The functional characterization of all genes and their gene products is the main challenge of the postgenomic era. Recent experimental and computational techniques have enabled the study of interactions among all proteins on a large scale. In this paper, approaches will be presented to exploit interaction information for the inference of protein structure, function, signalling pathways and ultimately entire interactomes. Interaction networks can be modelled as graphs, showing the operation of gene function in terms of protein interactions. Since the architecture of biological networks differs distinctly from random networks, these functional maps contain a signal that can be used for predictive purposes. Protein function and structure can be predicted by matching interaction patterns, without the requirement of sequence similarity. Moving on to a higher level definition of protein function, the question arises how to decompose complex networks into meaningful subsets. An algorithm will be demonstrated, which extracts whole signal-transduction pathways from noisy graphs derived from text-mining the biological literature. Finally, an algorithmic strategy is formulated that enables the proteomics community to build a reliable scaffold of the interactome in a fraction of the time compared with uncoordinated efforts.
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Conference Article|
June 01 2005
Algorithms for protein interaction networks
M. Lappe;
M. Lappe
* Max-Planck Institute of Molecular Genetics, Ihnestrasse 73, 14195 Berlin, Germany
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L. Holm
L. Holm
1
† Institute of Biotechnology and Department of Biosciences, P.O. Box 56, FI-00014, University of Helsinki, Finland
1 To whom correspondence should be addressed (email liisa.holm@helsinki.fi).
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Publisher: Portland Press Ltd
Received:
January 19 2005
Online ISSN: 1470-8752
Print ISSN: 0300-5127
© 2005 The Biochemical Society
2005
Biochem Soc Trans (2005) 33 (3): 530–534.
Article history
Received:
January 19 2005
Citation
M. Lappe, L. Holm; Algorithms for protein interaction networks. Biochem Soc Trans 1 June 2005; 33 (3): 530–534. doi: https://doi.org/10.1042/BST0330530
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