A Data-Driven, Mathematical Model of Mammalian Cell Cycle Regulation

Abstract

Few of >150 published cell cycle modeling efforts use significant levels of data for tuning and validation. This reflects the difficultly to generate correlated quantitative data, and it points out a critical uncertainty in modeling efforts. To develop a data-driven model of cell cycle regulation, we used contiguous, dynamic measurements over two time scales (minutes and hours) calculated from static multiparametric cytometry data. The approach provided expression profiles of cyclin A2, cyclin B1, and phospho-S10-histone H3. The model was built by integrating and modifying two previously published models such that the model outputs for cyclins A and B fit cyclin expression measurements and the activation of B cyclin/Cdk1 coincided with phosphorylation of histone H3. The model depends on Cdh1-regulated cyclin degradation during G1, regulation of B cyclin/Cdk1 activity by cyclin A/Cdk via Wee1, and transcriptional control of the mitotic cyclins that reflects some of the current literature. We introduced autocatalytic transcription of E2F, E2F regulated transcription of cyclin B, Cdc20/Cdh1 mediated E2F degradation, enhanced transcription of mitotic cyclins during late S/early G2 phase, and the sustained synthesis of cyclin B during mitosis. These features produced a model with good correlation between state variable output and real measurements. Since the method of data generation is extensible, this model can be continually modified based on new correlated, quantitative data.

Initiation and termination of DNA replication during S phase in relation to cyclins D1, E and A, p21WAF1, Cdt1 and the p12 subunit of DNA polymerase δ revealed in individual cells by cytometry

“…As mentioned, the correlation between DNA replication and expression of the cyclins D1, E and A is consistent with cytometric analysis of these cyclin proteins based on analysis of BrdU incorporation and SBIP methodology presented in the prior reports [ 74 - 76 ]. The presented data conform also to the assessments of the cell cycle kinetics based on multiparameter analysis of cyclins expression with respect to other proteins of the cell cycle machinery as reported by Jacobberger and his colleagues [ 77 - 79 ]. Their studies however were based on static analysis of presentation of these proteins vis-a-vis cellular DNA content (cell cycle phase) and did not include the kinetic information pertaining initiation and completion of DNA replication, or the rate of DNA replication that can be inferred from the degree of EdU incorporation during the pulse labeling.…”

Mathematical modelling of cortical neurogenesis reveals that the founder population does not necessarily scale with neuronal output

“…A vast literature for cell cycle models is available (Weis et al 2014), and different modelling approaches can be adopted for the description of the cell cycle at different scales of complexity. We are interested in the cell population dynamics, a higher scale level than the fine-grained study of progression through cell cycle phases and the molecular processes involved.…”

A review of computational models of mammalian cell cycle

“…The goal in this paper was to review computational modelling approaches for mammalian cell cycle to see how models have facilitated our understanding of it. We briefly explained the mechanism of mammalian cell cycle regulation system by highlighting the important effect of Cyclin/Cdks as main controllers as well as their (Obeyesekere et al, 1997) G1-S (Basse et al, 2003) G1-S-G2-M (Pfeuty et al, 2008) G1-S (Gauthier & Pohl, 2011) G1-S-G2-M (Thron, 1997) G2-M (Bai et al, 2003) G1-S (Tashima et al, 2008) G1-S (Zhang et al, 2013) G2-M (Kohn, 1998) G1-S G1-S-G2-M (Iwamoto et al, 2008) G1-S (Weis et al, 2014) G1-S-G2-M (Hatzimanikatis et al, 1999) G1-S (Novak & Tyson, 2004) G1-S-G2-M (Gérard & Goldbeter, 2009) G1-S-G2-M (Zhang et al, 2014a) G2-M (Obeyesekere et al, 1999) G1-S (Alarcon et al, 2004) G1-S-G2-M (Hamada et al, 2009) G2-M (Zhang et al, 2014b) G2-M (Aguda & Tang, 1999) G1-S (Swat et al, 2004) G1-S (Alfieri et al, 2009) G1-S Table 4. Stochastic models of mammalian cell cycle.…”