The effective application of a discrete transition model to explore cell-cycle regulation in yeast

Abstract

BackgroundBench biologists often do not take part in the development of computational models for their systems, and therefore, they frequently employ them as “black-boxes”. Our aim was to construct and test a model that does not depend on the availability of quantitative data, and can be directly used without a need for intensive computational background.ResultsWe present a discrete transition model. We used cell-cycle in budding yeast as a paradigm for a complex network, demonstrating phenomena such as sequential protein expression and activity, and cell-cycle oscillation. The structure of the network was validated by its response to computational perturbations such as mutations, and its response to mating-pheromone or nitrogen depletion. The model has a strong predicative capability, demonstrating how the activity of a specific transcription factor, Hcm1, is regulated, and what determines commitment of cells to enter and complete the cell-cycle.ConclusionThe model presented herein is intuitive, yet is expressive enough to elucidate the intrinsic structure and qualitative behavior of large and complex regulatory networks. Moreover our model allowed us to examine multiple hypotheses in a simple and intuitive manner, giving rise to testable predictions. This methodology can be easily integrated as a useful approach for the study of networks, enriching experimental biology with computational insights.

Gene Expression Changes and Anti-proliferative Effect of Noni (Morinda Citrifolia) Fruit Extract Analysed by Real Time-PCR

“…Some gene expression changes lead to the sensitive growth phenotypic of S.cerevisiae on the medium (Hermansyah, et al, 2009). Some genes such as SWI6, CDC6,CDC7,WHI5,CDC20,YOX1,YHP1,TAH11,SIC1,MCM1,MBP1,FUS3,FAR1, and DBF4 involved in cell cycle regulation in S.cerevisiae (Rubinstein, Hazan, Chor, Pinter, & Kassir, 2013;http://www.yeastgenome.org).…”

Advances and challenges in logical modeling of cell cycle regulation: perspective for multi-scale, integrative yeast cell models

“…(2013) and Rubinstein et al. (2013), have their foundations in the work of Li, Fauré or Irons. Todd and Helikar (2012), while adapting their model from the one of Irons (Irons 2009), investigated the cell cycle logical network as a Markov chain where stochasticity is not interpreted as noise but as continuous activity level.…”

“…However, this model is significantly larger, containing 65 nodes that represent RNA, proteins and cellular events (Rubinstein et al. 2013). The authors show that when the model starts at an excited G1 state the system falls into an attractor that, as in the Li model, mimics the activation levels of the nodes.…”

“…Furthermore, the model is able to reproduce the behavior of several mutants and the cell cycle response to nitrogen depletion (Rubinstein et al. 2013). Strikingly, the model is able to make detailed predictions.…”

“…The model predicts that if Cln3/Cdk1 activates Hcm1, there is a premature decline in the transcription of the CLB2 cyclin gene during the S phase (Rubinstein et al. 2013). Contrarily, if Cln1/Cdk1 or Clb5/Cdk1 activates Hcm1, the basic behavior of the cell cycle is observed; a simulation of the response to pheromone treatment suggested that Cln1/Cdk1 is more likely the activator of Hcm1.…”