Agile Business Process Management in Research Projects of Life Sciences

  • Silke Holzmüller-Laue
  • Bernd Göde
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 90)

Abstract

In life science laboratories the sub-process automation of methods with semi- or full automated, isolated solutions called islands of automation and several IT sys tems dominate. There are deficits in networking of these sub-processes. The R&D processes in the life sciences research are complex, flexible, unstructured, knowledge-intensive, distributed, and parallel; they use heterogeneous resources, and com bi ne automated, semi-automated, and manual activities in high-variable process chains with a high number of control structures. This characteristic of LSA-processes makes high demands on an integrated process management that contains an interdisciplinary collaborative process control and the documentation of the global process from for example purchasing, sample storage, method development to analytics and interpretation of results as well as the extraction of knowledge.

Using methods, techniques, and tools of business process management (BPM) in the life science automation offers the potential to improve the automation level, the networking and the quality of the life science applications. The authors investigate the suitability of the standard based methods and techniques of BPM for the introduction of a flexible, integrated, and automated process management in the heterogeneous and hybrid systems in life sciences. This article will be focusing on the advances made in distributed workflow automation in highly variable system and application environments of research projects by use of BPM methods and tools.

Keywords

Agile R&D Processes BPM Life Sciences Process Automation Workflow Management 

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References

  1. 1.
    Kalorama Information: The Worldwide Market for Lab automation. New York, USA (2008)Google Scholar
  2. 2.
    Elands, J.: The Evolution of Laboratory Automation. In: Seethala, R., Fernandes, P.B. (eds.) Handbook of Drug Screening, pp. 477–492. Marcel Dekker, New York (2001)Google Scholar
  3. 3.
    Haney, S.A. (ed.): High Content Screening – Science, Techniques and Applications. Wiley-Interscience Series on Mass Spectrometry. Wiley Interscience, Hoboken (2008)Google Scholar
  4. 4.
    Janzen, W.P.: High Throughput Screening - Methods and Protocols. Humana Press Inc., Totowa (2002)CrossRefGoogle Scholar
  5. 5.
    Zhang, M., Nelson, B., Felder, R. (eds.): Life Science Automation: Fundamentals and Applications. Artech House Inc., Boston (2007)Google Scholar
  6. 6.
    Moore, K.W., Newman, R., Chan, G.K.Y., Leech, C., Allison, K., Coulson, J., Simpson, P.B.: Implementation of a High Specification Dual-Arm Robotic Platform to Meet Flexible Screening Needs. JALA - Journal of the Association for Laboratory Automation 12(2), 115–123 (2007)CrossRefGoogle Scholar
  7. 7.
    Russo, M.F., Sasso, A.: Modeling, analysis, simulation and control of laboratory automation systems using Petri nets: Part 1- Modeling. JALA - Journal of the Association for Laboratory Automation 10(3), 172–181 (2005)CrossRefGoogle Scholar
  8. 8.
    Bühl, W., Daniel, J., Höynck, M., Jänicke, W.B., Szarowski, S.: Laboratory resource planning for quality control of pharmaceuticals. Pharmazeutische Industrie 66(11 A), 1430–1434 (2004)Google Scholar
  9. 9.
    Rodziewicz, P., Bell, B.: Overview and architecture of the Java integration framework, hybrid scheduler, and Web-enabled LIMS. JALA - Journal of the Association for Laboratory Automation 9(6), 411–420 (2004)CrossRefGoogle Scholar
  10. 10.
    King, J., Gosine, R., Delaney, B., Norvell, T., O’Young, S.: Discrete event control and dynamic scheduling for tele-robotic mining. CIM Bulletin 96(1069), 116–118 (2003)Google Scholar
  11. 11.
    Lämmel, J.: Systemintegration im Bereich der Laborinformatik. LaborPraxis 10 (2004)Google Scholar
  12. 12.
    Dellert-Ritter, M.: LIMS - Aktueller Stand und zukünftige Trends. GIT Laborzeitschrift 54(1), 39–42 (2010)Google Scholar
  13. 13.
    Thurow, K., Göde, B., Dingerdissen, U., Stoll, N.: Laboratory Information Management Systems for Life Science Applications. Organic Process Research & Development 8(6), 970–982 (2004)CrossRefGoogle Scholar
  14. 14.
    Göde, B., Holzmüller-Laue, S., Haller, D., Schneider, I., Thurow, K.: Flexible IT-Plattform zur automatisierten HTS-Wirkstoffanalyse. GIT 9, 741–744 (2007)Google Scholar
  15. 15.
    Göde, B., Holzmüller-Laue, S., Rimane, K., Thurow, K.: Integrierte flexible Datenverarbeitung in einem webbasierten LIMS: Idee und Praxis eines Excel-Prozessors in Serverapplikationen. Chemie – Ingenieur – Technik 12, 2043–2049 (2007)CrossRefGoogle Scholar
  16. 16.
    Berndt, R.D., Takenga, M.C., Kuehn, S., Preik, P., Stoll, N., Thurow, K., Kumar, M., Weippert, M., Rieger, A., Stoll, R.: A Scalable and Secure Telematics Platform for the Hosting of Telemedical Applications. In: Case Study of a Stress and Fitness Monitoring. IEEE, 13th International Conference on e-Health Networking, Application and Services, Columbia, USA, June 13-15 (accepted, 2011)Google Scholar
  17. 17.
    Neubert, S., Arndt, D., Thurow, K., Stoll, R.: Mobile Real-Time Data Acquisition System for Application in Preventive Medicine. Telemedicine and e-Health 16(4), 504–509 (2010)CrossRefGoogle Scholar
  18. 18.
  19. 19.

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Silke Holzmüller-Laue
    • 1
  • Bernd Göde
    • 2
  1. 1.Center for Life Science Automation (celisca)University of RostockRostockGermany
  2. 2.Institute of AutomationUniversity of RostockRostockGermany

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