The interplay between chromosome stability and cell cycle control explored through gene-gene interaction and...

Area of Science:

• Genetics
• Computational Biology
• Cell Biology

Background:

• Chromosome stability models are typically qualitative, limiting precise quantitative analysis.
• Understanding the molecular mechanisms of DNA repair, synthesis, and cell division is crucial for modeling chromosome stability.

Purpose of the Study:

• To explore how laboratory experiments, quantitative simulation, and seriation algorithms can inform quantitative models of chromosome stability.
• To identify genes causing chromosome instability and elucidate their underlying molecular mechanisms.

Main Methods:

• Laboratory experiments in Saccharomyces cerevisiae identified 19 over-expressed genes causing chromosome instability.
• Genetic interactions between these genes and known instability mutations were analyzed.
• Quantitative simulations of cell cycle models predicted consequences of genetic interactions.
• A seriation algorithm analyzed the genetic interaction matrix to reveal cyclical patterns related to cell cycle phases.

Main Results:

• Identified 19 genes that induce chromosome instability upon over-expression in yeast.
• Quantitative simulations suggested cell-cycle perturbations caused by these instability genes.
• A seriation algorithm confirmed cell-cycle involvement by revealing an underlying cyclical pattern in the genetic interaction matrix.
• The identified cyclical pattern accurately reflects cell cycle phase events.

Conclusions:

• Integrated laboratory experiments, quantitative simulation, and seriation algorithms to develop quantitative chromosome stability models.
• Demonstrated that chromosome instability genes perturb the cell cycle.
• Confirmed the utility of seriation algorithms in uncovering cyclical biological processes.
• Linked identified molecular mechanisms to established molecular interaction maps, enhancing model comprehensiveness.