Kazunari Iwamoto

Mathematical modeling and sensitivity analysis of G1/S phase in the cell cycle including the DNA-damage signal transduction pathway

The cell cycle has checkpoint systems, which control G1/S, G2/M and G0/G1 phase transitions. When a normal cell suffers from DNA-damage, the signal transduction of DNA-damage causes the cell cycle arrest by using the checkpoint systems. Therefore, the elucidation of interaction between the signal transduction of DNA-damage and the checkpoint systems is an important problem. In this study, we constructed a novel mathematical model (proposed model) which integrated G1/S-checkpoint model with a signal transduction of DNA damage model and performed some numerical simulations. The proposed model realized some biological findings of G1/S phase with or without DNA-damage, which suggested that proposed model is biologically appropriate. Moreover, the results of sensitivity analysis of the proposed model indicated the predominant factors of G1/S phase and some factors concerned with the transformation of cells.

Mathematical modeling of cell cycle regulation in response to DNA damage: Exploring mechanisms of cell-fate determination

After DNA damage, cells activate p53, a tumor suppressor gene, and select a cell fate (e.g., DNA repair, cell cycle arrest, or apoptosis). Recently, a p53 oscillatory behavior was observed following DNA damage. However, the relationship between this p53 oscillation and cell-fate selection is unclear. Here, we present a novel model of the DNA damage signaling pathway that includes p53 and whole cell cycle regulation and explore the relationship between p53 oscillation and cell fate selection. The simulation run without DNA damage qualitatively realized experimentally observed data from several cell cycle regulators, indicating that our model was biologically appropriate. Moreover, the comprehensive sensitivity analysis for the proposed model was implemented by changing the values of all kinetic parameters, which revealed that the cell cycle regulation system based on the proposed model has robustness on a fluctuation of reaction rate in each process. Simulations run with four different intensities of DNA damage, i.e. Low-damage, Medium-damage, High-damage, and Excess-damage, realized cell cycle arrest in all cases. Low-damage, Medium-damage, High-damage, and Excess-damage corresponded to the DNA damage caused by 100, 200, 400, and 800 J/m(2) doses of UV-irradiation, respectively, based on expression of p21, which plays a crucial role in cell cycle arrest. In simulations run with High-damage and Excess-damage, the length of the cell cycle arrest was shortened despite the severe DNA damage, and p53 began to oscillate. Cells initiated apoptosis and were killed at 400 and 800 J/m(2) doses of UV-irradiation, corresponding to High-damage and Excess-damage, respectively. Therefore, our model indicated that the oscillatory mode of p53 profoundly affects cell fate selection.

Conversion of graded phosphorylation into switch-like nuclear translocation via autoregulatory mechanisms in ERK signalling

The phosphorylation cascade in the extracellular signal-regulated kinase (ERK) pathway is a versatile reaction network motif that can potentially act as a switch, oscillator or memory. Nevertheless, there is accumulating evidence that the phosphorylation response is mostly linear to extracellular signals in mammalian cells. Here we find that subsequent nuclear translocation gives rise to a switch-like increase in nuclear ERK concentration in response to signal input. The switch-like response disappears in the presence of ERK inhibitor, suggesting the existence of autoregulatory mechanisms for ERK nuclear translocation involved in conversion from a graded to a switch-like response. In vitro reconstruction of ERK nuclear translocation indicates that ERK-mediated phosphorylation of nucleoporins regulates ERK translocation. A mathematical model and knockdown experiments suggest a contribution of nucleoporins to regulation of the ERK nuclear translocation response. Taken together, this study provides evidence that nuclear translocation with autoregulatory mechanisms acts as a switch in ERK signalling.

Sophisticated Framework between Cell Cycle Arrest and Apoptosis Induction Based on p53 Dynamics

The tumor suppressor, p53, regulates several gene expressions that are related to the DNA repair protein, cell cycle arrest and apoptosis induction, which activates the implementation of both cell cycle arrest and induction of apoptosis. However, it is not clear how p53 specifically regulates the implementation of these functions. By applying several well-known kinetic mathematical models, we constructed a novel model that described the influence that DNA damage has on the implementation of both the G2/M phase cell cycle arrest and the intrinsic apoptosis induction via its activation of the p53 synthesis process. The model, which consisted of 32 dependent variables and 115 kinetic parameters, was used to examine interference by DNA damage in the implementation of both G2/M phase cell cycle arrest and intrinsic apoptosis induction. A low DNA damage promoted slightly the synthesis of p53, which showed a sigmoidal behavior with time. In contrast, in the case of a high DNA damage, the p53 showed an oscillation behavior with time. Regardless of the DNA damage level, there were delays in the G2/M progression. The intrinsic apoptosis was only induced in situations where grave DNA damage produced an oscillation of p53. In addition, to wreck the equilibrium between Bcl-2 and Bax the induction of apoptosis required an extreme activation of p53 produced by the oscillation dynamics, and was only implemented after the release of the G2/M phase arrest. When the p53 oscillation is observed, there is possibility that the cell implements the apoptosis induction. Moreover, in contrast to the cell cycle arrest system, the apoptosis induction system is responsible for safeguarding the system that suppresses malignant transformations. The results of these experiments will be useful in the future for elucidating of the dominant factors that determine the cell fate such as normal cell cycles, cell cycle arrest and apoptosis.

