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Cloud-Based Life Sciences Manufacturing System: Integrated Experiment Management and Data Analysis via Amazon Web Services

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Abstract

A vital need in the life sciences industry is software that manages large amounts of fast-moving data for manufacturing quality assurance, clinical diagnostics, and research. In the life sciences industry and research labs, lab information management systems (LIMS) are often used to manage expensive lab instruments. We propose a new software architecture for cloud-based life sciences manufacturing system through the following two advances: (1) full life cycle support of life science experiment through cloud services, (2) workflow-based easy and automatic experiment management and data analysis. This paper discusses our software architecture and implementation on top of Amazon Web Services by utilizing its services including Lambda architecture, API gateway, serverless computing, and Internet of Things (IoT) services. We demonstrate its usage through a real-world life sciences instrument and experimental use case. To our best knowledge, it is the first work on supporting integrated experiment design, experiment instrument operation, experiment data storage, and experiment data analysis all in the cloud for the life sciences.

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References

  1. PR NEWSWIRE: Lims market size worth $2.22 billion by 2025—cagr: 9.1%: Grand view research, inc. http://news.sys-con.com/node/4372573. Accessed 21 Feb 2019

  2. A. Thomas, C. Voegele, D. de Silva, F. Le Calvez-Kelm, S. Cuber, S. Tavtigian, A Laboratory Information Management System (LIMS) for a high throughput genetic platform aimed at candidate gene mutation screening. Bioinformatics 23(18), 2504–2506 (2007). https://doi.org/10.1093/bioinformatics/btm365

    Article  Google Scholar 

  3. A.W. Ashton, D. Spruce, E.J. Gordon, G.A. Leonard, J. Gabadinho, K.E. Levik, L. Launer, M.A. Walsh, M. Nanao, O. Svensson, P. Brenchereau, R. Leal, S.D. Jones, S.M. McSweeney, S. Delagenire, S. Monaco, S. Veyrier, ISPyB: An information management system for synchrotron macromolecular crystallography. Bioinformatics 27(22), 3186–3192 (2011), https://doi.org/10.1093/bioinformatics/btr535, https://dx.doi.org/10.1093/bioinformatics/btr535

    Article  Google Scholar 

  4. A. Droit, J.M. Hunter, M. Rouleau, C. Ethier, A. Picard-Cloutier, D. Bourgais, G.G. Poirier, Parps database: A lims systems for protein-protein interaction data mining or laboratory information management system. BMC Bioinform. 8(1), 483 (2007), https://doi.org/10.1186/1471-2105-8-483

    Article  Google Scholar 

  5. D. Wu, M.J. Greer, D.W. Rosen, D. Schaefer, Cloud manufacturing: Strategic vision and state-of-the-art. J. Manuf. Syst. 32(4), 564–579 (2013). https://doi.org/10.1016/j.jmsy.2013.04.008, http://www.sciencedirect.com/science/article/pii/S0278612513000411

    Article  Google Scholar 

  6. F. Tao, Y. Cheng, L.D. Xu, L. Zhang, B.H. Li, Cciot-cmfg: Cloud computing and internet of things-based cloud manufacturing service system. IEEE Trans. Industr. Inf. 10(2), 1435–1442 (2014). https://doi.org/10.1109/TII.2014.2306383

    Article  Google Scholar 

  7. F. Tao, Y. Zuo, L.D. Xu, L. Zhang, Iot-based intelligent perception and access of manufacturing resource toward cloud manufacturing. IEEE Trans. Industr. Inf. 10(2), 1547–1557 (2014). https://doi.org/10.1109/TII.2014.2306397

    Article  Google Scholar 

  8. Amazon Simple Workflow Service (SWF). https://aws.amazon.com/swf/. Accessed 21 Feb 2019

  9. Microsoft flow: Automate processes + tasks. https://flow.microsoft.com/en-us/. Accessed 21 Feb 2019

  10. Amazon IoT. https://aws.amazon.com/iot/. Accessed 21 Feb 2019

  11. Azure IoT hub. https://azure.microsoft.com/en-us/services/iot-hub. Accessed 21 Feb 2019

  12. Amazon S3. https://aws.amazon.com/s3/. Accessed 21 Feb 2019

  13. IBM: IBM cloud object storage. https://www.ibm.com/cloud/object-storage. Accessed 21 Feb 2019

  14. Azure Storage. https://azure.microsoft.com/en-us/product-categories/storage/. Accessed 21 Feb 2019

  15. Amazon Relational Database Service (RDS). https://aws.amazon.com/rds/. Accessed 21 Feb 2019

  16. Azure Cosmos DB. https://azure.microsoft.com/en-us/free/cosmos-db/. Accessed 21 Feb 2019

  17. Amazon Elastic Compute Cloud (Amazon EC2). https://aws.amazon.com/ec2/. Accessed 21 Feb 2019

  18. Amazon Lambda. https://aws.amazon.com/lambda/. Accessed 21 Feb 2019

  19. R Core Team (2019) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austriahttp://www.R-project.org/

  20. R.T. Fielding, Architectural styles and the design of network-based software architectures. Ph.D. thesis, University of California, Irvine (2000)

    Google Scholar 

  21. Amazon API Gateway. https://aws.amazon.com/api-gateway/. Accessed 21 Feb 2019

  22. AWS Elastic Beanstalk. https://aws.amazon.com/elasticbeanstalk/. Accessed 21 Feb 2019

  23. Google: Angularjs. https://angularjs.org/. Accessed 21 Feb 2019

  24. Node.js Foundation: Node.js. https://nodejs.org/en/. Accessed 21 Feb 2019

  25. J.L. Axelson, Serial port complete: programming and circuits for RS-232 andRS-485 links and networks with disk. Lakeview Res. (1999)

    Google Scholar 

  26. OpenCPU: Opencpu home. https://www.opencpu.org/. Accessed 21 Feb 2019

  27. AWS IoT Greengrass. https://aws.amazon.com/greengrass/. Accessed 21 Feb 2019

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Acknowledgements

This work is supported in part by a Maryland Industrial Partnerships (MIPS) grant: the Low-Code Workflow Software for Life Sciences.

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Correspondence to Jianwu Wang .

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Guo, P., Peterson, R., Paukstelis, P., Wang, J. (2020). Cloud-Based Life Sciences Manufacturing System: Integrated Experiment Management and Data Analysis via Amazon Web Services. In: Yang, H., Qiu, R., Chen, W. (eds) Smart Service Systems, Operations Management, and Analytics. INFORMS-CSS 2019. Springer Proceedings in Business and Economics. Springer, Cham. https://doi.org/10.1007/978-3-030-30967-1_14

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