13h00 - 13h30 – Samuel Chaffron (Combi team, LS2N, Nantes)
Environmental vulnerability of the global ocean plankton community interactome.
Marine plankton form complex communities of interacting organisms at the base of the food web, which sustain oceanic biogeochemical cycles, and help regulate climate. Though global surveys are starting to reveal ecological drivers underlying planktonic community structure, and predicted climate change responses, it is unclear how community-scale species interactions will be affected by climate change. Here we leveraged Tara Oceans sampling to infer a global ocean cross-domain plankton co-occurrence network – the community interactome – and used niche modeling to assess its vulnerabilities to environmental change. Globally, this revealed a plankton interactome self-organized latitudinally into marine biomes (Trades, Westerlies, Polar), and more connected poleward. Integrated niche modeling revealed biome-specific community interactome responses to environmental change, and forecasted most affected lineages for each community. These results provide baseline approaches to assess community structure and organismal interactions under climate scenarios, while identifying plausible plankton bioindicators for ocean monitoring of climate change.
13h30 - 14h00 – Stéphanie Chevalier (Lifeware / Inria Saclay)
Synthesis of Boolean networks from single-cell differentiation data.
Processes like cell differentiation and cancerisation have dynamical properties around the notion of trajectory (succession of changes in gene state), non-reachability (bifurcating event) and stability (differentiated cell). Single-cell data on such behaviors are now quite widely available but dynamical modelling with them remains too complex to be commonly leveraged. I will present the approach we develop to automatically infer dynamical models from such data and prior knowledge on gene interactions. The inference method consists in formulating the inference as a Boolean satisfiability problem, described as a logic program containing both the modelling formalism (Most Permissive Boolean network - MPBN) and the data on the biological process (prior knowledge, experimental measurements, dynamics, hypotheses). Several constraints have been implemented in Answer-Set Programming to ensure the desired dynamical properties, and thanks to this logic modeling it is now possible to exhaustively enumerate the MPBN compatible with the constraints of cell differentiation behaviors. In order to leverage single-cell data, I firstly run classification and trajectory reconstruction methods, then data are translated into logical form to describe the cells dynamics. I will present preliminary results obtained for a large-scale modeling of hematopoiesis from cell-scale transcriptomic sequencing data (single-cell RNA-seq data). Potential influences between genes and proteins are extracted from the SIGNOR database, which brings more than 5500 components (genes, proteins and complexes).
Dernière modification le 12/03/2021