Inference of dynamic interaction networks: A comparison between Lotka-Volterra and multivariate autoregressive models - Olivença, Daniel V.; Davis, Jacob D.; Voit, Eberhard O. (Frontiers in Bioinformatics, 2022)
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 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 literature: Lotka-Volterra (LV) models and Multivariate Autoregressive (MAR) models. These models are computational frameworks with different mathematical structures which, nevertheless, have both been proposed for the same purpose of inferring the interactions within coexisting population networks from observed time-series data. Here, we assess these dynamic network inference methods for the first time in a side-by-side comparison, using both synthetically generated and ecological datasets. MAR and LV models are mathematically equivalent at the steady state, but the results of our comparison suggest that LV models are generally superior in capturing the dynamics of networks with non-linear dynamics, whereas MAR models are better suited for analyses of networks of populations with process noise and close-to linear behavior.