TY - JOUR
T1 - Boolean modeling of biological regulatory networks
T2 - A methodology tutorial
AU - Saadatpour, Assieh
AU - Albert, Réka
N1 - Funding Information:
The authors were supported by the NSF grant CCF-0643529.
PY - 2013/7/15
Y1 - 2013/7/15
N2 - Given the complexity and interactive nature of biological systems, constructing informative and coherent network models of these systems and subsequently developing efficient approaches to analyze the assembled networks is of immense importance. The integration of network analysis and dynamic modeling enables one to investigate the behavior of the underlying system as a whole and to make experimentally testable predictions about less-understood aspects of the processes involved. In this paper, we present a tutorial on the fundamental steps of Boolean modeling of biological regulatory networks. We demonstrate how to infer a Boolean network model from the available experimental data, analyze the network using graph-theoretical measures, and convert it into a predictive dynamic model. For each step, the pitfalls one may encounter and possible ways to circumvent them are also discussed. We illustrate these steps on a toy network as well as in the context of the Drosophila melanogaster segment polarity gene network.
AB - Given the complexity and interactive nature of biological systems, constructing informative and coherent network models of these systems and subsequently developing efficient approaches to analyze the assembled networks is of immense importance. The integration of network analysis and dynamic modeling enables one to investigate the behavior of the underlying system as a whole and to make experimentally testable predictions about less-understood aspects of the processes involved. In this paper, we present a tutorial on the fundamental steps of Boolean modeling of biological regulatory networks. We demonstrate how to infer a Boolean network model from the available experimental data, analyze the network using graph-theoretical measures, and convert it into a predictive dynamic model. For each step, the pitfalls one may encounter and possible ways to circumvent them are also discussed. We illustrate these steps on a toy network as well as in the context of the Drosophila melanogaster segment polarity gene network.
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U2 - 10.1016/j.ymeth.2012.10.012
DO - 10.1016/j.ymeth.2012.10.012
M3 - Article
C2 - 23142247
AN - SCOPUS:84881556356
SN - 1046-2023
VL - 62
SP - 3
EP - 12
JO - Methods
JF - Methods
IS - 1
ER -