Mitosis Lab Identifying stages of the cell cycle. This lab is ada...

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Mitosis Lab Identifying stages of the cell cycle. This lab is adapted from Bonner, J. M. 2010. A Scenario-Based Study of Root Tip Mitosis. Page(s) 36-49, in Tested Studies for Laboratory Teaching, Volume 31 (K.L. Clase, Editor). Proceedings of the 31st Workshop/Conference of the Association for Biology Laboratory Education (ABLE), 534 pages. Experiment Now that you can identify the stages of mitosis, lets put it to use to investigate the following problem: The Scenerio Mycologists working in Thailand report that a recently identified fungus, Rhizoctonia anaerobis, may be implicated in the death of several seasons of growth of Glycine max. Following periods of heavy rain that occurred during the growing seasons of 2003-2006 (total rainfall > 50 cm), agricultural observers noted that the normal growth rate of G. max decreased significantly (p < .005). Preliminary examination of these plants suggests that the root systems are not developing adequately. Examination of the soil in which the plants are grown reveals the presence of the fungus R. anaerobis. Initial chemical analysis suggests the presence in the soil of a lectin-like substance, presumably a secretion of the fungus. Lectins are known to accelerate mitosis. Fazio, A.W. (2008). Preliminary report of the effect of the fungus Rhizoctonia anaerobis on the growth of Glycine max. Journal of Mycological Interactions; 56, 47. January 15, 2014 Dr. Margaret MacNeil Department of Biology York College, CUNY Jamaica, NY 11451 Dear Dr. MacNeil: CROPS WITHOUT BOUNDARIES Let me introduce myself and explain the package that I hope will arrive soon at the X College, campus in USA. I am Anna Fazio, a Biology graduate (’79) of X College. Currently, I am working as a mycologist with Crops without Boundaries (CWB), a consortium of private foundations and universities. The goal of CWB is to develop and promote sustainable agriculture in developing areas of the world. Agricultural specialists in Thailand recently have suspected the involvement of a soil fungus in the death of many soybean plants (Glycine max) and they requested a mycologist to help with its discovery and identification. When I arrived, I soon found a fungus in many samples of the local fields, generally planted with soybeans. This fungus appears to thrive in the near-anaerobic conditions resulting from heavy rains. I noticed that the roots of many soybean plants looked unusual when I dug around them to obtain my soil samples. Our current hypothesis is that the soil fungus interferes somehow with the growth of the roots, because the roots are in greatest contact with the soil. We are planning to conduct our research on two fronts. First, we are studying the fungus to identify it and to determine if it produces a toxin. This work is being carried out by mycologists and chemists at CWB. On the second front, we would like to know more about the effect of the fungus on G. max. It struck me that this aspect of the research could be addressed by some York College biology students—can they determine whether the fungus has an effect on the growth of the roots of G. max? I have taken the liberty of assuming that some biology students, always ready for a challenge, will be able to help us, so I have supplied you with two sets of root tips of G. max, shipped in 70% ethanol. (International law prohibits the transport of the actual fungus out of the country.) One set of tips is from G. max plants exposed to the fungus, and the other is from the same plants without this exposure. I hope your students will be able to apply their understanding of biological concepts to this very real problem. Sincerely, Anna Fazio, NDM ’79, Ph.D. Supervising Mycologist, Crops without Boundaries Questions 1. Based on what you have read about this fungus’s suspected influence, what is your experimental hypothesis? What is your null hypothesis? Are they the same? 2. How would you design and experiment with onion bulbs to test whether lectins increased the number of cells in mitosis? 3. What would you measure and how would you measure it? 4. What would be an appropriate control? Data Collection and Analysis Data collection For this investigation, you will be analyzing two sets of onion root tips—one set of tips from plants exposed to fungus and one set of tips from plants not exposed to the fungus, with the aim to determine whether or not the fungus influences the rate of mitosis. You will conduct the experiment blind; that is, you will not know which tips are which until you have completed your analysis. The tips will be referred to as “blue” and “red” to distinguish between them—you will learn which condition is which after you complete your counts and the class’s data has been collected. When you are confident with your ability to identify the different stages of the cell cycle, you and a partner should study the images of the onion root tips from your instructor (each pair of students will be given different sets of images). Each set of image (identified only as “Blue” and “Red”) is from either the control group (no fungus) or the experimental/treatment group (grown with fungus); you will not know which is which until you turn in your data. Working as a team, identify the phase of the cell cycle for each cell in your set. Tally the numbers and include them in the tables below and on the class chart. Table 1. Individual