Skip to main content

Computational Biology — Algorithms and More

  • Conference paper
  • First Online:
Algorithms - ESA 2000 (ESA 2000)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1879))

Included in the following conference series:

  • 915 Accesses

Abstract

Computational biology is an area in applied computer science that has gained much attention recently. The reason is that new experimental methods in molecular biology and biochemistry have a.orded entirely novel ways of inspecting the molecular basis of life’s processes. Experimental breakthroughs have occurred in quick succession, with the first completely sequenced bacterial genome being published in 1995 (genome length 1.83 Mio bp, 1700 genes) [8], the first eukaryote yeast following in 1996 (genome length 13 Mio bp, 6275 genes) [9], the first multicellular organism C. elegans being sequenced in late 1998 (97 Mio bp, 19000 genes) [5], the fruit.y coming along this February (120 Mio bp, 13600 genes) [1] and man being pre-announced in April 2000. Several dozen completely sequenced microbial genomes are available today. This puts biology on a completely new footing since, for the first time, it can be ascertained not only which components are necessary to administer life but also which ones suffice.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. M.D. Adams et al., The Genome Sequence of Drosophila Melanogaster, Science 287 (2000) 2185–2195. http://www.celera.com/celerascience/index.cfm http://flybase.bio.indiana.edu/

    Article  Google Scholar 

  2. A. Bateman et al., The Pfam Protein Families Database, Nucleic Acids Research 28 (2000) 263–266. http://pfam.wustl.edu/

    Article  Google Scholar 

  3. Proteins: Structure, Function and Genetics, Suppl 1 (1997). http://PredictionCenter.llnl.gov/casp2/Casp2.html

  4. Proteins: Structure, Function and Genetics, Suppl: Third Meeting on the Critical Assessment of Techniques for Protein Structure Prediction (1999). http://PredictionCenter.llnl.gov/casp3/Casp3.html

  5. The C. elegans Sequencing Consortium, Genome Sequence of the Nematode C. elegans: A Platform for Investigating Biology, Science 282 (1998) 2012–2018. http://www.wormbase.org/

  6. J. L. DeRisi, V.R. Iyer, P.O. Brown, Exploring the metabolic and genetic control of gene expression on a genomic scale, Science 278 (1997) 680–685. http://cmgm.stanford.edu/pbrown/explore/

    Article  Google Scholar 

  7. S.R. Eddy, Profile hidden Markov Models, Bioinformatics 14,9 (1998) 755–763.

    Article  Google Scholar 

  8. R.D. Fleischmann, et al., Whole-genome random sequencing and assembly of Haemophilus influenzae Rd., Science 269 (1995) 496–512. http://www.tigr.org/tdb/mdb/mdb.html

    Article  Google Scholar 

  9. A. Goffeau et al., Life with 6000 genes, Science 274 (1996) 546–567. http://www.mips.biochem.mpg.de/proj/yeast/

    Article  Google Scholar 

  10. T. Jaakola, M. Diekhans, D. Haussler, Using the Fisher kernel method to detect remote protein homologies, In Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology (ISMB99), AAAI Press (1999) 149–158.

    Google Scholar 

  11. A. Krogh, M. Brown, I. S. Mian, K. Sjölander und D. Haussler (1994). Hidden Markov Models in computational biology: application to protein modeling. Journal of Molecular Biology 235, 1501–1531.

    Article  Google Scholar 

  12. E.M. Marcotte et al., A combined algorithm for genome-wide prediction of protein function, Nature 402 (1999) 83–86.

    Article  Google Scholar 

  13. H. Nielsen, S. Brunak, G. vonHeijne, Review. Machine learning approaches to the prediction of signal peptides and other protein sorting signals, Protein Engineering 12 (1999) 3–9.

    Article  Google Scholar 

  14. M. Pellegrini et al., Assigning protein functions by comparative genome analysis: protein phylogenetic profiles, Proc. Natl. Acad. Sci. USA 96 (1999) 4285–4288.

    Article  Google Scholar 

  15. N.N. Alexandrov, R. Nussinov, R.M. Zimmer, Fast Protein Fold Recognition via Sequence to Structure Alignment and Contact Capacity Potentials, Proceedings of the First Pacific Symposium on Biocomputing, World Scientific Publ. (1996) 53–72. http://cartan.gmd.de/ToPLign.html-Presents a dynamic programming method for protein folding. The key of the contribution is a novel and very effective cost function. A frequently used protein folding program on the internet.

  16. I. Koch, T. Lengauer, E. Wanke, An Algorithm for Finding all Common Subtopologies in a Set of Protein Structures, Journal of Computational Biology 3,2 (1996) 289–306.-Presents an algorithm based on clique enumeration that compares 3D protein structures

    Article  Google Scholar 

  17. B. Kramer, M. Rarey, T. Lengauer, Evaluation of the FlexX incremental construction algorithm for protein-ligand docking, PROTEINS: Structure, Function and Genetics 37 (1999) 228–241.-Detailed evaluation of FlexX on a set of 200 protein-ligand complexes.

