Robustness and fragility of Boolean models for genetic regulatory networks.

By Madalena Chaves, Réka Albert, Eduardo Sontag. Journal of Theoretical Biology, 2005

385 citations recorded by the source corpus.

Interactions between genes and gene products give rise to complex circuits that enable cells to process information and respond to external signals. Theoretical studies often describe these interactions using continuous, stochastic, or logical approaches. We propose a new modeling framework for gene regulatory networks, that combines the intuitive appeal of a qualitative description of gene states with a high flexibility in incorporating stochasticity in the duration of cellular processes. We apply our methods to the regulatory network of the segment polarity genes, thus gaining novel insights into the development of gene expression patterns. For example, we show that very short synthesis and decay times can perturb the wild-type pattern. On the other hand, separation of time-scales between pre- and post-translational processes and a minimal prepattern ensure convergence to the wild-type expression pattern regardless of fluctuations.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/b09e27256221b07554aad7e32a18ae69051deccc

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Citations in the corpus

Dynamical analysis of a generic Boolean model for the control of the mammalian cell cycle (2006, background): “Chaves et al. (2005) have recently explored this option to improve their Boolean model analysis of the segment polarity network Dynamical analysis of a generic Boolean model for the control of the mammalian cell cycle”
Scale-free networks in cell biology (2005, background): “Indeed, increasing evidence indicates the crucial role of network topology in determining dynamic behavior and function and robustness to fluctuations in kinetic parameters (Albert and Othmer, 2003; Barkai and Leibler, 1997; Chaves et al., 2005; Li, F. et al., 2004; von Dassow et al., 2000).”
Robustness Can Evolve Gradually in Complex Regulatory Gene Networks with Varying Topology (2007, background): “Quantitative models of cellular circuits help us to understand processes as different as circadian rhythms [17–25], the cell cycle [26], organismal development [7,9,10,16,27–31], bacterial chemotaxis [8], and the behavior of synthetic circuitry [32–36].”
A Logical Model Provides Insights into T Cell Receptor Signaling (2007, neutral): “Logical networks have until now been used for studying artiﬁcial (random) networks [14] or relatively small gene regulatory networks [15–18].”
Controllability of Boolean control networks via the Perron-Frobenius theory (2012, background): “…network (Kauffman et al. (2003)); the network controlling the segment polarity genes in the fly Drosophila melanogaster (Albert and Othmer (2003); Chaves et al. (2005)); the ABC network determining floral organ cell fate in Arabidopsis (Espinosa-Soto et al. (2004); Chaos et al. (2006)).”