological content of these resources. Crowdsourcing can be employed to guarantee the accuracy and transparency of the biological content underlining the tools used to interpret rich molecular data. Areas covered: In this review, the authors describe crowdsourcing in drug discovery. The focal point is the efforts that have successfully used the crowdsourcing approach to verify and augment pathway tools and biological network models. Technologies that enable the building of biological networks with the community are also described. Expert opinion: A crowd of experts can be leveraged for the entire development process of biological network models, from ontologies to the evaluation of their mechanistic completeness. The ultimate goal is to facilitate biomarker discovery and personalized medicine by mechanistically explaining patients’ differences with respect to disease prevention, diagnosis, and therapy outcome.

[1]  Theodore Sakellaropoulos,et al.  A crowd-sourcing approach for the construction of species-specific cell signaling networks , 2014, Bioinform..

[2]  Danail Bonchev,et al.  Evolution of metabolic network organization , 2010, BMC Systems Biology.

[3]  Kathleen M Jagodnik,et al.  Extraction and analysis of signatures from the Gene Expression Omnibus by the crowd , 2016, Nature Communications.

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

[5]  Hiroaki Kitano,et al.  Lessons from Toxicology: Developing a 21st-Century Paradigm for Medical Research , 2015, Environmental health perspectives.

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

[7]  Manuel C. Peitsch,et al.  Construction of a computable cell proliferation network focused on non-diseased lung cells , 2011, BMC Systems Biology.

[8]  Xinghua Lu,et al.  Trans-species learning of cellular signaling systems with bimodal deep belief networks , 2015, Bioinform..

[9]  Henning Hermjakob,et al.  The Reactome pathway knowledgebase , 2013, Nucleic Acids Res..

[10]  Yi Liu,et al.  Interactive crowdsourcing to spontaneous reporting of Adverse Drug Reactions , 2014, 2014 IEEE International Conference on Communications (ICC).

[11]  Samik Ghosh,et al.  Modeling and simulation using CellDesigner. , 2014, Methods in molecular biology.

[12]  Martin Schneider,et al.  Grants4Targets - an innovative approach to translate ideas from basic research into novel drugs. , 2011, Drug discovery today.

[14]  Martin Hofmann-Apitius,et al.  Computable cause-and-effect models of healthy and Alzheimer's disease states and their mechanistic differential analysis , 2015, Alzheimer's & Dementia.

[15]  Manuel C. Peitsch,et al.  Enhancement of COPD biological networks using a web-based collaboration interface , 2015, F1000Research.

[16]  Humberto González-Díaz,et al.  Brain-inspired cheminformatics of drug-target brain interactome, synthesis, and assay of TVP1022 derivatives , 2016, Neuropharmacology.

[17]  Monika Lessl,et al.  Grants4Targets: an open innovation initiative to foster drug discovery collaborations , 2014, Nature Reviews Drug Discovery.

[18]  Dario Floreano,et al.  Combining Multiple Results of a Reverse‐Engineering Algorithm: Application to the DREAM Five‐Gene Network Challenge , 2009, Annals of the New York Academy of Sciences.

[19]  Yang Xiang,et al.  Quantification of biological network perturbations for mechanistic insight and diagnostics using two-layer causal models , 2014, BMC Bioinformatics.

[20]  Manuel C. Peitsch,et al.  Assessment of network perturbation amplitudes by applying high-throughput data to causal biological networks , 2012, BMC Systems Biology.

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

[22]  Martin Hofmann-Apitius,et al.  ADO: A disease ontology representing the domain knowledge specific to Alzheimer's disease , 2014, Alzheimer's & Dementia.

[23]  Martin Hofmann-Apitius,et al.  Exploring novel mechanistic insights in Alzheimer’s disease by assessing reliability of protein interactions , 2015, Scientific Reports.

[24]  Chris T Evelo,et al.  Biotransformation pathway maps in WikiPathways enable direct visualization of drug metabolism related expression changes. , 2010, Drug discovery today.

[25]  Martin Hofmann-Apitius,et al.  PDON: Parkinson’s disease ontology for representation and modeling of the Parkinson’s disease knowledge domain , 2015, Theoretical Biology and Medical Modelling.

[26]  Darrell R Abernethy,et al.  Systems pharmacology to predict drug toxicity: integration across levels of biological organization. , 2013, Annual review of pharmacology and toxicology.

[27]  Yang Xiang,et al.  Community-Reviewed Biological Network Models for Toxicology and Drug Discovery Applications , 2016, Gene regulation and systems biology.

[28]  Todd Lingren,et al.  Web 2.0-Based Crowdsourcing for High-Quality Gold Standard Development in Clinical Natural Language Processing , 2013, Journal of medical Internet research.

[29]  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.

[30]  M. Peitsch,et al.  Verification of systems biology research in the age of collaborative competition , 2011, Nature Biotechnology.

[31]  Sterling Thomas,et al.  A survey of current software for network analysis in molecular biology , 2010, Human Genomics.

[32]  Humberto González Díaz,et al.  New Markov-Autocorrelation Indices for Re-evaluation of Links in Chemical and Biological Complex Networks used in Metabolomics, Parasitology, Neurosciences, and Epidemiology , 2012, J. Chem. Inf. Model..

[33]  Cristian R. Munteanu,et al.  ANN Multiscale Model of Anti-HIV Drugs Activity vs AIDS Prevalence in the US at County Level Based on Information Indices of Molecular Graphs and Social Networks , 2014, J. Chem. Inf. Model..

