Overall objectives
Digital microbial ecology studies communities of microorganisms in delimited ecosystems, who interact through the production and consumption of metabolic goods. These interactions define complex behaviors that are much more that the sum of the individual behaviors of the community members, and arise from cooperation and competition between a diversity of organisms providing a diversity of beneficial and harmful functions.
Pleiade builds methodological tools for digital microbioal ecology, that elucidate the behavior of microbial communities.
- We measure the
diversity of organismsby comparing DNA sequences in a sampled environment and building compact geometric representations. - We identify the
diversity of functionsperformed by these organisms, and represent them with genome-scale metabolic models and reasoning-compatible knowledge bases. - We build numerical and discrete
spatio-temporal modelsof community behavior.
Pleiade develops a synergistic, iterative combination of a mechanistic community-based strategy for deciphering the diversity in cultures and environmental samples, through metagenomic and metabolomic analysis of functional diversity and metabarcoding analysis of taxonomic diversity; and a phenomenological function-based strategy for contributing to digital twins of natural or designed communities through numerical models.
Shared methodologies needed to scale up to the complexity of biological systems, include high-performance computing (HPC); machine learning, including clustering, meta-modeling and classification for knowledge engineering; machine reasoning, specifically logical and rule-based methods used for model inference and network analysis. Logicial methods in particular promote explainable inference, since the rules are expressed in biological terms and are auditable by biologists, independently from the combinatorial and heuristic optimization techniques used to apply the rules.
Pleiade maintains strong collaborative relations with experimental biologists, and is committed to developing applications in ecology, evolution, biotechnology, and health. Team resources are dedicated to facilitating the adoption of our research by non specialist users, through development of reusable software, integration in HPC frameworks, improvement of web-based environments, and deployment of Jupyter, Galaxy, and Kubernetes interfaces.
