Dynamic directionality of cell states in Boolean gene regulatory networks; a model of structural constraints in cell development.



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This project studies the dynamics of three Boolean gene regulatory networks for B and T cell differentiation and proposes an original gene regulatory network for larval fat body remodeling in Drosophila. The difference between the limited number of attractors in the gene regulatory networks and the many transient states that lead to the attractors can be seen as analogous to the finite number of phenotypic traits and their underlying genetic variation. The entropy, self transitioning probability, attractability and steady-state probability of the Markov Chain simulation of transitions between the attractors was compared with the experimentally observed phenotypic transformation of cells undergoing differentiation in vitro and in vivo. The model was able to rank attractors bearing markers of differentiation in a logical order. The reconstruction of the potential transition graph revealed cyclic patterns of recurring attractor states. This supports the hypothesis of an attractor of attractors: the Boolean version of canalization. The construction of a gene regulatory network for larval fat body remodeling shed light on components and combinations of components that instruct the system to maintain its inherit canalization.



Boolean Network, Canalization, Entropy, Steady State Probability, Attractor