MRC National Institute for Medical Research, London
Science for Health
Quantitative tools for network analysis
30 November 2009
Many biological multi-component systems can be represented as networks, for example protein-protein interaction networks (PPINs). Network representations hold great promise for our understanding of complex cellular processes, but the analysis of the intrinsic network structure to extract biologically relevant information poses a considerable theoretical challenge. In recent years, experimental high-throughput technologies have been used to draft large PPINs of a diverse range of organisms, such as yeast (S.cerevisiae), fly (D.melanogaster) and human (H.sapiens). However, these network data are incomplete and are known to be biased by the experimental technique employed. This makes their theoretical analysis even more challenging, in particular when attempting cross-species genomic comparisons.
Jens Kleinjung in NIMR's Division of Mathematical Biology worked with a team of mathematicians and computational biologists at King's College London to derive exact information-theoretic tools for the quantitative description and comparison of macroscopic network properties. These were then used to analyse a large set of PPINs. Going beyond the conventional network description in terms of degree statistics ('degree' is the number of interaction partners), the new theoretical framework introduces novel concepts such as the network 'complexity', based on degree-degree correlations between network nodes, and the 'distance' between two networks as a measure of their structural similarity.
Colour plot of the relative degree correlations in PPINs of (i) E.coli and (ii) H.sapiens
E. coli
H. Sapiens
The information-theoretical tools provide us with an unprecedented amount of detail about the network structure, because they are precise analytical expressions designed to quantify connectivity patterns. We can compare single biological networks with random networks of the same degree statistics, to measure the divergence from randomness or measure the distance between pairs of networks to estimate changes in the connectivity.
Jens Kleinjung
Original article
The research findings are published in full in:
A Annibale, A C C Coolen, L P Fernandes, F Fraternali and J Kleinjung (2009).
Tailored graph ensembles as proxies or null models for real networks I: tools for quantifying structure
Journal of Physics A: Mathematical and Theoretical 42 485001. Publisher abstract
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