A stochastic model of size control in the budding yeast cell cycle

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.

Dynamical analysis of the fission yeast cell cycle via Markov chain

The cell cycle is a complex network involved in the regulation of cell growth and proliferation. Intrinsic molecular noise in gene expression in the cell cycle network can generate fluctuations in protein concentration. How the cell cycle network maintains its robust transitions between cell cycle phases in the presence of these fluctuations remains unclear. To understand the complex and robust behavior of the cell cycle system in the presence of intrinsic noise, we developed a Markov model for the fission yeast cell cycle system. We quantified the effect of noise on gene and protein activity and on the probability of transition between different phases of the cell cycle. Our analysis shows how network perturbations decide the fate of the cell. Our model predicts that the cell cycle pathway (subsequent transitions from [Formula: see text]) is the most robust and probable pathway among all possible trajectories in the cell cycle network. We performed a sensitivity analysis to find correlations between protein interaction weights and transition probabilities between cell cycle phases. The sensitivity analysis predicts how network perturbations affect the transition probability between different cell cycle phases and, consequently, affect different cell fates, thus, forming testable in vitro/in vivo hypotheses. Our simulation results agree with published experimental findings and reveal how noise in the cell cycle regulatory network can affect cell cycle progression.

On the Molecular Mechanisms Regulating Animal Cell Size Homeostasis

Cell size is fundamental to cell physiology because it sets the scale of intracellular geometry, organelles, and biosynthetic processes. In animal cells, size homeostasis is controlled through two phenomenologically distinct mechanisms. First, size-dependent cell cycle progression ensures that smaller cells delay cell cycle progression to accumulate more biomass than larger cells prior to cell division. Second, size-dependent cell growth ensures that larger and smaller cells grow slower per unit mass than more optimally sized cells. This decade has seen dramatic progress in single-cell technologies establishing the diverse phenomena of cell size control in animal cells. Here, we review this recent progress and suggest pathways forward to determine the underlying molecular mechanisms.

The bZIP transcription factor FpAda1 is essential for fungal growth and conidiation in Fusarium pseudograminearum

Fusarium pseudograminearum is an important pathogen of Fusarium crown rot and Fusarium head blight, which is able to infect wheat and barley worldwide, causing great economic losses. Transcription factors (TFs) of the basic leucine zipper (bZIP) protein family control important processes in all eukaryotes. In this study, we identified a gene, designated FpAda1, encoding a bZIP TF in F. pseudograminearum. The homolog of FpAda1 is also known to affect hyphal growth in Neurospora crassa. Deletion of FpAda1 in F. pseudograminearum resulted in defects in hyphal growth, mycelial branching and conidia formation. Pathogenicity assays showed that virulence of the Δfpada1 mutant was dramatically decreased on wheat coleoptiles and barley leaves. However, wheat coleoptile inoculation assay showed that Δfpada1 could penetrate and proliferate in wheat cells. Moreover, the FpAda1 was required for abnormal nuclear morphology in conidia and transcription of FpCdc2 and FpCdc42. Taken together, these results indicate that FpAda1 is an important transcription factor involved in growth and development in F. pseudograminearum.