Cell sizes emerge in the model, which predicts that a single CDK-cyclin pair per growth phase suffices for size control in budding yeast, despite the necessity of the cell cycle network around the cyclins to integrate other cues.

It emerges as a combination of the cell cycle , controlling the orderly orchestration of duplication and division, and the individual growth rate, reflecting extra and intracellular physiological conditions.

The cell cycle and the growth rate are coupled, such that proliferation and growth are balanced, avoiding abnormally large or small cells.

First, the cell cycle as well as cellular growth are two fundamental properties that can be found in nearly all forms of life.

Quantitativer understanding the robustness, adaptivity and efficiency of cell cycle dynamics under the influence of noise is a fundamental but difficult question to answer for most eu-karyotic organisms.

Using a simplified budding yeast cell cycle model perturbed by intrinsic noise, we systematically explore these issues from an energy landscape point of view by constructing an energy landscape for the considered system based on large deviation theory.

Analysis shows that the cell cycle trajectory is sharply confined by the ambient energy barrier, and the landscape along this trajectory exhibits a generally flat shape.

Quantitativer understanding the dynamic behavior of the yeast cell cycle process under noise perturbations is a fundamental problem in theoretical biology.

Our results demonstrate that the cell cycle trajectory is sharply confined as a canal bounded by ambient energy barriers, with the landscape adaptively reshaping itself in response to external signals, such as the nutrients improving and the activation of DNA replication checkpoint in our work.

After performing quantitative analysis based on the landscape, We found that along the cell cycle trajectory, the typical

The yeast cell cycle is an important biological process in which a cell reproduces itself through DNA replication and mitosis events, which are intimately related to the checkpoint mechanism [17, 18].

Recent work has revealed the dynamic regulatory mechanisms of the cell cycle, and the cell cycle process is now considered a series of irreversible transitions from one state to another [19—21].

In particular we analyze two datasets: the cell cycle dataset in yeast, a common model organism, and a well characterized human leukemia dataset.

Yeast T Fs are cooperative during cell cycle .

We used Loregic to characterize the TF-TF-target logics during the yeast cell cycle (Materials and Methods) and found 4,126 TF-TF-tar-get triplets that are gate-consistent (Fig.

We apply Loregic to study regulatory factors (TFs and miRNAs) in the yeast cell cycle and human cancer datasets.

We apply Loregic to find the logic operations that characterize the FFLs from a genome-Wide perspective in both the yeast cell cycle and human leukemia cancer datasets.

Transcripts are characterized by decreased halflife: this is considered peculiar of genes involved in regulatory systems with high turnover; consistently, their function affects biological processes such as cell cycle regulation or stress response.

More, we show that ORFs with peak in 3’ UTR share a number of common functions in biological processes such as cell cycle regulation or stress response.

The second one is their enrichment in genes related to cell cycle regulation, apoptosis or similar processes involved in cancer development [65].

Coherently with the above described functional aspects, flexibility peaks in yeast occur in ORFs involved in cell cycle control or stress response, where flexible sequences seemed to play a regulatory role in gene expression.

The outcomes for Biological Process GO terms (visualized as treemap in supporting 81 file, figure 6, top) point out the presence of ORFs with role in cell cycle , phosphorus/ organic cyclic compound/ nitrogen compound metabolism, phosphorylation reproduction, growth, response to acid, signaling.

The 175 ORFs include genes expressing key components of cell cycle progression and regulation: TUBZ and TUB3 encoding a and fl tubulins, CLB4 and PH 080 encoding cyclins, CDC53 and APC9 encoding respectively the cullin structural protein of SCF complexes and a subunit of the Ana-phase-Promoting Complex/Cyclosome; moreover, AME] , RAD24, RAD59 and SWEI involved in checkpoint maintenance, the F U83, DI G2 and SLT2 encoding MAP-kinases and their regulator BMH1 encoding the major isoform of 14-3-3 proteins.