Analyzing Trends in Enzyme Reaction Data

• Trends need support from multiple data sources, ideally pulled from a graph.

Explaining trends in data

• Explain how parameters change as temperature increases to optimum, and what happens after optimum is exceeded.
• Example: a reaction at 25°C had a rate of 10 mm/sec, the highest rate compared to 5°C and 45°C.
• At 5°C, substrate particles moved slower leading to fewer collisions at the active site, resulting in a lower reaction rate.
• At 45°C, the reaction rate could be zero because the enzyme denatured.
• Avoid using terms like "steep increase" or "rapid decrease."
• Explain trends from available data, even when mistakes occur or results deviate from expectations.
• The enzyme used is potato catalase, which neutralizes hydrogen peroxide by decomposing it.

Reliability

• Refers to the consistency and closeness of the repeats of an experiment.
• To assess, make a judgment and provide reasoning (whether it is judged reliable or unreliable) and evidence from the data.
• Procedure changes could include ensuring a timer is used or assessing the range of pH buffers used.

Validity

• Refers to how well controlled variables are, and whether the experiment measured what it intended to measure.
• When assessing validity, a clear judgment must be made whether the experiment is valid or not, with supporting evidence.
• Check if all variables were controlled, and whether the aim of the experiment was addressed.
• Determine if measurements reflected the effect of temperature on catalase activity or if uncontrolled variables impacted the experiment.

Experiment Modifications

• Measure the volume of released oxygen instead of the volume of bubbles.
• Bubbles in test tubes vary in shape, leading to nonuniform oxygen concentration.
• The test can be improved by capturing released oxygen to provide a more quantitative data set.
• Use a balloon over the experiment (simple but qualitative), or use a graduated cylinder on test tubes placed in a filled beaker to measure water displacement.

Errors

• Two types of experimental errors: systematic and random errors.
• Measurement errors can be classified into systematic errors and random errors.

Systematic Errors

• Errors occur consistently in the same direction because of flaws, e.g. miscalibrated scale always adds extra weight.
• They occur systematically due to flaws in measuring instruments, experimental setups, or observer bias.
• Can be reduced by calibrating instruments and improving techniques.
• Include instrumental errors (faulty equipment), observational errors (parallax when reading a scale), and environmental errors (temperature or humidity).

Random Errors

• Unpredictable variations in measurements caused by environmental fluctuations or human reaction time.
• Errors are minimized by taking multiple measurements and averaging them.
• Arise from unpredictable variations, such as random observational errors from inconsistent human reaction times, and random environmental errors from electrical noise in sensors.
• Scatter measurements around the true value, and can be minimized by averaging multiple readings.

Graphing Data

• When graphing the effect of temperature on potato catalase, plot average reaction rates for each temperature.
• Place temperature on the x-axis and reaction rate on the y-axis.

Validity Improvements

• If water bath temperature might fluctuate, use a digital water bath or continuously monitor and adjust with a thermometer.
• Always place test tubes in the water bath for a set time (e.g., 1 minute) before adding hydrogen peroxide to allow proper equilibration.
• If only 3 trials are performed per condition, increase to at least 5 trials per temperature to reduce random errors and improve data accuracy.
• If measuring bubble volume visually might be inconsistent, collect oxygen in an inverted graduated cylinder filled with water to measure gas volume precisely.