Recently a cell differentiation model based on noisy random Boolean networks has been proposed. This mathematical model is able to describe in an elegant way the most relevant features of cell differentiation. Noise plays a key role in this model; the different stages of the differentiation process are emergent dynamical configurations deriving from the control of the intracellular noise level. In this work we compare two approaches to this cell differentiation framework: the first one (already present in the literature) is focused on a network analysis representing the average wandering of the system among its attractors, whereas the second (new) approach takes into consideration the dynamical stories of thousands of individual cells. Results showed that under a particular noise condition the two approaches produce comparable results. Therefore both can be used to model the cell differentiation process in an integrative and complementary manner.

A comparison between threshold ergodic sets and stochastic simulation of boolean networks for modelling cell differentiation / Braccini, Michele; Roli, Andrea; Villani, Marco; Serra, Roberto. - 830(2018), pp. 116-128. ((Intervento presentato al convegno 12th Italian Workshop on Artificial Life and Evolutionary Computation, WIVACE 2017 tenutosi a ita nel 2017 [10.1007/978-3-319-78658-2_9].

A comparison between threshold ergodic sets and stochastic simulation of boolean networks for modelling cell differentiation

Villani, Marco;Serra, Roberto
2018

Abstract

Recently a cell differentiation model based on noisy random Boolean networks has been proposed. This mathematical model is able to describe in an elegant way the most relevant features of cell differentiation. Noise plays a key role in this model; the different stages of the differentiation process are emergent dynamical configurations deriving from the control of the intracellular noise level. In this work we compare two approaches to this cell differentiation framework: the first one (already present in the literature) is focused on a network analysis representing the average wandering of the system among its attractors, whereas the second (new) approach takes into consideration the dynamical stories of thousands of individual cells. Results showed that under a particular noise condition the two approaches produce comparable results. Therefore both can be used to model the cell differentiation process in an integrative and complementary manner.
apr-2018
12th Italian Workshop on Artificial Life and Evolutionary Computation, WIVACE 2017
ita
2017
830
116
128
Braccini, Michele; Roli, Andrea; Villani, Marco; Serra, Roberto
A comparison between threshold ergodic sets and stochastic simulation of boolean networks for modelling cell differentiation / Braccini, Michele; Roli, Andrea; Villani, Marco; Serra, Roberto. - 830(2018), pp. 116-128. ((Intervento presentato al convegno 12th Italian Workshop on Artificial Life and Evolutionary Computation, WIVACE 2017 tenutosi a ita nel 2017 [10.1007/978-3-319-78658-2_9].
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11380/1162672
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