About this Research Topic
Cell fate decision is a fundamental biological process involved in development, disease progression and regeneration. In this process, a cell alters its behaviors and functions in a robust manner. At the tissue level, the fates of a cell population must be made correctly in terms of both space and time. The dynamical properties of the fate decisions are governed by regulatory networks consisting of complex molecular interactions within and between cells. Mathematical modeling has become an essential component that complements experimental procedures for understanding cell fate and cellular dynamics. Recent accumulation of molecular data at higher precisions and greater scales provides new opportunities and challenges for modeling these cellular processes.
Computational models provide important tools for understanding the function and dynamic behavior of biological systems. Molecular network modeling plays a crucial role in identifying potential drug targets. This Research Topic will introduce new concepts concerning cell fate decisions that are driven by mathematical modeling, as well as new mathematical and computational tools that help to understand the complexity and dynamics of cell fate decisions. The topic will focus on the mathematical modeling of molecular or cellular networks for cell decision processes. We welcome investigators to contribute their latest original research articles, as well as relevant review articles on recent advances in computational methods and their applications to cell dynamics, disease progression, and the understanding of the underlying mechanisms of biological processes involved in cell fate decisions.
The aim of the current Research Topic is to cover promising, recent, and novel research trends in the field of cell fates and cellular dynamics. Areas to be covered in this Research Topic may include, but are not limited to:
• Development of computational approaches in modeling cellular dynamics
• Molecular networks modeling on cell cycle progression, differentiation and cell death
• Spatial control of cell fate decisions
• Chemical basis of morphogenesis
• Stochastic models for cell fate switching
• Temporal dynamics driven by cell fate switching
• Mathematical modeling for molecular networks in disease progression
• Models for cell fate decisions in regeneration
• Modeling for cell-cell communication networks
• Computational models for the dynamics of neural systems
Computational models provide important tools for understanding the function and dynamic behavior of biological systems. Molecular network modeling plays a crucial role in identifying potential drug targets. This Research Topic will introduce new concepts concerning cell fate decisions that are driven by mathematical modeling, as well as new mathematical and computational tools that help to understand the complexity and dynamics of cell fate decisions. The topic will focus on the mathematical modeling of molecular or cellular networks for cell decision processes. We welcome investigators to contribute their latest original research articles, as well as relevant review articles on recent advances in computational methods and their applications to cell dynamics, disease progression, and the understanding of the underlying mechanisms of biological processes involved in cell fate decisions.
The aim of the current Research Topic is to cover promising, recent, and novel research trends in the field of cell fates and cellular dynamics. Areas to be covered in this Research Topic may include, but are not limited to:
• Development of computational approaches in modeling cellular dynamics
• Molecular networks modeling on cell cycle progression, differentiation and cell death
• Spatial control of cell fate decisions
• Chemical basis of morphogenesis
• Stochastic models for cell fate switching
• Temporal dynamics driven by cell fate switching
• Mathematical modeling for molecular networks in disease progression
• Models for cell fate decisions in regeneration
• Modeling for cell-cell communication networks
• Computational models for the dynamics of neural systems
Keywords: Gene regulatory network, pattern formation, cell cycle, cell fate decisions, stochastic models
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
