From Boolean to probabilistic Boolean networks as models of genetic regulatory networks

By Ilya Shmulevich, Edward R. Dougherty, Wei Zhang. Proceedings of the IEEE, 2002

636 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/e5fd0bf9db8ec4f7dcde97920aafd1d3104bb566

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Citations in the corpus

• Linear Finite Dynamical Systems (2005, background): “The subject of the FDS has acquired renewed attention with regard to genetic networks, where in addition to the traditional boolean networks, there are also probabilistic genetic networks ( Shmulevich et al., 2002 ), machines of finite states, and finite sequential systems (Laubenbacher and Pareigis”
• Inferring gene regulatory networks from time series data using the minimum description length principle (2006, unknown): “Among the models relevant to this paper are Boolean networks, which model regulatory relations in terms of Boolean relationships and combinatorial logic circuits (Kauffman, 1969), probabilistic Boolean networks (PBNs), which are composed of finite numbers of constituent Boolean networks, each of whi”
• Observability, Reconstructibility and State Observers of Boolean Control Networks (2013, background): “Indeed, regulatory genes inside the cells act just like switches, that may take either an “on” or an “off” state (1 and 0, respectively), and this discovery led Kauffman [9] to introduce random Boolean networks as models for genetic networks (see also [14]).”