Dynamical analysis of the fission yeast cell cycle via Markov chain

FvMbp1-Swi6 complex regulates vegetative growth, stress tolerance, and virulence in Fusarium verticillioides

The mycotoxigenic fungus Fusarium verticillioides is a common pathogen of grain and medicine that contaminates the host with fumonisin B1 (FB1) mycotoxin, poses serious threats to human and animal health. Therefore, it is crucial to unravel the regulatory mechanisms of growth, and pathogenicity of F. verticillioides. Mbp1 is a component of the MluI cell cycle box binding factor complex and acts as an APSES-type transcription factor that regulates cell cycle progression. However, no information is available regarding its role in F. verticillioides. In this study, we demonstrate that FvMbp1 interacts with FvSwi6 that acts as the cell cycle transcription factor, to form the heteromeric transcription factor complexes in F. verticillioides. Our results show that ΔFvMbp1 and ΔFvSwi6 both cause a severe reduction of vegetative growth, conidiation, and increase tolerance to diverse environmental stresses. Moreover, ΔFvMbp1 and ΔFvSwi6 dramatically decrease the virulence of the pathogen on the stalk and ear of maize. Transcriptome profiling show that FvMbp1-Swi6 complex co-regulates the expression of genes associated with multiple stress responses. These results indicate the functional importance of the FvMbp1-Swi6 complex in the filamentous fungi F. verticillioides and reveal a potential target for the effective prevention and control of Fusarium diseases.

Types of Cell Death from a Molecular Perspective

The former conventional belief was that cell death resulted from either apoptosis or necrosis; however, in recent years, different pathways through which a cell can undergo cell death have been discovered. Various types of cell death are distinguished by specific morphological alterations in the cell's structure, coupled with numerous biological activation processes. Various diseases, such as cancers, can occur due to the accumulation of damaged cells in the body caused by the dysregulation and failure of cell death. Thus, comprehending these cell death pathways is crucial for formulating effective therapeutic strategies. We focused on providing a comprehensive overview of the existing literature pertaining to various forms of cell death, encompassing apoptosis, anoikis, pyroptosis, NETosis, ferroptosis, autophagy, entosis, methuosis, paraptosis, mitoptosis, parthanatos, necroptosis, and necrosis.

Mathematical Models of Death Signaling Networks

This review provides an overview of the progress made by computational and systems biologists in characterizing different cell death regulatory mechanisms that constitute the cell death network. We define the cell death network as a comprehensive decision-making mechanism that controls multiple death execution molecular circuits. This network involves multiple feedback and feed-forward loops and crosstalk among different cell death-regulating pathways. While substantial progress has been made in characterizing individual cell death execution pathways, the cell death decision network is poorly defined and understood. Certainly, understanding the dynamic behavior of such complex regulatory mechanisms can be only achieved by applying mathematical modeling and system-oriented approaches. Here, we provide an overview of mathematical models that have been developed to characterize different cell death mechanisms and intend to identify future research directions in this field.

Predicting efficacy of 5-fluorouracil therapy via a mathematical model with fuzzy uncertain parameters

Background:

Due to imprecise/missing data used for parameterization of ordinary differential equations (ODEs), model parameters are uncertain. Uncertainty of parameters has hindered the application of ODEs that require accurate parameters.

Methods:

We extended an available ODE model of tumor-immune system interactions via fuzzy logic to illustrate the fuzzification procedure of an ODE model. The fuzzy ODE (FODE) model assigns a fuzzy number to the parameters, to capture parametric uncertainty. We used the FODE model to predict tumor and immune cell dynamics and to assess the efficacy of 5-fluorouracil (5-FU) chemotherapy.

Result:

FODE model investigates how parametric uncertainty affects the uncertainty band of cell dynamics in the presence and absence of 5-FU treatment. In silico experiments revealed that the frequent 5-FU injection created a beneficial tumor microenvironment that exerted detrimental effects on tumor cells by enhancing the infiltration of CD8+ T cells, and natural killer cells, and decreasing that of myeloid-derived suppressor cells. The global sensitivity analysis was proved model robustness against random perturbation to parameters.

Conclusion:

ODE models with fuzzy uncertain kinetic parameters cope with insufficient/imprecise experimental data in the field of mathematical oncology and can predict cell dynamics uncertainty band.

Crosstalk between Plk1, p53, cell cycle, and G2/M DNA damage checkpoint regulation in cancer: computational modeling and analysis

Different cancer cell lines can have varying responses to the same perturbations or stressful conditions. Cancer cells that have DNA damage checkpoint-related mutations are often more sensitive to gene perturbations including altered Plk1 and p53 activities than cancer cells without these mutations. The perturbations often induce a cell cycle arrest in the former cancer, whereas they only delay the cell cycle progression in the latter cancer. To study crosstalk between Plk1, p53, and G2/M DNA damage checkpoint leading to differential cell cycle regulations, we developed a computational model by extending our recently developed model of mitotic cell cycle and including these key interactions. We have used the model to analyze the cancer cell cycle progression under various gene perturbations including Plk1-depletion conditions. We also analyzed mutations and perturbations in approximately 1800 different cell lines available in the Cancer Dependency Map and grouped lines by genes that are represented in our model. Our model successfully explained phenotypes of various cancer cell lines under different gene perturbations. Several sensitivity analysis approaches were used to identify the range of key parameter values that lead to the cell cycle arrest in cancer cells. Our resulting model can be used to predict the effect of potential treatments targeting key mitotic and DNA damage checkpoint regulators on cell cycle progression of different types of cancer cells.