4.6 Article

Diversity and Plasticity of Th Cell Types Predicted from Regulatory Network Modelling

Journal

PLOS COMPUTATIONAL BIOLOGY
Volume 6, Issue 9, Pages -

Publisher

PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pcbi.1000912

Keywords

-

Funding

  1. French National Agency [ANR-06-BYOS-0006, ANR-08-SYSC-003]
  2. Calouste Gulbenkian Foundation
  3. Agence Nationale de la Recherche (ANR) [ANR-06-BYOS-0006] Funding Source: Agence Nationale de la Recherche (ANR)

Ask authors/readers for more resources

Alternative cell differentiation pathways are believed to arise from the concerted action of signalling pathways and transcriptional regulatory networks. However, the prediction of mammalian cell differentiation from the knowledge of the presence of specific signals and transcriptional factors is still a daunting challenge. In this respect, the vertebrate hematopoietic system, with its many branching differentiation pathways and cell types, is a compelling case study. In this paper, we propose an integrated, comprehensive model of the regulatory network and signalling pathways controlling Th cell differentiation. As most available data are qualitative, we rely on a logical formalism to perform extensive dynamical analyses. To cope with the size and complexity of the resulting network, we use an original model reduction approach together with a stable state identification algorithm. To assess the effects of heterogeneous environments on Th cell differentiation, we have performed a systematic series of simulations considering various prototypic environments. Consequently, we have identified stable states corresponding to canonical Th1, Th2, Th17 and Treg subtypes, but these were found to coexist with other transient hybrid cell types that co-express combinations of Th1, Th2, Treg and Th17 markers in an environment-dependent fashion. In the process, our logical analysis highlights the nature of these cell types and their relationships with canonical Th subtypes. Finally, our logical model can be used to explore novel differentiation pathways in silico.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.6
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available