Stochasticity of intranuclear biochemical reaction processes controls the final decision of cell fate associated with DNA damage

A massive integrative mathematical model of DNA double-strand break (DSB) generation, DSB repair system, p53 signaling network, and apoptosis induction pathway was constructed to explore the dominant factors of unknown criteria of cell fate decision. In the proposed model, intranuclear reactions were modeled as stochastic processes and cytoplasmic reactions as deterministic processes, and both reaction sets were simulated simultaneously. The simulated results at the single-cell level showed that the model generated several sustained oscillations (pulses) of p53, Mdm2, ATM, and Wip1, and cell-to-cell variability in the number of p53 pulses depended on IR intensity. In cell populations, the model generated damped p53 oscillations, and IR intensity affected the amplitude of the first p53 oscillation. Cells were then subjected to the same IR dose exhibiting apoptosis induction variability. These simulated results are in quantitative agreement with major biological findings observed in human breast cancer epithelial MCF7, NIH3T3, and fibrosarcoma cells, demonstrating that the proposed model was concededly biologically appropriate. Statistical analysis of the simulated results shows that the generation of multiple p53 pulses is a prerequisite for apoptosis induction. Furthermore, cells exhibited considerable individual variability in p53 dynamics, which correlated with intrinsic apoptosis induction. The simulated results based on the proposed model demonstrated that the stochasticity of intranuclear biochemical reaction processes controls the final decision of cell fate associated with DNA damage. Applying stochastic simulation to an exploration of intranuclear biochemical reaction processes is indispensable in enhancing the understanding of the dynamic characteristics of biological multi-layered systems of higher organisms.

A Computational Framework for Bioimaging Simulation.

Using bioimaging technology, biologists have attempted to identify and document analytical interpretations that underlie biological phenomena in biological cells. Theoretical biology aims at distilling those interpretations into knowledge in the mathematical form of biochemical reaction networks and understanding how higher level functions emerge from the combined action of biomolecules. However, there still remain formidable challenges in bridging the gap between bioimaging and mathematical modeling. Generally, measurements using fluorescence microscopy systems are influenced by systematic effects that arise from stochastic nature of biological cells, the imaging apparatus, and optical physics. Such systematic effects are always present in all bioimaging systems and hinder quantitative comparison between the cell model and bioimages. Computational tools for such a comparison are still unavailable. Thus, in this work, we present a computational framework for handling the parameters of the cell models and the optical physics governing bioimaging systems. Simulation using this framework can generate digital images of cell simulation results after accounting for the systematic effects. We then demonstrate that such a framework enables comparison at the level of photon-counting units.

Modeling Cellular Noise Underlying Heterogeneous Cell Responses in the Epidermal Growth Factor Signaling Pathway

Cellular heterogeneity, which plays an essential role in biological phenomena, such as drug resistance and migration, is considered to arise from intrinsic (i.e., reaction kinetics) and extrinsic (i.e., protein variability) noise in the cell. However, the mechanistic effects of these types of noise to determine the heterogeneity of signal responses have not been elucidated. Here, we report that the output of epidermal growth factor (EGF) signaling activity is modulated by cellular noise, particularly by extrinsic noise of particular signaling components in the pathway. We developed a mathematical model of the EGF signaling pathway incorporating regulation between extracellular signal-regulated kinase (ERK) and nuclear pore complex (NPC), which is necessary for switch-like activation of the nuclear ERK response. As the threshold of switch-like behavior is more sensitive to perturbations than the graded response, the effect of biological noise is potentially critical for cell fate decision. Our simulation analysis indicated that extrinsic noise, but not intrinsic noise, contributes to cell-to-cell heterogeneity of nuclear ERK. In addition, we accurately estimated variations in abundance of the signal proteins between individual cells by direct comparison of experimental data with simulation results using Apparent Measurement Error (AME). AME was constant regardless of whether the protein levels varied in a correlated manner, while covariation among proteins influenced cell-to-cell heterogeneity of nuclear ERK, suppressing the variation. Simulations using the estimated protein abundances showed that each protein species has different effects on cell-to-cell variation in the nuclear ERK response. In particular, variability of EGF receptor, Ras, Raf, and MEK strongly influenced cellular heterogeneity, while others did not. Overall, our results indicated that cellular heterogeneity in response to EGF is strongly driven by extrinsic noise, and that such heterogeneity results from variability of particular protein species that function as sensitive nodes, which may contribute to the pathogenesis of human diseases.

Numerical simulation for synchronization of excitatory action between beating cells

The isolated beating cells have different intrinsic excitatory period interval with fluctuation. The excitatory conduction between the beating cells synchronizes with time when such beating cells formed a cellular clot, which eventually shows single excitatory period interval. However, as of yet, it is not clear how the beating cell regulates the excitatory action of other cells. On the other hand, FitzHugh, Nagumo et al. and Hodgkin et al. proposed a kinetic mathematical model which made it possible to realize the excitatory action numerically. The numerical simulation makes it possible to analyze several effects of the excitatory period interval on the synchronization based on the experimentally observed data, so it is useful for elucidation of a mechanism of the synchronization. In the current study, with employing a kinetic mathematical model for the excitatory conduction, we developed a novel numerical simulator for which excitatory wave propagates on a two-dimensional cellular matrix, and numerically analyzed an interference of excitatory conduction. In addition, we verified several effects of both average and fluctuation for the excitatory period interval on the synchronization of excitatory conduction. Our proposed numerical simulator constructed by employing Barkley’s model qualitatively realized a synchronization of excitatory conduction between beating cells. The synchronization of excitatory conduction was regulated by single cell for which both the average and standard deviation for excitatory period interval is smaller than those of other cells. As a result, it was clear that both the average and the standard deviation for the excitatory period interval played an important role in the synchronization of excitatory conduction between beating cells. Therefore, a consideration of both the average and the standard deviation for excitatory period interval is indispensable to elucidation of a mechanism of the synchronization between beating cells.