A Density-Based Cellular Automaton Model for Studying the Clustering of Noninvasive Cells

How do chondrocytes aggregate on fibroin substrate

The effects of substrate material on the spatio-temporal behavior of cells is an important issue. Although cell aggregation has been observed on various fibroin substrates, the mechanisms of this aggregation have yet to be fully clarified. In this study, cell aggregation behavior on fibroin substrates were evaluated, focusing on the distance between each cell and the direction of individual cell migration. Our results showed that on fibroin substrates cells did not attract each other. However cells stayed close to adjacent cells over 24 hours of cultivation.

Quantification of cell co-migration occurrences during cell aggregation on fibroin substrates

A quantitative analytical method was proposed for measuring cell co-migration, which was defined as two or more cells migrating together. To accurately identify and quantify this behavior, cell migration on fibroin substrates was analyzed with respect to intercellular distance. Specifically, cell size was characterized by major diameter, and then, based on these measurements and cell center data, a specific threshold distance for defining co-migration was determined after analyzing cell motion using the Voronoi diagram method. The results confirmed that co-migration occurrences of rounded cells were significantly more stable on fibroin than on ProNectin substrates under the present experimental conditions. The cell co-migration analysis method in this article was shown to be successful in evaluating the stability of cell co-migration and also suggested the presence of "critical distance" where two cells interact on fibroin substrates. With further research, the cell co-migration analysis method and "critical distance" may prove to be capable of identifying the aggregation behavior of other cells on different materials, making it a valuable tool that can be used in tissue engineering design.

Live cell tracking based on cellular state recognition from microscopic images

The analysis of cell motion is an essential process in fundamental medical studies because most active cellular functions involve motion. In this paper, a computer-assisted motion analysis system is proposed for cell tracking. In the proposed tracking process, unlike in conventional tracking methods, cellular states referring to the cellular life cycle are defined and appropriate strategies are adopted for cells at different states. The use of cellular state recognition allows detection of possible cell division and hence can improve the robustness of cell tracking. Experimental results show that cells can be successfully segmented and tracked over a long period of time, and the proposed system is found to be as accurate as manual tracking. Various quantitative analyses and visualizations are used to represent cell motion, which demonstrates the usefulness of the proposed system in the study of cell dynamics.

Multi-agent systems simulating the physiological role of plasmic membrane

This paper presents our multi-agent framework for modelling and simulating the physiological role of plasmic membrane specially the 'endocytosis by receptors' and the 'exocytosis' mechanisms. As the dysfunction of a plasmic membrane may cause several illnesses (the family hypercholesterolemia, the dilation and the permeability of blood vessel, the allergic reactions as the asthma, rums of hays, hives, etc.), the objective of our multi-agent framework is to be a virtual world of cellular and molecular biology while simulating the physiological role of plasmic membrane in order to help its specialists to better understand, to good interpret and to warn changes of cell interaction and cell adaptation with its environment via its plasmic membrane. The architecture of our multi-agent framework is composed of a set of reactive agents. These agents use an ontology (named OntoCell) to communicate and to represent their knowledge. OntoCell describes the knowledge of cellular biology. Our framework is implemented with a multi-agent platform (named DIMA).

Characterizing the spatio-temporal behavior of cell populations through image auto-and cross-correlation microscopy

We propose two methods for characterizing the spatio-temporal behavior of cell populations in culture. The first method, image auto-correlation microscopy (IACM), allows us to characterize the variation in the number of objects as a function of time, thus enabling the quantification of the clustering properties of cell populations to be performed. The second method, image cross-correlation microscopy (ICCM), allows us to characterize the migration properties of cell populations. The latter method does not require estimation or measurement of the trajectories of individual cells, which is very demanding when populations of >100 cells are examined. The capabilities of the two methods are demonstrated with simulated cell populations, and their usefulness is illustrated with experiments involving invasive and noninvasive tumor cell populations.