[34]  Zhiyong Lu,et al.  Crowdsourcing in biomedicine: challenges and opportunities , 2016, Briefings Bioinform..

[35]  Marc A Marti-Renom,et al.  Should network biology be used for drug discovery? , 2016, Expert opinion on drug discovery.

[36]  Jing Chen,et al.  NDEx, the Network Data Exchange. , 2015, Cell systems.

[37]  Yang Xiang,et al.  sbv IMPROVER Diagnostic Signature Challenge , 2013 .

[38]  Michel Dumontier,et al.  Ranking Adverse Drug Reactions With Crowdsourcing , 2015, Journal of medical Internet research.

[39]  Manuel C. Peitsch,et al.  A Modular Cell-Type Focused Inflammatory Process Network Model for Non-Diseased Pulmonary Tissue , 2013, Bioinformatics and biology insights.

[40]  Samik Ghosh,et al.  Connecting the dots: role of standardization and technology sharing in biological simulation. , 2010, Drug discovery today.

[41]  K. Bretonnel Cohen,et al.  Crowdsourcing and curation: perspectives from biology and natural language processing , 2016, Database J. Biol. Databases Curation.

[42]  T. Sablinski.  Opening Up Clinical Study Design to the Long Tail , 2014, Science Translational Medicine.

[43]  Hiroaki Kitano,et al.  CellDesigner: a process diagram editor for gene-regulatory and biochemical networks , 2003 .

[44]  Robin Haw,et al.  Using the Reactome Database , 2012, Current protocols in bioinformatics.

[45]  Lincoln Stein,et al.  Reactome knowledgebase of human biological pathways and processes , 2008, Nucleic Acids Res..

[46]  C. Zipfel,et al.  Class uncorrected errors as misconduct , 2016, Nature.

[47]  Chris T. A. Evelo,et al.  Reactome from a WikiPathways Perspective , 2016, PLoS Comput. Biol..

[48]  M. Swan.  Crowdsourced Health Research Studies: An Important Emerging Complement to Clinical Trials in the Public Health Research Ecosystem , 2012, Journal of medical Internet research.

[49]  Cristian R. Munteanu,et al.  Modeling Complex Metabolic Reactions, Ecological Systems, and Financial and Legal Networks with MIANN Models Based on Markov-Wiener Node Descriptors , 2014, J. Chem. Inf. Model..

[50]  Mathieu Vinken,et al.  The adverse outcome pathway concept: a pragmatic tool in toxicology. , 2013, Toxicology.

[51]  Jennifer Park,et al.  Causal biological network database: a comprehensive platform of causal biological network models focused on the pulmonary and vascular systems , 2015, Database J. Biol. Databases Curation.

[52]  Erhan Bilal,et al.  Understanding the limits of animal models as predictors of human biology: lessons learned from the sbv IMPROVER Species Translation Challenge , 2014, Bioinform..

[53]  Ruili Huang,et al.  Novel Phenotypic Outcomes Identified for a Public Collection of Approved Drugs from a Publicly Accessible Panel of Assays , 2015, PloS one.

[54]  Alexander R. Pico,et al.  WikiPathways: Pathway Editing for the People , 2008, PLoS biology.

[55]  A. Bauer-Mehren,et al.  Pathway databases and tools for their exploitation: benefits, current limitations and challenges , 2009, Molecular systems biology.

[56]  Julia Hoeng,et al.  Quantitative assessment of biological impact using transcriptomic data and mechanistic network models. , 2013, Toxicology and applied pharmacology.

[57]  Natalie L. Catlett,et al.  Reverse causal reasoning: applying qualitative causal knowledge to the interpretation of high-throughput data , 2013, BMC Bioinformatics.

[58]  Lincoln Stein,et al.  Using the Reactome Database , 2004, Current protocols in bioinformatics.

[59]  Jose M Villaveces,et al.  Tools for visualization and analysis of molecular networks, pathways, and -omics data , 2015, Advances and applications in bioinformatics and chemistry : AABC.

[60]  Balakrishnan Chandrasekaran,et al.  What are ontologies, and why do we need them? , 1999, IEEE Intell. Syst..

[61]  Jennifer Park,et al.  A computable cellular stress network model for non-diseased pulmonary and cardiovascular tissue , 2011, BMC Systems Biology.

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

[63]  Samik Ghosh,et al.  Payao: a community platform for SBML pathway model curation , 2010, Bioinform..

[64]  A. Califano,et al.  Dialogue on Reverse‐Engineering Assessment and Methods , 2007, Annals of the New York Academy of Sciences.

[65]  Manuel C. Peitsch,et al.  Construction of a Computable Network Model of Tissue Repair and Angiogenesis in the Lung , 2013 .

[66]  Marcus R Munafò,et al.  Significance chasing in research practice: causes, consequences and possible solutions. , 2014, Addiction.

[67]  Barend Mons,et al.  Open PHACTS: semantic interoperability for drug discovery. , 2012, Drug discovery today.

[68]  C. Morton.  Innovating Openly: Researchers and Patients Turn to Crowdsourcing to Collaborate on Clinical Trials, Drug Discovery, and More , 2014, IEEE Pulse.

[69]  John Moult,et al.  A decade of CASP: progress, bottlenecks and prognosis in protein structure prediction. , 2005, Current opinion in structural biology.

[70]  Users' Handbook supplement to the Guidance Document for developing and assessing Adverse Outcome Pathways , 2019 .

[71]  Chris T. A. Evelo,et al.  WikiPathways: building research communities on biological pathways , 2011, Nucleic Acids Res..