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.

KEYWORDS:

• Mechanistic
• pathway
• network model
• crowd sourcing
• ontology
• Biological Expression Language

Article highlights

• Pathway tools and biological network models are necessary to unravel impacted biology from molecular data.

• Crowdsourcing provides unbiased assessment, transparency and innovation in drug discovery.

• Crowd sourcing can be used to handle rich systems biology data and to verify methods.

• Expert validated ontologies are essential to organize the unstructured information into computable biological network models.

• Crowd verification of computable pulmonary network models allowed to address conflicting information and to reduce researcher bias and complement classical peer-review.

• A Reputation-based collaborative network biology platform designed to build, edit and verify networks allows scientists (e.g. subject matter experts) to bring their perspectives to bear on large representations of mechanisms.

This box summarizes key points contained in the article.

Acknowledgments

The figures were prepared by S Boue of Philip Morris International R&D, Philip Morris Products S.A(PMI) and Samantha Elmhurst of Living-Art. The artwork was paid by PMI. English editing was performed by Edanz and paid by PMI.

Declaration of interest

M Talikka, MC Peitsch and J Hoeng are all employees of Philip Morris International R&D, Philip Morris Products S.A(PMI). L Alexopoulos is a co-founder of Protavio Ltd. N Bukharov is an employee of Clarivate Analytics while WS Hayes is VP of Data Sciences at Applied Dynamic Solutions, LLC. M Hofmann-Apitius is an employee of the Fraunhofer Institute for Algorithms and Scientific Computing. This manuscript was subjected to language review, funded by PMI.

Additional information

Funding

Language review and artwork was funded by Philip R&D, Philip Morris Products S.A. Morris International.