Pathway structure determination in complex stochastic networks with non-exponential dwell times.
Analysis of complex networks has been widely used as a powerful tool for investigating various physical, chemical, and biological processes. To understand the emergent properties of these complex systems, one of the most basic issues is to determine the structure and topology of the underlying networks. Recently, a new theoretical approach based on first-passage analysis has been developed for investigating the relationship between structure and dynamic properties for network systems with exponential dwell time distributions. However, many real phenomena involve transitions with non-exponential waiting times. We extend the first-passage method to uncover the structure of distinct pathways in complex networks with non-exponential dwell time distributions. It is found that the analysis of early time dynamics provides explicit information on the length of the pathways associated to their dynamic properties. It reveals a universal relationship that we have condensed in one general equation, which relates the number of intermediate states on the shortest path to the early time behavior of the first-passage distributions. Our theoretical predictions are confirmed by extensive Monte Carlo simulations.
Department of Chemistry, Rice University, Houston, Texas 77005, USA.
Xin
X
Li
Department of Chemistry and Center for Theoretical Biological Physics, Rice University, Houston, Texas 77005, USA.
Anatoly B
AB
Kolomeisky
Department of Theory and Bio-Systems, Max Planck Institute of Colloids and Interfaces, 14424 Potsdam, Germany.
Angelo
A
Valleriani
D000465:Algorithms; D001704:Biopolymers / Q000737:chemistry* / Q000378:metabolism*; D003198:Computer Simulation; D008954:Models, Biological; D008956:Models, Chemical; D015233:Models, Statistical; D015398:Signal Transduction / Q000502:physiology*; D013269:Stochastic Processes; D013329:Structure-Activity Relationship
D001704:Biopolymers