PAGERANK-INSPIRED APPROACH FOR MODELING LONG-TERM BEHAVIOR IN STOCHASTIC GENE REGULATORY NETWORKS

Abstract

Gene regulatory network (GRN) play a crucial role in understanding the complex interac-tions between
genes and their regulatory elements. However, accurately modeling the long-term behavior of GRNs
under stochasticity remains a challenging task. In this study, we propose a methodology that leverages the
concept of PageRank, originally developed for ranking web pages, to analyze the long-term behavior of
GRNs. This approach involves constructing a directed graph representation of the GRN, where genes are
represented as nodes and regulatory interactions as directed edges. We then adapt the PageRank
algorithm to the GRN context, considering the stochastic nature of gene expression and incorporating the
inherent randomness in regulatory interactions. By iteratively computing the PageRank scores, we obtain
a ranking of the transition states based on their long-term influence within the network. To evaluate the
effectiveness of our approach, we apply it to synthetic GRN models. Our findings cap-tured essential
aspects of the long-term behavior in stochastic GRNs. We observe that genes with higher PageRank
scores tend to have a greater influence on the overall network dynamics and exhibit more stable and
persistent expression patterns