Biology Lab Protocols: Dilution, Tests, Photosynthesis

Simple and Serial Dilution
Biological Tests
Enzyme experiments example
Potometer Experiment (Transpiration Rate)
Respirometer Experiment (Rate of Respiration)
Investigating Yeast Respiration Using Redox Indicators
Investigating the effect of light intensity on the rate of photosynthesis in Elodea
Chromatography (Separation of Chloroplast Pigments)
Estimating Population Size Using Random Sampling with Frame Quadrats
Investigating Biodiversity Using Line and Belt Transects
Mark-Release-Recapture (Population Estimation)
Gel Electrophoresis to Separate DNA Fragments
Other concept info
Enzymes
Photosynthesis graphs

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Simple and Serial Dilution

Defining the Problem

Aim: To prepare different concentrations of a solution using simple and serial dilution.
Independent variable: Concentration of the solution.
Dependent variable: Final concentration achieved.
Controlled variables: Temperature, volume of solvent, accuracy of pipette use.

Planning the Experiment

1. Label five test tubes as 100%, 50%, 25%, 12.5%, and 6.25%.

2. Simple dilution:

○ Add 10 cm³ of the stock solution into tube 1.

○ Add 5 cm³ of the stock solution and 5 cm³ of distilled water in tube 2 (50%).

○ Repeat for other concentrations.

3. Serial dilution:

○ Add 10 cm³ of stock solution into tube 1.

○ Transfer 5 cm³ from tube 1 to tube 2, then add 5 cm³ of distilled water.

○ Repeat for all tubes.

Control Experiment

Use distilled water instead of the solution to verify accuracy.

Safety Precautions

Use gloves and goggles if handling corrosive chemicals.

Data Analysis

Use a colorimeter to measure absorbance at each concentration.

Biological Tests (e.g., Benedict’s, Biuret, Iodine, Emulsion tests)

Defining the Problem

Aim: To test for the presence of reducing sugars, proteins, starch, and lipids.
Independent variable: Type of biological molecule.
Dependent variable: Color change observed.
Controlled variables: Volume of reagent, time left for reaction, temperature.

Planning the Experiment

1. Benedict’s Test (Reducing Sugars)

○ Add 2 cm³ of Benedict’s reagent to 2 cm³ of sample.

○ Heat in a water bath at 80°C for 5 minutes.

○ Observe color change (blue → green/yellow/orange/red).

2. Biuret Test (Proteins)

○ Add 2 cm³ of Biuret reagent to the sample.

○ Observe color change (blue → purple if proteins are present).

3. Iodine Test (Starch)

○ Add iodine solution directly to the sample.

○ Observe color change (yellow-brown → blue-black if starch is present).

4. Emulsion Test (Lipids)

○ Add 2 cm³ of ethanol to the sample and shake.

○ Add 2 cm³ of COLD water and observe for a white emulsion.

Control Experiment

Use distilled water instead of the sample.

Safety Precautions

Ethanol is flammable; keep away from open flames.

Data Analysis

Record observed color changes and compare against known standards.

Enzyme experiments example: Investigating the effect of temperature on enzyme activity using catalase and hydrogen peroxide

Defining the Problem

Aim: To determine how different temperatures affect the activity of catalase.
Independent variable: Temperature (use thermostatically controlled water baths at 10°C, 20°C, 30°C, 40°C, and 50°C).
Dependent variable: Rate of oxygen production (measured using a gas syringe).
Controlled variables: pH (use a buffer), substrate concentration (same volume and concentration of H₂O₂), enzyme concentration (use equal mass of catalase source, e.g., potato discs).

Planning the Experiment

1. Prepare catalase solutions by blending equal masses of potato and filtering the extract.

2. Place 10 cm³ of 1.0 M hydrogen peroxide in a conical flask in a water bath set at different temperatures.

3. Add 1 cm³ of catalase solution to the flask and immediately connect a gas syringe to measure oxygen released.

4. Record the volume of gas produced every 30 seconds for 3 minutes.

5. Repeat the experiment 3 times at each temperature and calculate the mean.

6. Plot a graph of temperature vs. rate of reaction (oxygen produced per minute).

Control Experiment

Use boiled catalase to ensure oxygen release is enzyme-dependent.

Safety Precautions

Hydrogen peroxide is corrosive; wear gloves and goggles.
Handle glassware carefully to prevent breakage.

Data Analysis

Identify the optimum temperature by finding the highest rate of reaction.
Use a t-test to compare rates at two different temperatures for significance.

