Abstract
A mathematical model is proposed which is able to describe the most important features of cell differentiation, without requiring specific detailed assumptions concerning the interactions which drive the phenomenon. On the contrary, cell differentiation is described here as an emergent property of a generic model of the underlying gene regulatory network, and it can therefore be applied to a variety of different organisms. The model points to a peculiar role of cellular noise in differentiation and leads to non trivial predictions which could be subject to experimental testing. Moreover, a single model proves able to describe several different phenomena observed in various differentiation processes.
Highlights
A major challenge in complex systems biology is that of providing a general theoretical framework to describe the phenomena involved in cell differentiation, i.e. the process whereby stem cells, which can develop into different types, become progressively more specialized, The aim of this paper is that of proposing a dynamical model of cell differentiation which is able to cover a broad spectrum of experimentally observed phenomena
The signals correspond to the activation or deactivation of selected genes or groups of genes; 4. limited reversibility: the differentiation process is almost always irreversible but there are limited exceptions, in that a cell which has reached an intermediate degree of differentiation can come back to a previous stage, under the action of appropriate signals [5] [6]; 5. induced pluripotency: it has been observed that fully differentiated cells can come back to a pluripotent state by modifying the expression level of some genes [7] [8]; 6. induced change of cell type: it has been observed that the expression of few transcription factors can convert one cell type into another, e.g. mouse fibroblasts into induced functional neurons [9]
We hypothesize that the robust properties of differentiation are rather the outcome of the interaction of very many genes, so our model is based on a simplified dynamical model of genetic regulatory networks, namely noisy random Boolean networks (NRBNs for short), which allow simulations of large networks [12]
Summary
A major challenge in complex systems biology is that of providing a general theoretical framework to describe the phenomena involved in cell differentiation, i.e. the process whereby stem cells, which can develop into different types, become progressively more specialized, The aim of this paper is that of proposing a dynamical model of cell differentiation which is able to cover a broad spectrum of experimentally observed phenomena. Deterministic differentiation: in some experimental conditions (different from those of point 2 above), e.g. during embryo growth or in controlled experiments, specific signals trigger the development of a multipotent cell into a well-defined type [4], through a repeatable sequence of intermediate states. Since cell differentiation is tightly related to the activation/ deactivation of groups of genes, it is appropriate to look at models of gene networks in order to describe the dynamics of differentiation. NRBNs represent an extension of the well-known model of random Boolean networks [13] [14] [15] [16] (RBNs) that, in spite of their approximations, have been able to describe important experimental facts concerning gene expression[17] [18] [19]
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