Logical analysis of the budding yeast cell cycle

Abstract

The budding yeast Saccharomyces cerevisiae is a model organism that is commonly used to investigate control of the eukaryotic cell cycle. Moreover, because of the extensive experimental data on wild type and mutant phenotypes, it is also particularly suitable for mathematical modelling and analysis. Here, I present a new Boolean model of the budding yeast cell cycle. This model is consistent with a wide range of wild type and mutant phenotypes and shows remarkable robustness against perturbations, both to reaction times and the states of component genes/proteins. Because of its simple logical nature, the model is suitable for sub-network analysis, which can be used to identify a four node core regulatory circuit underlying cell cycle regulation. Sub-network analysis can also be used to identify key sub-dynamics that are essential for viable cell cycle control, as well as identifying the sub-dynamics that are most variable between different mutants.

Synchronization Design of Boolean Networks Via the Semi-Tensor Product Method

“…Then we can express the BN (19) in its algebraic form as x(t + 1) = Fx(t), where F = δ 8 (8,2,8,6,8,4,7,7). Since this BN contains three cycles {δ 8 (2), δ 8 (2)}, {δ 8 (7), δ 8 (7)}, {δ 8 (4), δ 8 (6), δ 8 (4)}, we have…”

“…Since this BN contains three cycles {δ 8 (2), δ 8 (2)}, {δ 8 (7), δ 8 (7)}, {δ 8 (4), δ 8 (6), δ 8 (4)}, we have…”

Quasi-cyclic codes exhibiting the gene regulatory network of the cell cycle

“…More specifically this includes: (i) the existence of a cyclic attractor, (ii) self-correction, (iii) an ability to "trace" the external factor such as cell mass. An even more interesting question is possibility of constructing an artificial Boolean network that is smaller but more robust than the Boolean networks corresponding to those occurring in nature (e.g., the yeast cell cycle inferred from known gene interactions [7]). Robustness of a network is typically analyzed in presence of cellular noise which is modeled as transient errors [8].…”

“…Transient errors are the result of variability of protein concentrations. Another type of error is gene mutations which is due to production of defective proteins and correspond to permanent errors [7], [9]. Mutations can have harmful effects on genes such as preventing the gene to function properly.…”

“…[15] extended the model and introduced a model with cyclic behavior but with only two stable phases in the cell cycle. The Boolean network model proposed by Irons [7] for the budding yeast contains 18 nodes (14 nodes for genes/proteins incorporate in each cell cycle phase and 4 nodes for transitions). In Irons model, adding extra nodes (dummy nodes) enables the network to show time-delays in activation or degradation of gene expression levels.…”