Cell cycle commitment in budding yeast emerges from the cooperation of multiple bistable switches

The start-transition (START) in the G1 phase marks the point in the cell cycle at which a yeast cell initiates a new round of cell division. Once made, this decision is irreversible and the cell is committed to progressing through the entire cell cycle, irrespective of arrest signals such as pheromone. How commitment emerges from the underlying molecular interaction network is poorly understood. Here, we perform a dynamical systems analysis of an established cell cycle model, which has never been analysed from a commitment perspective. We show that the irreversibility of the START transition and subsequent commitment can be consistently explained in terms of the interplay of multiple bistable molecular switches. By applying an existing mathematical model to a novel problem and by expanding the model in a self-consistent manner, we achieve several goals: we bring together a large number of experimental findings into a coherent theoretical framework; we increase the scope and the applicability of the original model; we give a systems level explanation of how the START transition and the cell cycle commitment arise from the dynamical features of the underlying molecular interaction network; and we make clear, experimentally testable predictions.

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How Do Drug-Induced Topoisomerase I-DNA Lesions Signal to the Molecular Interaction Network that Regulates Cell Cycle Checkpoints, DNA Replication, and DNA Repair?

Cell Biochemistry and Biophysics ◽

10.1385/cbb:33:2:175 ◽

2000 ◽

Vol 33 (2) ◽

pp. 175-180 ◽

Cited By ~ 22

Author(s):

Kurt W. Kohn ◽

Rhong-Guang Shao ◽

Yves Pommier

Keyword(s):

Cell Cycle ◽

Dna Repair ◽

Dna Replication ◽

Molecular Interaction ◽

Topoisomerase I ◽

Interaction Network ◽

Dna Lesions ◽

Cell Cycle Checkpoints ◽

Molecular Interaction Network ◽

Drug Induced

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Using a Machine Learning Approach to Identify Key Prognostic Molecules for Esophageal Squamous Cell Carcinoma

10.21203/rs.3.rs-310517/v1 ◽

2021 ◽

Author(s):

Meng-Xiang Li ◽

Xiao-Meng Sun ◽

Wei-Gang Cheng ◽

Hao-Jie Ruan ◽

Ke Liu ◽

...

Keyword(s):

Machine Learning ◽

Squamous Cell Carcinoma ◽

Feature Selection ◽

Esophageal Squamous Cell Carcinoma ◽

Molecular Interaction ◽

Prognostic Biomarker ◽

Interaction Network ◽

Machine Learning Algorithms ◽

Functional Modules ◽

Molecular Interaction Network

Abstract ObjectiveA plethora of prognostic biomarkers for esophageal squamous cell carcinoma (ESCC) that have hitherto been reported are challenged with low reproducibility due to high molecular heterogeneity of ESCC. The purpose of this study is to identify the optimal biomarkers for ESCC using machine learning algorithms.MethodsBiomarkers related to clinical survival, recurrence or therapeutic response of patients with ESCC were determined through literature database searching. Forty-eight biomarkers linked to prognosis of ESCC were used to construct a molecular interaction network based on NetBox and then to identify the functional modules. Publicably available mRNA transcriptome data of ESCC downloaded from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) datasets included GSE53625 and TCGA-ESCC. Five machine learning algorithms, including logical regression (LR), support vector machine (SVM), artificial neural network (ANN), random forest (RF) and XGBoost, were used to develop classifiers for prognostic classification for feature selection. The area under ROC curve (AUC) was used to evaluate the performance of the prognostic classifiers. The importances of these 17 molecules were ranked by their occurrence frequencies in the prognostic classifiers. Kaplan-Meier survival analysis and log-rank test were performed to determine the statistical significance of overall survival.ResultsA total of 48 clinical proven molecules associated with ESCC progression were used to construct a molecular interaction network with 3 functional modules comprising 17 component molecules. The 131071 prognostic classifiers using these 17 molecules were built for each machine learning algorithm. Using the occurrence frequencies in the prognostic classifiers with AUCs greater than the mean value of all 131,071 AUCs to rank importances of these 17 molecules, stratifin encoded by SFN was identified as the optimal prognostic biomarker for ESCC, whose performance was further validated in another 2 independent cohorts.ConclusionThe occurrence frequencies across various feature selection approaches reflect the degree of clinical importance and stratifin is an optimal prognostic biomarker for ESCC.

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