BIO 270

Metabolism Write-up

In lab, you are performing a procedure called indirect calorimetry. In this experiment you will measure oxygen consumption of a mouse (about 20g) and of a rat (about 100g). This measurement will be converted, through a series of calculations (described in the lab manual) to give you metabolic rate in Cal/hr/m² . Below is a table of oxygen consumption and weights of many different animals (be glad I didn't ask you to stuff an elephant into that chamber)

Weight in grams (g)	O2 consumption (ml/hr)	Metabolic rate (Cal/hr/m2)
4.8	35.5	158.9
23.6	102.8	97
96	98.8	23
290	250	19.3
2500	1700	15.2
11700	3870	7.4
42700	9590	5
70000	14760	4.7
650000	71100	2.5
3833000	268000	1.6

Two graphs are presented below that use the data from the above table. In the first you see the relationship between body size(weight) and metabolic rate. In the second graph, you see the relationship between body size(weight) and oxygen consumption.

Figure 1: The relationship of weight to oxygen consumption in animals. Oxygen consumption was plotted against weight.

Figure 2: The relationship of weight to metabolic rate in animals. Metabolic rates were calculated from oxygen consumption data for various animals and plotted against weight .

1. What is the relationship of body size (measured as weight in grams) with oxygen consumption?

2. What is the relationship of body size (measured as weight in grams) with metabolic rate?

3. Give a hypothesis comparing the metabolic rate of the rat and the mouse.

Performing Statistical analysis of data and generating H_o and H_a for statistical testing

(Using pooled data from our lab sections)

The following example is used to demonstrate how to set up hypotheses appropriate for statistical testing. In this experiment, two groups of mice were treated, one with a placebo andt he other with a weight-loss medication, ephedrine to test its effect on weight loss. The table below summarizes the data.

Table 1: Weights of control and ephedrine treated mice

Condition mouse + ephedrine	weight (g)	Condition mouse + placebo	weight (g)
	15	mouse	20
	17		25.2
	19		19.1
	20		20.1
	8.5		11.5
	15		24
	11		16
	13		18.5
	19		22.4
	10		23.1
AVERAGE	11.5	AVERAGE	19.9

Test the hypothesis that the average weight of ephedrine treated mice is statistically significantly lower than the average weight of placebo treated mice.

H_o (null hypothesis): The average weight of ephedrine treated mice is the same or greater than the average weight of placebo treated mice.

H_a (alternative hypothesis): The average weight of ephedrine treated mice is lower than the average weight of placebo treated mice.

How to make an inference: Reject H_o if p<0.05. Which statistical test is appropriate in this case? ___________

How to make a conclusion: The conclusion is based on the hypothesis that is accepted. For example, if H_a is accepted, then we would conclude that mice have a lower average weight than do rats of the same age and this is statistically significant.

Now it's your turn!

Test the hypothesis that the average metabolic rate of mice is higher than the average metabolic rate of rats. Use the pooled data that will either be sent to you by email as an attachment or posted to the website.

Using MS Excel (or some other database/spreadsheet program), calculate the metabolic rates of the mice and rats used in the lab. This will have been demonstrated in class, but I will also refer you to the metabolism lab protocol.

1. Using the information presented in the metabolism lab, explain the calculation of metabolic rate. This means that you should describe the information that you start with (all 8 steps in the equation of the data you collect- time in seconds for 15 mls of oxygen consumption) and how you transform that to a metabolic rate.

2. Report the average and standard deviation of the metabolic rate of the mice

3. Report the average and standard deviation metabolic rate of the rats.

4. What is your H_o?

5. What is your H_a?

6. What is the statistical test you will use to analyze the data?

7. Report the p-value.

8. What is your inference?

9. What is your conclusion?

10. Graph your data using good graphing techniques.