Abstract Once centered on animal social behavior, investigations into cooperation have expanded across the tree of life to include microorganisms such as bacteria and viruses. Cooperative interactions are now understood to drive evolutionary dynamics within and between numerous microbial species and communities, including pathogen adaptation to and persistence in new hosts and environments. Identification and characterization of the underlying mechanisms of cooperation offer innovative opportunities for therapeutic interventions targeting difficult-to-treat pathogens through disruption of interactive networks. The current gold standards for evaluating microorganismal cooperation often rely on assessing coordinated changes of pheno-typic traits and the genetic and environmental factors that can affect them. Among these approaches, in vitro methods are labor-intensive, time-consuming, and often fail to replicate the natural microenvironment. Computational methods applied in vivo offer scalability and applicability but often require prior knowledge of metabolic pathways, restricting their use to bacterial systems. In contrast, sequence- and phylogeny-based frameworks can extend to viral datasets, though are typically con- strained by smaller sample sizes and incomplete annotations. Herein we focus on existing computational approaches used in identifying and/or characterizing cooperation and detail their advantages and limitations in shaping our understanding of cooperative pathogens.
Ramirez-Mata et al. (Sat,) studied this question.