group identification of cells in “blue” G. max root tips Tip Number of Cells Interphase Prophase Metaphase Anaphase and Telophase Total 1 2 3 Total Table 2. Individual group identification of cells in “red” G. max root tips Number of Cells Tip Interphase Prophase Metaphase Anaphase and Telophase Total 1 2 3 Total Presenting your data Once you have tabulated the data, you need to analyze it, both graphically. To graph your results, I recommend using MS Word or Excel. If you are using MS Word, select the “CHARTS” tab and then select the type of chart you are interested in making. For categorical data, I would recommend constructing a bar chart. You will have a number of options, but I would select a 2D clustered column chart. Once you make your selection, a spreadsheet will open with some columns and rows already labeled and a ready-made chart (Figure 1): Series 1 Series 2 Series 3 Category 1 4.3 2.4 2 Category 2 2.5 4.4 2 Category 3 3.5 1.8 3 Category 4 4.5 2.8 5 Figure 1: The default chart produced by Microsoft Word. To simplify things for yourself, just update the values displayed here with your own values and the chart will be updated as well. But before you can update the values, be sure you know which data belongs in which column or row. In this case, the rows should list the independent variable (that which is experimentally varied; also known as the manipulated variable) and the columns should include the dependent variable (also known as the responding variable). For your experiment, the variable you altered (treatment condition--with fungus or without fungus) is the independent variable and is listed in the rows. The percentage of cells in each phase of the cell cycle is the dependent variable and belongs in the columns. Once you have added your data, delete the unneeded columns and rows (Figure 2). Figure 2: The default chart showing user’s raw data, but no additional formatting. Next, turn your attention to the chart. There are two main things you should notice. First, is the formatting. There isn’t a title and the label for the Y-axis is missing. To make these additions to your table, select your chart by clicking on the border, and then Chart Layout from the Charts tab. Select Chart Title and Axis titles to add them to your chart (Figure 3). You can modify the text by selecting the title and typing in new text or reformatting it. Figure 3: The user’s chart with formatting. The second, more important thing to recognize is which data you are presenting. The data above is completely acceptable: it shows the number of cells counted from the control group and from the treatment group. What are some observations that you can make about these two groups? One observation is that the number of cells in the treatment group is 10X larger than in the control group. However, the fraction of the cells in mitosis in both groups is 20%. Since differences in sample sizes can lead to misinterpretation of the data, we need some way of standardizing the data so that we can make direct comparisons. Interphase Mitosis Control 50 10 Treatment 500 100 Once way to do this is to convert the number to percentages. If we do so with our sample data, we can see that in this example, 83% of the control cells are in interphase, and 83% of the cells in the treatment condition are in interphase as well; when controlling for sample size, there is no apparent difference between the two conditions (Figure 4). Both ways of reporting the data are correct, but using percentages in this case, helps demonstrate your findings better than using the raw numbers. In some cases, you will want to show your findings in both ways. Figure 4: The data shown as percentages, rather than raw numbers. Note that differences in sample sizes are eliminated, making it easier to compare differences between the control and treatment groups. Questions • For onion roots exposed to the fungus (treatment group), which phase were most cells in? For cells in Mitosis, which stage was the most common? Use data to support your answer. • For the control onion roots (those not exposed to the fungus), which phase of Mitosis were most cells in? For cells in Mitosis, which stage was the most common? Use data to support your answer? • Comparing the treatment and control groups, were there differences in the number of cells in the different stages? How can you control for differences in cell number so that the groups can be compared directly? • Once you controlled for differences in cell number, what differences did you observe between the treatment and control groups? Use data to support your answer. • What is the function of the control group? • What do the observed differences in the mitotic division rates of the control group and treatment groups tell you about the effect of fungus exposure on growth of the soybean plants? Use data to support your answer.

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1. Experimental hypothesis- Prove influence of Rhizoctonia anaerobis on soybean plant (Glycine max) root by increasing mitosis.
Null hypothesis- Rhizoctonia anaerobis does not influence cell mitosis in roots of soybean plant (Glycine max).
Experimental and null hypothesis are not the same
2. Designing this type of experiments considers running two type of tests. First representative sample of onion bulbs needs to be acquired. Same number of onion bulbs will be planted in soil with Rhizoctonia anaerobis, and the...

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