Random boolean networks are a model of genetic regulatory networks that has proven able to describe experimental data in biology. Random boolean networks not only reproduce important phenomena in cell dynamics, but they are also extremely interesting from a theoretical viewpoint, since it is possible to tune their asymptotic behaviour from order to disorder. The usual approach characterizes network families as a whole, either by means of static or dynamic measures. We show here that a more detailed study, based on the properties of system’s attractors, can provide information that makes it possible to predict with higher precision important properties, such as system’s response to gene knock-out. A new set of principled measures is introduced, that explains some puzzling behaviours of these networks. These results are not limited to random Boolean network models, but they are general and hold for any discrete model exhibiting similar dynamical characteristics.

Villani, M., Campioli, D., Damiani, C., Roli, A., Filisetti, A., Serra, A. (2017). Dynamical regimes in non-ergodic random Boolean networks. NATURAL COMPUTING, 16(2), 353-363 [10.1007/s11047-016-9552-7].

Dynamical regimes in non-ergodic random Boolean networks

Damiani, C;
2017

Abstract

Random boolean networks are a model of genetic regulatory networks that has proven able to describe experimental data in biology. Random boolean networks not only reproduce important phenomena in cell dynamics, but they are also extremely interesting from a theoretical viewpoint, since it is possible to tune their asymptotic behaviour from order to disorder. The usual approach characterizes network families as a whole, either by means of static or dynamic measures. We show here that a more detailed study, based on the properties of system’s attractors, can provide information that makes it possible to predict with higher precision important properties, such as system’s response to gene knock-out. A new set of principled measures is introduced, that explains some puzzling behaviours of these networks. These results are not limited to random Boolean network models, but they are general and hold for any discrete model exhibiting similar dynamical characteristics.
Articolo in rivista - Articolo scientifico
Attractors, Dynamical measures, Dynamical regimes, Gene knock-out, Random Boolean networks
English
353
363
11
Villani, M., Campioli, D., Damiani, C., Roli, A., Filisetti, A., Serra, A. (2017). Dynamical regimes in non-ergodic random Boolean networks. NATURAL COMPUTING, 16(2), 353-363 [10.1007/s11047-016-9552-7].
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/186459
Citazioni
  • Scopus 12
  • ???jsp.display-item.citation.isi??? 12
Social impact