Inference of dynamic interaction networks: A comparison between Lotka-Volterra and multivariate autoregressive models.

ABSTRACT: Networks are ubiquitous throughout biology, spanning the entire range from molecules to food webs and global environmental systems. Yet, despite substantial efforts by the scientific community, the inference of these networks from data still presents a problem that is unsolved in general. One frequent strategy of addressing the structure of networks is the assumption that the interactions among molecular or organismal populations are static and correlative. While often successful, these static methods are no panacea. They usually ignore the asymmetry of relationships between two species and inferences become more challenging if the network nodes represent dynamically changing quantities. Overcoming these challenges, two very different network inference approaches have been proposed in the

SUBMITTER: Olivenca DV 

PROVIDER: S-EPMC9815445 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature