RT Journal Article
SR Electronic
T1 Revisiting Parameter Estimation in Biological Networks: Influence of Symmetries
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 674739
DO 10.1101/674739
A1 Jithin K. Sreedharan
A1 Krzysztof Turowski
A1 Wojciech Szpankowski
YR 2019
UL http://biorxiv.org/content/early/2019/06/18/674739.abstract
AB Graph models often give us a deeper understanding of real-world networks. In the case of biological networks they help in predicting the evolution and history of biomolecule interactions, provided we map properly real networks into the corresponding graph models. In this paper, we show that for biological graph models many of the existing parameter estimation techniques overlook the critical property of graph symmetry (also known formally as graph automorphisms), thus the estimated parameters give statistically insignificant results concerning the observed network. To demonstrate it and to develop accurate estimation procedures, we focus on the biologically inspired duplication-divergence model, and the up-to-date data of protein-protein interactions of seven species including human and yeast. Using exact recurrence relations of some prominent graph statistics, we devise a parameter estimation technique that provides the right order of symmetries and uses phylogenetically old proteins as the choice of seed graph nodes. We also find that our results are consistent with the ones obtained from maximum likelihood estimation (MLE). However, the MLE approach is significantly slower than our methods in practice.