Skip to main content

Advertisement

SpringerLink
Log in

Parallel computational techniques for the analysis of sedimentation velocity experiments in UltraScan

  • Original Contribution
  • Published:
Colloid and Polymer Science Aims and scope Submit manuscript

Abstract

The advent of parallel computing technology and low-cost computing hardware has facilitated the adoption of high-performance computing tools for the analysis of sedimentation data. Over the past 15 years, we have developed the UltraScan software (Demeler et al., http://ultrascan.uthscsa.edu) to support sedimentation analysis, experimental design, and data management. We describe here recent extensions and advances in methodology that have been adapted in UltraScan. High-performance computing methods implemented on parallel supercomputers utilizing grid computing technology are used to analyze sedimentation experiments at much higher resolution than was previously possible. We discuss the implementation of parallel computing in three novel algorithms used in UltraScan for modeling of sedimentation velocity experiments and provide guidelines for effective data analysis.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Moore GE (1965) Cramming more components onto integrated circuits. Electronics 38(8) (April)

  2. Brookes EH, Boppana RV, Demeler B (2006) Computing large sparse multivariate optimization problems with an application in biophysics. In: SuperComputing 2006 Conference Proceedings. ACM, IEEE, November

  3. Brookes EH, Demeler B (2006) Genetic algorithm optimization for obtaining accurate molecular weight distributions for sedimentation velocity experiments. In: Analytical Ultracentrifugation VIII. Prog Colloid & Polym Sci 131:78–82 (Springer)

  4. Grama A, Gupta A, Karypis G, Kumar V (2003) Introduction to parallel computing, 2nd edn. Addison-Wesley, Boston

    Google Scholar 

  5. Hall MW, Anderson JM, Amarasinghe SP, Murphy BR, Liao S-W, Bugnion E, Lam MS (1996) Maximizing multiprocessor performance with the SUIF compiler. IEEE Computer, December

  6. Lamm O (1929) Die Differentialgleichung der Ultrazentrifugierung. In: Ark Mat Astrol Fys 21B:1–4

  7. Lawson CL, Hanson RJ (1974) Solving least squares problems. Prentice Hall, New Jersey

    Google Scholar 

  8. Demeler B, van Holde KE (2004) Sedimentation velocity analysis of highly heterogeneous systems. In: Anal Biochem 335:279–288

  9. Holland JH (1992) Adaptation in natural and artificial systems, 2nd edn. MIT, Cambridge, MA

    Google Scholar 

  10. Brookes E, Demeler B (2007) Parsimonious regularization using genetic algorithms applied to the analysis of analytical ultracentrifugation experiments. In: Proceedings of Genetic and Evolutionary Computation Conference 2007 (in press)

  11. Demeler B, Brookes E (2007) Monte Carlo analysis of sedimentation experiments. Prog Colloid & Polym Sci (in press) DOI 10.1007/s00396-007-1699-4

  12. Demeler B (2005) UltraScan: a comprehensive data analysis software package for analytical ultracentrifugation experiments. Royal Society of Chemistry, UK

    Google Scholar 

  13. Wall L, Christiansen T, Orwant J (2000) Programming PERL, 3rd edn. O’Reilly and Associates, Sebastopol, California

    Google Scholar 

  14. Schuck P (2000) Size-distribution analysis of macromolecules by sedimentation velocity ultracentrifugation and Lamm equation modeling. Biophys J 78(3):1606–1619

    Article  CAS  Google Scholar 

Download references

Acknowledgment

We would like to thank Josh Wilson, Yu Ning, and Bruce Dubbs for contributions to the web interface code. This research has been supported by NSF Grant DBI-9974819, NIH Grant 1 R01 RR022200-01A1, and the San Antonio Life Science Institute with Grant #10001642, all to B.D. The parallel calculations were performed on the Linux cluster at the Bioinformatics Core Facility at the University of Texas Health Science Center and on the Lonestar cluster at TACC through NSF Teragrid Allocation # TG-MCB070038. We gratefully acknowledge support by the Robert J. Kleberg Jr. and Helen C. Kleberg Foundation.

Author information

Authors and Affiliations

  1. Department of Computer Science, The University of Texas at San Antonio, One UTSA Circle, San Antonio, TX, 78249-1644, USA

    Emre Brookes

  2. Department of Biochemistry, University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Drive, MC 7760, San Antonio, TX, 78229-3901, USA

    Borries Demeler

Authors
  1. Emre Brookes
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Borries Demeler
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Borries Demeler.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Brookes, E., Demeler, B. Parallel computational techniques for the analysis of sedimentation velocity experiments in UltraScan. Colloid Polym Sci 286, 139–148 (2008). https://doi.org/10.1007/s00396-007-1714-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00396-007-1714-9

Keywords

Search

Navigation