Phylosystemics: Merging Phylogenomics, Systems Biology, and Ecology to Study Evolution

Abstract

We define phylosystemics, a multidisciplinary strategy uniting short timescale interaction studies from systems biologists and ecologists with the longer timescale studies familiar to evolutionary biologists, taking advantage of methods from network sciences. Phylosystemics superimposes evolutionary information on entities/edges forming interaction networks produced by systems biology and ecology. At the molecular level, phylosystemics could provide evidence to infer and to time the evolution of molecular processes within a single branch of a phylogeny, in particular between the first and last common ancestors of a group arising during a major evolutionary transition. At the ecosystemic level, phylosystemics could culminate with the development of multilayer temporal networks encompassing biotic and abiotic interactions, whose analyses could unravel ecological interactions with evolutionary consequences. Trends in Microbiologyemergent field aiming to track the evolution of biological processes by general approaches of graph theory, then (ii) we demonstrate that phylosystemics is timely and actionable, and (iii) illustrate some major pay-offs expected from this approach.

The greening ashore

“…The Chloroplastida: stepwise adaptations to conquer land Both paleobotanical records and phylogenomic studies plot a monophyletic origin for the terrestrial flora from within the Chloroplastida [8,30,31]. This green archaeplastidal lineage is divided into three phyla, the recently identified Prasinodermophyta [32], the Chlorophyta, and the Streptophyta.…”

Hierarchies, Networks, and Causality: The Applied Evolutionary Epistemological Approach

“…But network science (e.g. Watson et al 2020) is currently flourishing largely in part to its ability to visualize reticulate evolutionary processes as they occur by symbiosis, symbiogenesis, lateral gene transfer and infective heredity (Gontier 2015). Networks differ from the classic, ladder-like hierarchies and instead depict interactional hierarchies (Gontier 2017) that are characterized by what we here call reticulate causation (Table 8).…”

“…And this brings forth the non-linear dynamics so typical of network hierarchies. But at present, the differential time intervals of the processes mapped remain understudied (but see Watson et al 2020).…”

Modeling the evolution of interconnected processes: It is the song and the singers

“…In contrast, ITSATS claims that embracing a common ontology, according to which all evolving entities can be described as patterns of interaction [9] provides a scientifically operational framework, inspired by work in systems biology as well as novel approaches in evolutionary biology. [11][12][13] More explicitly than ITSNTS, ITSATS thus emphasizes the hierarchical structure of systems, pointing out that at any level objects (singers) interacting to produce a song can be modeled as songs themselves, performed by other interacting objects (singers) at the next level down. Singers and songs become labels to be applied contextually.…”