A Theoretical Model for the Cell Cycle and Drug Induced Cell Cycle Arrest of FUCCI Systems with Cell-to-Cell Variation during Mitosis

Interdependency of estradiol-mediated ERα activation and subsequent PR and GREB1 induction to control cell cycle progression

Various groups of chemicals that we encounter in every-day life are known to disrupt the endocrine system, such as estrogen mimics that can disturb normal cellular development and homeostasis. To understand the effect of estrogen on intracellular protein dynamics and how this relates to cell proliferation, we aimed to develop a quantitative description of transcription factor complexes and their regulation of cell cycle progression in response to estrogenic stimulation. We designed a mathematical model that describes the dynamics of three proteins, GREB1, PR and TFF1, that are transcriptionally activated upon binding of 17β-estradiol (E2) to estrogen receptor alpha (ERα). Calibration of this model to imaging data monitoring the expression dynamics of these proteins in MCF7 cells suggests that transcriptional activation of GREB1 and PR depends on the association of the E2-ERα complex with both GREB1 and PR. We subsequently combined this ER signaling model with a previously published cell cycle model and compared this to quantification of cell cycle durations in MCF7 cells following nuclei tracking based on images segmented with deep neural networks. The resulting model predicts the effect of GREB1 and PR knockdown on cell cycle progression, thus providing mechanistic insight in the molecular interactions between ERα-regulated proteins and their relation to cell cycle progression. Our findings form a valuable basis to further investigate the pharmacodynamics of endocrine disrupting chemicals and their influence on cellular behavior.

A comprehensive review of computational cell cycle models in guiding cancer treatment strategies

This article reviews the current knowledge and recent advancements in computational modeling of the cell cycle. It offers a comparative analysis of various modeling paradigms, highlighting their unique strengths, limitations, and applications. Specifically, the article compares deterministic and stochastic models, single-cell versus population models, and mechanistic versus abstract models. This detailed analysis helps determine the most suitable modeling framework for various research needs. Additionally, the discussion extends to the utilization of these computational models to illuminate cell cycle dynamics, with a particular focus on cell cycle viability, crosstalk with signaling pathways, tumor microenvironment, DNA replication, and repair mechanisms, underscoring their critical roles in tumor progression and the optimization of cancer therapies. By applying these models to crucial aspects of cancer therapy planning for better outcomes, including drug efficacy quantification, drug discovery, drug resistance analysis, and dose optimization, the review highlights the significant potential of computational insights in enhancing the precision and effectiveness of cancer treatments. This emphasis on the intricate relationship between computational modeling and therapeutic strategy development underscores the pivotal role of advanced modeling techniques in navigating the complexities of cell cycle dynamics and their implications for cancer therapy.

Merits and Demerits of ODE Modeling of Physicochemical Systems for Numerical Simulations

In comparison with the first-principles calculations mostly using partial differential equations (PDEs), numerical simulations with modeling by ordinary differential equations (ODEs) are sometimes superior in that they are computationally more economical and that important factors are more easily traced. However, a demerit of ODE modeling is the need of model validation through comparison with experimental data or results of the first-principles calculations. In the present review, examples of ODE modeling are reviewed such as sonochemical reactions inside a cavitation bubble, oriented attachment of nanocrystals, dynamic response of flexoelectric polarization, ultrasound-assisted sintering, and dynamics of a gas parcel in a thermoacoustic engine.

Interdomain exchange and the flip-flop of cholesterol in ternary component lipid membranes and their effects on heterogeneous cholesterol diffusion

Cell membranes are heterogeneous with a variety of lipids, cholesterol, and proteins and are composed of domains of different compositions. Such heterogeneous environments make the transport of cholesterol complicated: cholesterol not only diffuses within a particular domain but also travels between domains. Cholesterol also flip-flops between upper and lower leaflets such that cholesterol may reside both within leaflets and in the central region between two leaflets. How the presence of multiple domains and the interdomain exchange of cholesterol would affect the cholesterol transport, however, remains elusive. In this study, therefore, we perform molecular dynamics simulations up to 100μs for ternary component lipid membranes, which consist of saturated lipids (dipalmitoylphosphatidylcholine, DPPC), unsaturated lipids (dilinoleylphosphatidylcholine, DIPC), and cholesterol. The ternary component membranes in our simulations form two domains readily: DPPC and DIPC domains. We find that the diffusion of cholesterol molecules is much more heterogeneous and non-Gaussian than expected for binary component lipid membranes of lipids and cholesterol. The non-Gaussian parameter of the cholesterol molecules is about four times larger in the ternary component lipid membranes than in the binary component lipid membranes. Such non-Gaussian and heterogeneous transport of cholesterol arises from the interplay among the interdomain kinetics, the different diffusivity of cholesterol in different domains, and the flip-flop of cholesterol. This suggests that in cell membranes that consist of various domains and proteins, the cholesterol transport can be very heterogeneous. We also find that the mechanism of the interdomain exchange differs for different domains: cholesterol tends to exit the DIPC domain along the central region of the membrane for the DIPC-to-DPPC transition, while the cholesterol is likely to exit the DPPC domain within the membrane leaflet for the DPPC-to-DIPC transition. Also, the interdomain exchange kinetics of cholesterol for the DPPC-to-DIPC transition is up to 7.9 times slower than the DIPC-to-DPPC transition.

Cinobufagin induces cell cycle arrest at the S phase and promotes apoptosis in nasopharyngeal carcinoma cells

Emerging evidence suggests that cinobufagin, an active ingredient in Venenum Bufonis, inhibits cell proliferation in several tumor cells. However, the anti-tumor effect of cinobufagin on nasopharyngeal carcinoma and the underlying molecular mechanisms are still unclear. In this study, we found that cinobufagin significantly inhibits the proliferation of nasopharyngeal carcinoma HK-1 cells. Further analyses demonstrated that cinobufagin induces cell cycle arrest at the S phase in HK-1 cells through downregulating the levels of CDK2 and cyclin E. Moreover, cinobufagin significantly downregulates the protein level of Bcl-2 and upregulates the levels of Bax, subsequently increasing the levels of cytoplasmic cytochrome c, Apaf-1, cleaved PARP1, cleaved caspase-3, and cleaved caspase-9, leading to HK-1 apoptosis. Furthermore, we found that cinobufagin significantly increases ROS levels and decreases the mitochondrial membrane potential in HK-1 cells. Collectively, these data imply that cinobufagin induces cell cycle arrest at the S phase and induces apoptosis through increasing ROS levels, thereby inhibiting cell proliferation in HK-1 cells. Therefore, cinobufagin is a promising bioactive agent that may contribute to the development of treatment strategies of nasopharyngeal carcinoma.

Mathematical models incorporating a multi-stage cell cycle replicate normally-hidden inherent synchronization in cell proliferation

We present a suite of experimental data showing that cell proliferation assays, prepared using standard methods thought to produce asynchronous cell populations, persistently exhibit inherent synchronization. Our experiments use fluorescent cell cycle indicators to reveal the normally hidden cell synchronization, by highlighting oscillatory subpopulations within the total cell population. These oscillatory subpopulations would never be observed without these cell cycle indicators. On the other hand, our experimental data show that the total cell population appears to grow exponentially, as in an asynchronous population. We reconcile these seemingly inconsistent observations by employing a multi-stage mathematical model of cell proliferation that can replicate the oscillatory subpopulations. Our study has important implications for understanding and improving experimental reproducibility. In particular, inherent synchronization may affect the experimental reproducibility of studies aiming to investigate cell cycle-dependent mechanisms, including changes in migration and drug response.