Dataset Information

Swat2004_Mammalian_G1_S_Transition

ABSTRACT: This is the extended model described the article: Bifurcation analysis of the regulatory modules of the mammalian G1/S transition. Swat M, Kel A, Herzel H. Bioinformatics 2004 Jul 10;20(10):1506-11. PMID: 15231543 , doi: 10.1093/bioinformatics/bth110 Abstract: MOTIVATION: Mathematical models of the cell cycle can contribute to an understanding of its basic mechanisms. Modern simulation tools make the analysis of key components and their interactions very effective. This paper focuses on the role of small modules and feedbacks in the gene-protein network governing the G1/S transition in mammalian cells. Mutations in this network may lead to uncontrolled cell proliferation. Bifurcation analysis helps to identify the key components of this extremely complex interaction network. RESULTS: We identify various positive and negative feedback loops in the network controlling the G1/S transition. It is shown that the positive feedback regulation of E2F1 and a double activator-inhibitor module can lead to bistability. Extensions of the core module preserve the essential features such as bistability. The complete model exhibits a transcritical bifurcation in addition to bistability. We relate these bifurcations to the cell cycle checkpoint and the G1/S phase transition point. Thus, core modules can explain major features of the complex G1/S network and have a robust decision taking function. This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information. In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.. To cite BioModels Database, please use Le Novère N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

SUBMITTER: Nicolas Le Novère

PROVIDER: BIOMD0000000228 | BioModels | 2009-08-17

REPOSITORIES: BioModels

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Bifurcation analysis of the regulatory modules of the mammalian G1/S transition.

Swat Maciej M Kel Alexander A Herzel Hanspeter H

Bioinformatics (Oxford, England) 20040701 10

<h4>Motivation</h4>Mathematical models of the cell cycle can contribute to an understanding of its basic mechanisms. Modern simulation tools make the analysis of key components and their interactions very effective. This paper focuses on the role of small modules and feedbacks in the gene-protein network governing the G1/S transition in mammalian cells. Mutations in this network may lead to uncontrolled cell proliferation. Bifurcation analysis helps to identify the key components of this extreme ...[more]

PMID: 15231543

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Project description:Not many models of mammalian cell cycle system exist due to its complexity. Some models are too complex and hard to understand, while some others are too simple and not comprehensive enough. Moreover, some essential aspects, such as the response of G1-S and G2-M checkpoints to DNA damage as well as the growth factor signalling, have not been investigated from a systems point of view in current mammalian cell cycle models. To address these issues, we bring a holistic perspective to cell cycle by mathematically modelling it as a complex system consisting of important sub-systems that interact with each other. This retains the functionality of the system and provides a clearer interpretation to the processes within it while reducing the complexity in comprehending these processes. To achieve this, we first update a published ODE mathematical model of cell cycle with current knowledge. Then the part of the mathematical model relevant to each sub-system is shown separately in conjunction with a diagram of the sub-system as part of this representation. The model sub-systems are Growth Factor, DNA damage, G1-S, and G2-M checkpoint signalling. To further simplify the model and better explore the function of sub-systems, they are further divided into modules. Here we also add important new modules of: chk-related rapid cell cycle arrest, p53 modules expanded to seamlessly integrate with the rapid arrest module, Tyrosine phosphatase modules that activate Cyc_Cdk complexes and play a crucial role in rapid and delay arrest at both G1-S and G2-M, Tyrosine Kinase module that is important for inactivating nuclear transport of CycB_cdk1 through Wee1 to resist M phase entry, Plk1-Related module that is crucial in activating Tyrosine phosphatases and inactivating Tyrosine kinase, and APC-Related module to show steps in CycB degradation. This multi-level systems approach incorporating all known aspects of cell cycle allowed us to (i) study, through dynamic simulation of an ODE model, comprehensive details of cell cycle dynamics under normal and DNA damage conditions revealing the role and value of the added new modules and elements, (ii) assess, through a global sensitivity analysis, the most influential sub-systems, modules and parameters on system response, such as G1-S and G2-M transitions, and (iii) probe deeply into the relationship between DNA damage and cell cycle progression and test the biological evidence that G1-S is relatively inefficient in arresting damaged cells compared to G2-M checkpoint. To perform sensitivity analysis, Self-Organizing Map with Correlation Coefficient Analysis (SOMCCA) is developed which shows that Growth Factor and G1-S Checkpoint sub-systems and 13 parameters in the modules within them are crucial for G1-S and G2-M transitions. To study the relative efficiency of DNA damage checkpoints, a Checkpoint Efficiency Evaluator (CEE) is developed based on perturbation studies and statistical Type II error. Accordingly, cell cycle is about 96% efficient in arresting damaged cells with G2-M checkpoint being more efficient than G1-S. Further, both checkpoint systems are near perfect (98.6%) in passing healthy cells. Thus this study has shown the efficacy of the proposed systems approach to gain a better understanding of different aspects of mammalian cell cycle system separately and as an integrated system that will also be useful in investigating targeted therapy in future cancer treatments.

Project description:Genome-wide association studies (GWASs) for bone mineral density (BMD), one of the most significant predictors of osteoporotic fracture, have identified over 1100 independent associations; however, few of the causal genes have been identified. Recently, the “omnigenic” model of the genetic architecture of complex traits proposed two general categories of causal genes, core and peripheral. Core genes play a direct role in regulating traits; thus, their identification is key to revealing critical regulators and potential therapeutic targets. Here, we identified a co-expression module enriched for genes exhibiting properties consistent with core genes for BMD by analyzing GWAS data through the lens of a cell-type and timepoint-specific gene co-expression network for mineralizing osteoblasts. We identified multiple co-expression modules enriched for genes implicated by BMD GWAS and prioritized modules based on their enrichment for genes with core-like properties. Only one module, the purple module, was enriched for genes correlated with in vitro mineralization (r = 0.49; FDR = 0.012), with known roles in skeletal development (P <2.2 x 10-16), that when perturbed produce a bone phenotype in mice (OR = 4.1; P = 2.14 x 10-9), and are monogenic bone disease genes in humans (OR=21.3; P=6.94 x 10-9). Furthermore, the purple module contained genes from two distinct transcriptional profiles with regards to osteoblast differentiation, one of which, termed the late differentiation cluster (LDC), was more highly enriched for genes with core-like properties. Within the LDC, we found that the most highly connected genes were more likely to overlap a BMD GWAS association and associations that contained LDC genes overlapped enhancers and promoters in osteoblasts. Finally, we identified four LDC genes (B4GALNT3, CADM1, DOCK9, and GPR133) with colocalizing expression quantitative trait loci (eQTL) and altered BMD in mouse knockouts. Our network-based approach identified a “core” module for BMD and has provided a resource for expanding our understanding of the genetics of bone mass.

Dataset Information

Swat2004_Mammalian_G1_S_Transition

Publications

Bifurcation analysis of the regulatory modules of the mammalian G1/S transition.

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