Abstract
Cell fate reprogramming, such as the generation of insulin-producing β cells from other pancreas cells, can be achieved by external modulation of key transcription factors. However, the known gene regulatory interactions that form a complex network with multiple feedback loops make it increasingly difficult to design the cell reprogramming scheme because the linear regulatory pathways as schemes of causal influences upon cell lineages are inadequate for predicting the effect of transcriptional perturbation. However, sufficient information on regulatory networks is usually not available for detailed formal models. Here we demonstrate that by using the qualitatively described regulatory interactions as the basis for a coarse-grained dynamical ODE (ordinary differential equation) based model, it is possible to recapitulate the observed attractors of the exocrine and β, δ, α endocrine cells and to predict which gene perturbation can result in desired lineage reprogramming. Our model indicates that the constraints imposed by the incompletely elucidated regulatory network architecture suffice to build a predictive model for making informed decisions in choosing the set of transcription factors that need to be modulated for fate reprogramming.
Highlights
A gene regulatory network (GRN) in which fate-determining transcription factors (TFs) regulate each other drives the development of tissues by orchestrating the activation or suppression of the appropriate genes across the genome to establish the steady-state gene expression patterns that specify a given cell type [1]
Ever since the recognition of gene regulation it has been proposed that cell differentiation into a variety of cell types is due to the emergence of multiple stable attractor states in GRNs which guarantee the stability of the cell type specific expression patterns [2,3,4]
We seek to answer the following pragmatic question: does the qualitative information on functional and regulatory relationships between key TFs reported in the literature generate sufficient constraints on the dynamics of a system so that one can formalize how cell fate commitment, natural or
Summary
A gene regulatory network (GRN) in which fate-determining transcription factors (TFs) regulate each other drives the development of tissues by orchestrating the activation or suppression of the appropriate genes across the genome to establish the steady-state gene expression patterns that specify a given cell type [1]. Early transdifferentiation experiments or reprogramming between related cell lineages revealed this expected cell line plasticity and self-organization [7,8,9] they have received little attention because of the deeply rooted dogma of immutability between cell lineages. Such reprogrammability has seen a revival in the past years owning to the increasing understanding of some governing principles of fate determination by the transcriptional network and the recent interest in the successful reprogramming of cell phenotypes for regenerative medicine, including the conversion of a variety of adult somatic cells into the embryonic stem cell like state [10]
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