The yeast cell-cycle network is robustly designed.

By Fangting Li, T. Long, Ying Lu, Ouyang Qi, Chao Tang. Proceedings of the National Academy of Sciences of the United States of America, 2003

1,104 citations recorded by the source corpus.

The interactions between proteins, DNA, and RNA in living cells constitute molecular networks that govern various cellular functions. To investigate the global dynamical properties and stabilities of such networks, we studied the cell-cycle regulatory network of the budding yeast. With the use of a simple dynamical model, it was demonstrated that the cell-cycle network is extremely stable and robust for its function. The biological stationary state, the G1 state, is a global attractor of the dynamics. The biological pathway, the cell-cycle sequence of protein states, is a globally attracting trajectory of the dynamics. These properties are largely preserved with respect to small perturbations to the network. These results suggest that cellular regulatory networks are robustly designed for their functions.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/465d4a4e703fe73427273e1e9afa259a06017148

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Citations in the corpus

Logic-Based Models for the Analysis of Cell Signaling Networks (2010, neutral): “Boolean discrete time steps binary (13, 15-17, 19, 24, 25, 33); (20, 42, 43, 51, 52)”
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].”
On simplicity and complexity in the brave new world of large-scale neuroscience. (2015, methodology): “For example [75] derived a single boolean network model of the yeast cell-cycle control network, while [76] developed methods to count and sample from the space of all networks that realize the yeast cell-cycle.”
Analyzing and modeling real-world phenomena with complex networks: a survey of applications (2007, background): “[398] who observed that the cell-cycle of the regulatory network in Yeast was extremely stable and robust.”
Interactome: gateway into systems biology. (2005, background): “Moreover, proteins of known function are found in interaction clusters that are of completely different function, suggesting potentially new functions for these ‘known’ proteins (48,49,130,131).”
Controllability of Boolean control networks via the Perron-Frobenius theory (2012, methodology): “Our work is motivated by the variational analysis of continuous-time switched systems (see Margaliot (2006); Margaliot and Branicky (2009); Sharon and Margaliot (2007); Margaliot and Liberzon (2006)).”
Scale-free networks in cell biology (2005, background): “(McCraith et al., 2000), prokaryotes such as H. pylori (Rain et al., 2001) and eukaryotes such as S. cerevisiae (Gavin et al., 2002; Ho et al., 2002; Ito et al., 2001; Uetz et al., 2000), C. elegans (Li et al., 2004b) and D. melanogaster (Giot et al., 2003). The current versions of protein interacti”
Colloquium: Criticality and dynamical scaling in living systems (2017, background): “18 ation, differentiation, etc.) can be viewed as programs encoded in the dynamical attractors of gene regulatory networks (Albert and Othmer, 2003; Espinosa-Soto et al., 2004; Huang et al., 2005; Li et al., 2004).”
Modelling and analysis of gene regulatory networks (2008, background): “To study the dynamics of cell-cycle regulation in yeast, Li et al”
Boolean Network Model Predicts Cell Cycle Sequence of Fission Yeast (2007, background): “For example, a class of discrete dynamical systems with binary states, mathematically similar to models used in artificial neural networks, has recently proven to predict specific sequence patterns of protein and gene activity as observed in living cells [12,13].”
Executable cell biology (2007, background): “Despite this clearly simplified view of biological networks, several examples from models of genetic regulatory networks show that Boolean approaches give meaningful biological information 32–34 .”
Modeling the cell cycle: why do certain circuits oscillate? (2011, positive): “The explicit time delays, like the discrete time steps in the Boolean model (Figure 2), help to keep the activities of CDK1 and APC from settling into a stable steady state.”
BoolNet - an R package for generation, reconstruction and analysis of Boolean networks (2010, methodology): “As they comprise the states in which a gene-regulatory network resides most of the time, they carry strong biological implications and often can be linked to phenotypes (Kauffman, 1993; Li et al., 2004).”
Nonequilibrium physics in biology (2019, methodology): “mammalian cells, several scenarios have been proposed, including treating the cell cycle as a discrete attractor (Li et al., 2004), using bifurcations (Chen et al.”