Toward rigorous comprehension of biological complexity: modeling, execution, and visualization of thymic T-cell maturation

Abstract

One of the problems biologists face is a data set too large to comprehend in full. Experimenters generate data at an ever-growing pace, each from their own niche of interest. Current theories are each able, at best, to capture and model only a small part of the data. We aim to develop a general approach to modeling that will help broaden biological understanding. T-cell maturation in the thymus is a telling example of the accumulation of experimental data into a large disconnected data set. The thymus is responsible for the maturation of stem cells into mature T cells, and its complexity divides research into different fields, for example, cell migration, cell differentiation, histology, electron microscopy, biochemistry, molecular biology, and more. Each field forms its own viewpoint and its own set of data. In this study we present the results of a comprehensive integration of large parts of this data set. The integration is performed in a two-tiered visual manner. First, we use the visual language of Statecharts, which makes specification precise, legible, and executable on computers. We then set up a moving graphical interface that dynamically animates the cells, their receptors, the different gradients, and the interactions that constitute thymic maturation. This interface also provides a means for interacting with the simulation.

Figure 1

A pseudo statechart of a thymocyte. The three-dimensional representation is our way of representing statecharts from different levels and showing their interrelationships.

Figure 2

The 288 final nodes represent the final decisions of a thymocyte regarding which chemokine it may respond to. (The graph representing the tree was built with the DiGraph drawing algorithm described in Carmel et al. (2002).

Figure 3

A theory of interactions between thymic epithelial cells and thymocytes presented as a statechart.

Figure 4

A snapshot of the simulation during run time.

Figure 5

Decision making during simulation. The thyocyte surrounded by a circle in A decides where to migrate according to statecharts similar to the ones portrayed in B. The thymocyte in C, after making physical contact with an epithelial cell, instantiated the theory portrayed in Figure 3, concluding that it should proliferate.

Figure 6

An example of menus that open in response to clicking a thymocyte.

Figure 7

A visual representation of the developmental stages thymocytes go through. The representation also shows, together with conventional markers, the migratory abilities of each developmental stage. During run-time, the user may click on an animated thymocyte to retrieve the representation shown in the figure, with the appropriate stage highlighted. Further, each of the marked receptors and molecules serves as a button. A click on this button retrieves the scientific paper that is the source for the inclusion of the receptor, molecule, or any scientific detail.

Figure 8

User intervention directly influencing statecharts.

Figure 9

The software setup that enables the modeling, simulation, and interactive animation.

Figure 10

A general view of the methodology used in this work. The left side displays the procedure of turning scientific data into computer-legible specification. The right side displays the procedure of building self-constructed animation through building animation components and their instructional combinations.