SGD Paper 
Gidvani RD, et al. (2012) A quantitative model of the initiation of DNA replication in Saccharomyces cerevisiae predicts the effects of system perturbations. BMC Syst Biol 6(1):78
Abstract: ABSTRACT: BACKGROUND: Eukaryotic cell proliferation involves DNA replication, a tightly regulated process mediatedby a multitude of protein factors. In budding yeast, the initiation of replication is facilitatedby the heterohexameric origin recognition complex (ORC). ORC binds to specific origins ofreplication and then serves as a scaffold for the recruitment of other factors such as Cdt1,Cdc6, the Mcm2-7 complex, Cdc45 and the Dbf4-Cdc7 kinase complex. While many of themechanisms controlling these associations are well documented, mathematical models areneeded to explore the network's dynamic behaviour. We have developed an ordinarydifferential equation-based model of the protein-protein interaction network describingreplication initiation. RESULTS: The model was validated against quantified levels of protein factors over a range of cell cycletimepoints. Using chromatin extracts from synchronized Saccharomyces cerevisiae cellcultures, we were able to monitor the in vivo fluctuations of several of the aforementionedproteins, with additional data obtained from the literature. The model behaviour conforms toperturbation trials previously reported in the literature, and accurately predicts the results ofour own knockdown experiments. Furthermore, we successfully incorporated our replicationinitiation model into an established model of the entire yeast cell cycle, thus providing acomprehensive description of these processes. CONCLUSIONS: This study establishes a robust model of the processes driving DNA replication initiation. Themodel was validated against observed cell concentrations of the driving factors, andcharacterizes the interactions between factors implicated in eukaryotic DNA replication.Finally, this model can serve as a guide in efforts to generate a comprehensive model of themammalian cell cycle in order to explore cancer-related phenotypes.
| Status: Epub ahead of print | Type: Journal Article | PubMed ID: 22738223 |
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