5.3 Analysis Tools
In order to study anything in science it is necessary to be able to visualize and quantify what is happening. Without this basic ability you are simply shooting in the dark. Several different tools have been built for the developmental simulator to allow the researcher to peer into the inner workings and see what is happening.
One of the most powerful tools that biologists have recently added to their inventory is immuno-labeling. This tool allows the research to see the concentration of specific chemicals like proteins as they are actually in living cell cultures. In its simplest form, antibodies for the protein being studied are produced and tagged. The tag can be a number of different things from gold particles that show up in electron microscopy, or radioactive tags that stain film. But the tool specifically emulated here is that of fluorescent labeling. A fluorescent tag is added to the antibody, and as the antibodies attach to the proteins that we are interested in. A UV light is then used that causes the antibodies to actually glow. The researcher can then see where in the cells that those proteins are located, in which cells are the most concentrated, and using base lines they can even get a good quantitative idea of how much of the protein is present. The immuno-labeling system developed here is similar. The tool presents two-dimensional charts of sections of the cells that are being simulated. Within each chart the researcher can "label" different proteins or genes. The labeling takes place using a query for the protein or gene. Each chart can have a maximum of three queries, and each of them are associated with one of the primary colors: red, green, and blue. The query itself looks for genes or proteins that match specific criteria. So for instance you can find all transcription factors with a binding ID of 100, or you can get even more specific and specify range values for each of the parameters of the transcription factor. Similarly, you can find a gene that expresses the protein above and has enhancers with binding ID's of 100 and 212. The tool goes through each cell that is being charted and finds the total quantity of all matching proteins or genes and uses this to paint a color onto the chart for that cell based on the quantity. Each query also contains a color range. If the quantity value is within this range then the color that is pained is proportional to this range. For example, lets say we have a query for a protein that has a quantity of 1000 in a cell, and the color range is 0 to 3000. Color values can range between 0 and 255. So the color that will be painted will be ((1000/3000) * 255) = 85. And since this query is associated with blue that will be the value of blue for that pixel. Other queries may specify the values to use for red and green. This combination then creates a color value that will allow you to get an idea of the quantities for all three queries simultaneously. Figure 1 gives a demonstration of what immuno-labeling looks like. Table 1 shows the different types of quantities that can be measured and displayed with the different analysis tools. Finally, a series of images can be captured at specified time slices that can be combined to produce a video showing how the quantities change over time.
This is very similar to the immuno-labeling above. The main difference is that instead of trying to display a whole section of cells to get an idea of the quantities we make a graph to display the actual values in specific cells. There is no restriction of only having three queries here. But each query has to be specific for not only the protein to find, but for the cell to search in also. This allows the research to get a better, quantitative view of what is happening in the cells and how the proteins are interacting. Figure 2 shows a typical example of what an immuno-graph looks like.
4. Chromosome Analysis
Attempting to understand the relationships between the different proteins and genes in even a simple chromosome can be very difficult. Which genes does protein TF_0 effect and how? What does the expressed protein of receptor CR_2 do? This questions can be very difficult to answer when you are looking at the raw chromosome even when it is saved out as xml. In its raw binary format it is pretty much impossible. The chromosome analysis tool lets us answer these types of questions by producing a graphical chart that shows how all of the proteins and genes in the chromosome are related. Also, by clicking on any of the links you can see the actual properties of the gene or protein, including a graph of the expression functions if they exist. The different arrowheads are significant and help show what kind of relationship one node has with another. For example, a dark arrowhead means that the origin node produces the destination node. So G_1 produces CR_2 and CR_2 produces GC_3. The circles are used to represent a gene controller affecting a gene. Filled in circles means it turns the gene off and open circles means it turns it on. There are a number of different types of associations, but I am not going to go into them fully right now. Chromosome analysis's are sprinkled throughout the following pages in places where it is helpful for you to see the interactions between the proteins and genes during a description. If you click on the link it will open a new browser window showing the analysis.
5. Analysis Tools Overview
This page was meant to give the reader a basic understanding of the different analysis tools that were used for this system so that they can follow along with the different graphs and figures. The graphs, diagrams, and charts used throughout this section of the site are primarily generated using these tools. Therefore if you are to understand what is being discussed it is necessary to have a basic grasp of how the different tools work and what they do.