An Improved Model of Nonuniform Coleochaete Cell Division

Bubble model analysis on the cell formation of a green alga

Mathematical simulation for cell and division, a complex process resulting from a series of major morphological evolution and biological mechanisms, is shown by using variational principle. Groups of differential equation systems with boundary conditions for the cell walls satisfying come out from analysis on minimally total length. Double bubble model for cell geometric formation is found and a mechanism for tri-bubble model is also investigated here. An interesting fact is that every angle between any two tangent lines at the same intersections of cell edges for both double bubble and tri-bubble is equal to [Formula: see text]. In addition to the derivation of these differential equation models, these theories are also applied to the specific experimental data analysis, and it is found that the model is very consistent with the experimental results.

Cell Modeling Based on Bubbles with Weighted Membranes

Mathematicization for cell modeling provides an effective tool to verify the biological theory, and the existing research mainly focuses on the description of cell structures. This article then addresses the pattern question of cell division or morphogenesis by means of bubble model with weighted membranes. In this study, we show that cell shapes including intersection angles at junction points depend on weights on membranes. For convenience, adhesion and contractile force are considered together as a factor in construction of patterning model. This model is also used to compare with experimental data. And the consistency between our model and experiments is also obtained consequently. This system of differential equations with their boundary conditions theorizes the existing experimental models, and improves the rationality of these models.

Computational Analysis of Cell Dynamics in Videos with Hierarchical-Pooled Deep-Convolutional Features

Computational analysis of cellular appearance and its dynamics is used to investigate physiological properties of cells in biomedical research. In consideration of the great success of deep learning in video analysis, we first introduce two-stream convolutional networks (ConvNets) to automatically learn the biologically meaningful dynamics from raw live-cell videos. However, the two-stream ConvNets lack the ability to capture long-range video evolution. Therefore, a novel hierarchical pooling strategy is proposed to model the cell dynamics in a whole video, which is composed of trajectory pooling for short-term dynamics and rank pooling for long-range ones. Experimental results demonstrate that the proposed pipeline effectively captures the spatiotemporal dynamics from the raw live-cell videos and outperforms existing methods on our cell video database.