Antagonism and bistability in protein interaction networks

Sabouri-Ghomi, Mohsen; Ciliberto, Andrea; Kar, Sandip; Novák, Béla; Tyson, John J.

doi:10.1016/j.jtbi.2007.09.001

Cited by 69 publications

(62 citation statements)

References 36 publications

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“…[35][36][37] Recent research has justified the extension of Gillespie's algorithm to Michaelis-Menten kinetics under some circumstances. [38][39][40][41] On the contrary, other recent research 42,43 suggests that the deterministic cell cycle models developed by Tyson 45 and we have done the same for the Chen-2004 model. We consider this stochastic interpretation of the Chen-2004 model as a "first approximation" to effects of molecular noise in the cell cycle control system of budding yeast.…”

Section: ©2 0 1 1 L a N D E S B I O S C I E N C E D O N O T D I S Tmentioning

confidence: 75%

“…16 This approach allows for straightforward conversion of a deterministic model to a stochastic version, 45 but it is subject to some uncertainties. 42 As a first step to explore the stochastic phenotype of CLB2-dbΔ clb5Δ mutant cells, we opted for the simplified approach. More accurate and reliable models, based on multisite phosphorylation mechanisms that are perfectly suitable for stochastic simulation by Gillespie's algorithm, are currently under development.…”

Section: ©2 0 1 1 L a N D E S B I O S C I E N C E D O N O T D I S Tmentioning

confidence: 99%

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Stochastic exit from mitosis in budding yeast

et al. 2011

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Section: ©2 0 1 1 L a N D E S B I O S C I E N C E D O N O T D I S Tmentioning

confidence: 75%

Section: ©2 0 1 1 L a N D E S B I O S C I E N C E D O N O T D I S Tmentioning

confidence: 99%

Stochastic exit from mitosis in budding yeast

et al. 2011

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“…However, applying Gillespie's SSA to phenomenological rate laws may possibly generate incorrect stochastic results. 17 Thus, a model based fully on mass-action kinetics for all reactions was developed in Kar et al 18 by "unpacking" the reactions with phenomenological rate laws in the Tyson-Novak three-variable model into sets of elementary stochastic reactions. In the process, mRNA variables for X, Y, Z, and other helper proteins were introduced into the network.…”

Section: Introductionmentioning

confidence: 99%

Hybrid modeling and simulation of stochastic effects on progression through the eukaryotic cell cycle

Liu

Yang

et al. 2012

The Journal of Chemical Physics

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The eukaryotic cell cycle is regulated by a complicated chemical reaction network. Although many deterministic models have been proposed, stochastic models are desired to capture noise in the cell resulting from low numbers of critical species. However, converting a deterministic model into one that accurately captures stochastic effects can result in a complex model that is hard to build and expensive to simulate. In this paper, we first apply a hybrid (mixed deterministic and stochastic) simulation method to such a stochastic model. With proper partitioning of reactions between deterministic and stochastic simulation methods, the hybrid method generates the same primary characteristics and the same level of noise as Gillespie's stochastic simulation algorithm, but with better efficiency. By studying the results generated by various partitionings of reactions, we developed a new strategy for hybrid stochastic modeling of the cell cycle. The new approach is not limited to using mass-action rate laws. Numerical experiments demonstrate that our approach is consistent with characteristics of noisy cell cycle progression, and yields cell cycle statistics in accord with experimental observations.

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“…A bistable motif is defined as a regulatory network motif that can reside stably in either of its two steady states [20], [21]. We identified 6 distinct classes of bistable motifs of size 3, as well as 22 distinct classes of bistable motifs of size 4.…”

Section: Resultsmentioning

confidence: 99%

Evolutionary Conservation and Network Structure Characterize Genes of Phenotypic Relevance for Mitosis in Human

2012

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The impact of gene silencing on cellular phenotypes is difficult to establish due to the complexity of interactions in the associated biological processes and pathways. A recent genome-wide RNA knock-down study both identified and phenotypically characterized a set of important genes for the cell cycle in HeLa cells. Here, we combine a molecular interaction network analysis, based on physical and functional protein interactions, in conjunction with evolutionary information, to elucidate the common biological and topological properties of these key genes. Our results show that these genes tend to be conserved with their corresponding protein interactions across several species and are key constituents of the evolutionary conserved molecular interaction network. Moreover, a group of bistable network motifs is found to be conserved within this network, which are likely to influence the network stability and therefore the robustness of cellular functioning. They form a cluster, which displays functional homogeneity and is significantly enriched in genes phenotypically relevant for mitosis. Additional results reveal a relationship between specific cellular processes and the phenotypic outcomes induced by gene silencing. This study introduces new ideas regarding the relationship between genotype and phenotype in the context of the cell cycle. We show that the analysis of molecular interaction networks can result in the identification of genes relevant to cellular processes, which is a promising avenue for future research.

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Antagonism and bistability in protein interaction networks

Cited by 69 publications

References 36 publications

Stochastic exit from mitosis in budding yeast

Stochastic exit from mitosis in budding yeast

Hybrid modeling and simulation of stochastic effects on progression through the eukaryotic cell cycle

Evolutionary Conservation and Network Structure Characterize Genes of Phenotypic Relevance for Mitosis in Human

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