Grandison Scott, Morris Richard J
Department of Computational & Systems Biology, John Innes Centre, Norwich Research Park, Colney Lane, NR4 7UH Norwich, UK.
Bioinformatics. 2008 Mar 15;24(6):741-3. doi: 10.1093/bioinformatics/btn041. Epub 2008 Jan 30.
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.
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.
无标度网络在生物学领域产生了深远影响。网络理论如今被常规用于可视化、浏览并帮助理解基因网络、蛋白质 - 蛋白质相互作用、调控网络和代谢途径。在此，我们分析生物网络的数值特性而非拓扑特性，并专注于研究途径内的动力学速率常数。
我们分析了生物模型数据库中的所有当前条目，并表明动力学速率参数紧密遵循本福特定律。累积直方图显示出潜在的幂律。这意味着这些数据是尺度不变的，从而将生物网络拓扑及其化学性质置于等效的“无标度”幂律基础之上。
