One model fits all: Combining inference and simulation of gene regulatory networks.

The rise of single-cell data highlights the need for a nondeterministic view of gene expression, while offering new opportunities regarding gene regulatory network inference. We recently introduced two strategies that specifically exploit time-course data, where single-cell profiling is performed after a stimulus: HARISSA, a mechanistic network model with a highly efficient simulation procedure, and CARDAMOM, a scalable inference method seen as model calibration. Here, we combine the two approaches and show that the same model driven by transcriptional bursting can be used simultaneously as an inference tool, to reconstruct biologically relevant networks, and as a simulation tool, to generate realistic transcriptional profiles emerging from gene interactions.

MBNL-dependent impaired development within the neuromuscular system in myotonic dystrophy type 1.

Aims: Myotonic dystrophy type I (DM1) is one of the most frequent muscular dystrophies in adults. Although DM1 has long been considered mainly a muscle disorder, growing evidence suggests the involvement of peripheral nerves in the pathogenicity of DM1 raising the question of whether motoneurons (MNs) actively contribute to neuromuscular defects in DM1.

Methods: By using micropatterned 96-well plates as a coculture platform, we generated a functional neuromuscular model combining DM1 and muscleblind protein (MBNL) knock-out human-induced pluripotent stem cells-derived MNs and human healthy skeletal muscle cells.

Evidence for close molecular proximity between reverting and undifferentiated cells.

Background: According to Waddington's epigenetic landscape concept, the differentiation process can be illustrated by a cell akin to a ball rolling down from the top of a hill (proliferation state) and crossing furrows before stopping in basins or "attractor states" to reach its stable differentiated state. However, it is now clear that some committed cells can retain a certain degree of plasticity and reacquire phenotypical characteristics of a more pluripotent cell state. In line with this dynamic model, we have previously shown that differentiating cells (chicken erythrocytic progenitors (T2EC)) retain for 24 h the ability to self-renew when transferred back in self-renewal conditions.

Reverse engineering of a mechanistic model of gene expression using metastability and temporal dynamics.

Differentiation can be modeled at the single cell level as a stochastic process resulting from the dynamical functioning of an underlying Gene Regulatory Network (GRN), driving stem or progenitor cells to one or many differentiated cell types. Metastability seems inherent to differentiation process as a consequence of the limited number of cell types. Moreover, mRNA is known to be generally produced by bursts, which can give rise to highly variable non-Gaussian behavior, making the estimation of a GRN from transcriptional profiles challenging.

Reduction of a stochastic model of gene expression: Lagrangian dynamics gives access to basins of attraction as cell types and metastabilty.

Differentiation is the process whereby a cell acquires a specific phenotype, by differential gene expression as a function of time. This is thought to result from the dynamical functioning of an underlying Gene Regulatory Network (GRN). The precise path from the stochastic GRN behavior to the resulting cell state is still an open question.