    Article  Google Scholar 

  18. C. Lemmen, C. Hiller, T. Lengauer, RigFit: A New Approach to Superimposing Ligand Molecules, Journal of Computer-Aided Molecular Design 12,5 (1998) 491–502.-Fast rigid molecular superpositioning method based on algorithmic approach taken from computer-based crystallography.

    Article  Google Scholar 

  19. C. Lemmen, T. Lengauer, G. Klebe, FlexS: A Method for Fast Flexible Ligand Superposition, Journal of Medicinal Chemistry 41,23 (1998) 4502–4520. http://cartan.gmd.de/flex-bin/FlexS-Presents a method for superposing two drug molecules in 3D space in order to ascertain whether they have similar biochemical function. This algorithm offers an answer to the protein-ligand docking problem in the common case that the 3D structure of the protein is not available. FlexS is a commercialized software product that is a successor development of FlexX (see below).

    Article  Google Scholar 

  20. T. Lengauer, Molekulare Bioinformatik: Eine interdisziplinäre Herausforderung. In Highlights aus der Informatik (I. Wegener ed.), Springer Verlag, Heidelberg (1996) 83–111.-An earlier review paper on algorithmic aspects of computational biology, somewhat more detailed.

    Google Scholar 

  21. M. Rarey, J.S. Dixon, Feature Trees: A new molecular similarity measure based on tree matching, Journal of Comput. Aided Mol. Design, 12 (1998) 471–490. http://cartan.gmd.de/ftrees/-Presents a method for very fast structural molecular comparison of drug molecules.

    Article  Google Scholar 

  22. M. Rarey, B. Kramer, T. Lengauer, G. Klebe, A Fast Flexible Docking Method Using an Incremental Construction Algorithm. Journal of Molecular Biology 261,3 (1996) 470–489. http://cartan.gmd.de/flexx//html/flexx-intro.html-Presents a fast algorithm for docking drug molecules into the binding sites of target proteins. Thus the program takes the structures of the two involved molecules and computes the structure of the complex (drug bound to protein). The program also returns a estimate of the binding energy, in order to discriminate between tightly and loosely binding drugs. The structural flexibility of the drug molecule is treated algorithmically; the protein is considered rigid. This algorithm is the basis of a successfully commercialized software product FlexX.

    Article  Google Scholar 

  23. M. Rarey, T. Lengauer, A Recursive Algorithm for Efficient Combinatorial Library Docking, to appear in Drug Discovery and Design (2000).-Presents a version of FlexX that treats combinatorial libraries.

    Google Scholar 

  24. R. Thiele, R. Zimmer, T. Lengauer, Protein Threading by Recursive Dynamic Programming, Journal of Molecular Biology 290 (1999) 757–779. http://cartan.gmd.de/ToPLign.html-introduces a combinatorial method that heuristically solves an NP-hard formulation of the protein folding problem.

    Article  Google Scholar 

  25. A. Zien, G. Rätsch, S. Mika, B. Schölkopf, C. Lemmen, A. Smola, T. Lengauer, K. Müller, Engineering support vector machines kernels that recognize translation initiation sites, to appear in Bioinformatics (2000)-Uses support vector machines to solve an important classification problem in computational biology

    Google Scholar 

  26. A. Zien, R. Küffner, R. Zimmer, T. Lengauer, Analysis of Gene Expression Data With Pathway Scores, International Conference on Intelligent Systems for Molecular Biology (ISMB’00), AAAI Press (2000)-Presents a statistical method for validating a biological hypothesis that assigns a protein under study to a certain biochemical pathway. The method uses correlation studies of differential expression data to arrive at its conclusion.

    Google Scholar 

  27. A. Zien, R. Zimmer, T. Lengauer, A simple iterative approach to parameter optimization, Proceedings of the Fourth Annual Conference on Research in Computational Molecular Biology (RECOMB’00), ACM Press (2000)-Offers a combinatorial method for optimizing hard-to-set parameters in protein folding programs.

    Google Scholar 

  28. R. Zimmer, T. Lengauer, Fast and Numerically Stable Parametric Alignment of Biosequences. Proceedings of the First Annual Conference on Research in Computational Molecular Biology (RECOMB’97) (1997) 344–353.-Solves an algorithmically challenging parametric version of the pairwise sequence alignment problem.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2000 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Lengauer, T. (2000). Computational Biology — Algorithms and More. In: Paterson, M.S. (eds) Algorithms - ESA 2000. ESA 2000. Lecture Notes in Computer Science, vol 1879. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45253-2_2

Download citation

  • DOI: https://doi.org/10.1007/3-540-45253-2_2

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41004-1

  • Online ISBN: 978-3-540-45253-9

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics