Random Boolean network models and the yeast transcriptional network

By Stuart A. Kauffman, C. Peterson, B. Samuelsson, C. Troein. Proceedings of the National Academy of Sciences of the United States of America, 2003

584 citations recorded by the source corpus.

The recently measured yeast transcriptional network is analyzed in terms of simplified Boolean network models, with the aim of determining feasible rule structures, given the requirement of stable solutions of the generated Boolean networks. We find that, for ensembles of generated models, those with canalyzing Boolean rules are remarkably stable, whereas those with random Boolean rules are only marginally stable. Furthermore, substantial parts of the generated networks are frozen, in the sense that they reach the same state, regardless of initial state. Thus, our ensemble approach suggests that the yeast network shows highly ordered dynamics.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/9a191582874b1b892b2dab873fa5dfccbfa7459e

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Citations in the corpus

Biomolecular computing systems: principles, progress and potential (2012, background): “Circuit models resemble coupled chemical reactions, in which the concentrations of individual species are interpreted as their values, and individual interactions are compared to circuit wires 21–24 .”
Modelling and analysis of gene regulatory networks (2008, methodology): “Boolean networks were recently used to analyse the relationship between regulation functions and network stability in the yeast transcriptional network, using only the network's structur”
Controllability of Boolean control networks via the Perron-Frobenius theory (2012, background): “Key words: Logical systems, reachability, controllability, gene regulating networks, systems biology, nonnegative matrices.”
Colloquium: Criticality and dynamical scaling in living systems (2017, methodology): “Hence, criticality might confer on such networks an optimal tradeoff between the robustness and accuracy that biological machinery demands and responsiveness to environmental clues (Kauffman et al., 2003).”
Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review (2012, background): “on network perturbations (Kauffman et al., 2003; Shmulevich & Kauffman, 2004; Berg et al., 2005; Antal et al., 2009; Farkas et al., 2011).”
Robustness Can Evolve Gradually in Complex Regulatory Gene Networks with Varying Topology (2007, result): “It is because our work starts from such a developmental framework that S(0) and S‘ play a central role; this is in contrast with most studies determining the generic properties of random Boolean networks [30,31,37,38,47–50].”
Robustness and fragility of Boolean models for genetic regulatory networks. (2005, methodology): “Typical timedependent Boolean models use synchronous updating rules (Kauffman et al., 2003; Albert and Othmer, 2003; Bodnar, 1997; Kauffman, 1993), assuming that the timescales of the processes taking place in the system are similar.”