Abstract
Motivation: Scale-free networks have had a profound impact in Biology. Network theory is now used routinely to visualize, navigate through, and help understand gene networks, protein–protein interactions, regulatory networks and metabolic pathways. Here we analyse the numerical rather than topological properties of biological networks and focus on the study of kinetic rate constants within pathways.
Results: We have analysed all current entries in the BioModels database and show that the kinetic rate parameters follow Benford's; law closely. The cumulative histogram plot reveals an underlying power-law. This implies that these data are scale-invariant, thus placing biological network topology and their chemistry on an equivalent ‘scale-free’ power-law foundation.
Results: We have analysed all current entries in the BioModels database and show that the kinetic rate parameters follow Benford's; law closely. The cumulative histogram plot reveals an underlying power-law. This implies that these data are scale-invariant, thus placing biological network topology and their chemistry on an equivalent ‘scale-free’ power-law foundation.
Original language | English |
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Pages (from-to) | 741-743 |
Number of pages | 3 |
Journal | Bioinformatics |
Volume | 24 |
Issue number | 6 |
DOIs | |
Publication status | Published - 2008 |