We need an ontology that can represent a diverse set of processes including, for example, as well as phylogenetic relations, interactions that stabilise or destabilise the interacting entities, spatial relations, ecological connections, genetic exchanges, etc.
In this article we consider microbial entities as more or less stabilised functional wholes, and sketch a network-based ontology that can represent a diverse set of processes including, for example, as well as phylogenetic relations, interactions that stabilise or destabilise the interacting entities, spatial relations, ecological connections, and genetic exchanges.
There are many different sources of information that can be used in PPI prediction [7], including protein structures, phylogenetic distribution, interactions between homologous proteins in other organisms, genomic neighborhood, and gene fusions.
This calls for a more explicit integration of how phylogenetic structure drives interactions in network studies (Eklof et al. 2011), which will require sequencing both neutrally evolving and functional markers involved in the interaction.
For each species, annotations are based on machine learning algorithms applied to an integrated data collection including protein motif annotation, phenotype and disease association, phylogenetic profiles, protein interactions and gene expression.
Although a global pest, DBM is also an excellent system for studies on comparative genomics, ecological entomology, morphogenesis, insecticide resistance, migration, phylogenetic evolution and interactions with host plants and/or natural enemies (4).
Furthermore, Gómez et al. [12] showed that phylogenetic conservatism of interaction patterns was equally likely to occur in mutualistic and antagonistic interactions, suggesting no different mechanism for both type of interactions.
Next, we analyzed moonlighting proteins in terms of protein-protein interaction, gene expression, phylogenetic profile, and genetic interaction networks.
Molecular co-evolution between amino acids can be the result of their structural, functional, interaction, phylogenetic, or stochastic link [ 77].
In addition, we characterize with parsimony and Bayesian analyses the phylogenetic utility and interaction of characters with statistical measures, including the utility of highly consistent (non-homoplasious) characters as a conservative measure of phylogenetic robustness.
