Modeling relaxation experiments with a mechanistic model of gene expression.

Background: In the present work, we aimed at modeling a relaxation experiment which consists in selecting a subfraction of a cell population and observing the speed at which the entire initial distribution for a given marker is reconstituted.

Methods: For this we first proposed a modification of a previously published mechanistic two-state model of gene expression to which we added a state-dependent proliferation term. This results in a system of two partial differential equations.

TopoDoE: a design of experiment strategy for selection and refinement in ensembles of executable gene regulatory networks.

Background: Inference of Gene Regulatory Networks (GRNs) is a difficult and long-standing question in Systems Biology. Numerous approaches have been proposed with the latest methods exploring the richness of single-cell data. One of the current difficulties lies in the fact that many methods of GRN inference do not result in one proposed GRN but in a collection of plausible networks that need to be further refined.

Multiscale chromatin dynamics and high entropy in plant iPSC ancestors.

Plant protoplasts provide starting material for of inducing pluripotent cell masses that are competent for tissue regeneration in vitro, analogous to animal induced pluripotent stem cells (iPSCs). Dedifferentiation is associated with large-scale chromatin reorganisation and massive transcriptome reprogramming, characterised by stochastic gene expression. How this cellular variability reflects on chromatin organisation in individual cells and what factors influence chromatin transitions during culturing are largely unknown.

Kernel-based testing for single-cell differential analysis.

Single-cell technologies offer insights into molecular feature distributions, but comparing them poses challenges. We propose a kernel-testing framework for non-linear cell-wise distribution comparison, analyzing gene expression and epigenomic modifications. Our method allows feature-wise and global transcriptome/epigenome comparisons, revealing cell population heterogeneities.