Potometer Experiment (Transpiration Rate)

Defining the Problem

Aim: To measure the rate of transpiration in a plant.
Independent variable: Environmental condition (light intensity, humidity, wind speed).
Dependent variable: Rate of water uptake (measured via movement of air bubble).
Controlled variables: Leaf surface area, temperature, species of plant.

Planning the Experiment

1. Cut a healthy shoot underwater and insert it into a potometer.

2. Seal joints with petroleum jelly to prevent air leaks.

3. Introduce an air bubble into the capillary tube.

4. Record the distance moved by the air bubble every minute for 5 minutes.

5. Repeat under different conditions (e.g., fan for wind, lamp for light, mist spray for humidity).

Control Experiment

Use a potometer without a plant to check for evaporation losses.

Safety Precautions

Use a razor blade with caution when cutting stems.

Data Analysis

Plot a graph of environmental factor vs. transpiration rate.

Respirometer Experiment (Rate of Respiration)

Defining the Problem

Aim: To measure the rate of respiration in germinating seeds.
Independent variable: Temperature or type of organism.
Dependent variable: Oxygen uptake (measured using manometer).
Controlled variables: Same mass of seeds, same time duration.

Planning the Experiment

1. Place germinating seeds in a respirometer.

2. Add potassium hydroxide (KOH) to absorb CO₂.

3. Use a manometer to measure the drop in oxygen level.

4. Record readings every 5 minutes for 20 minutes.

5. Repeat at different temperatures.

Control Experiment

Use boiled seeds to confirm changes are due to respiration.

Safety Precautions

KOH is corrosive; handle with gloves.

Data Analysis

Calculate respiration rate using the formula:

Investigating Yeast Respiration Using Redox Indicators (DCPIP/Methylene Blue)

Defining the Problem

Aim: To determine the effect of temperature on the rate of respiration in yeast using redox indicators.
Independent variable: Temperature (e.g., 20°C, 30°C, 40°C, 50°C, 60°C).
Dependent variable: Time taken for the indicator to change color (from blue to colorless for methylene blue or DCPIP).
Controlled variables: Same yeast concentration, same volume of indicator, same volume of glucose solution.

Planning the Experiment

1. Prepare a yeast suspension by mixing yeast with glucose solution.

2. Divide the solution into five test tubes.

3. Add 1 cm³ of methylene blue/DCPIP to each test tube.

4. Place each tube in a water bath set at different temperatures.

5. Start the stopwatch and record the time taken for the indicator to become colorless.

6. Repeat each experiment three times and calculate the mean.

Control Experiment

Use a boiled yeast solution to confirm that any color change is due to respiration.

Safety Precautions

Methylene blue/DCPIP can stain skin and clothing; wear gloves and goggles.
Handle glassware carefully to prevent breakage.

Data Analysis

Plot a graph of temperature vs. time taken for the indicator to change color.
Use Spearman’s rank correlation to determine the relationship between temperature and respiration rate.

Investigating the effect of light intensity on the rate of photosynthesis in Elodea

Defining the Problem

Aim: To determine how different light intensities affect the rate of photosynthesis in Elodea.
Independent variable: Light intensity (measured as 1/distance from the lamp²)
Dependent variable: Rate of oxygen production (measured by counting oxygen bubbles per minute or using a gas syringe to measure volume of gas collected)
Controlled variables: Temperature (use a thermostatically controlled water bath), CO₂ concentration (use a fixed concentration of sodium hydrogen carbonate), species and size of Elodea, same light wavelength (use same bulb).

Planning the Experiment

1. Cut equal lengths of Elodea (5 cm each) and place them in test tubes filled with 0.1M sodium hydrogen carbonate solution.

2. Place a lamp at different distances from the Elodea (e.g., 10 cm, 20 cm, 30 cm, 40 cm, 50 cm).

3. Allow the plant to acclimate for 5 minutes before measuring oxygen production.

4. Count the number of oxygen bubbles released per minute or collect gas in a graduated syringe.

5. Repeat each distance 3 times and calculate the mean rate of bubble production.

6. Plot a graph of light intensity (1/distance²) against the rate of photosynthesis.

Control Experiment

Set up an identical test tube but keep it in the dark to confirm that oxygen production is due to photosynthesis.

Safety Precautions

Avoid handling lamps with wet hands (risk of electrocution).
Use goggles to prevent sodium hydrogen carbonate solution from splashing into the eyes.

Data Analysis

Compare mean rates of oxygen production across different distances.
Perform Spearman’s rank correlation to determine the relationship between light intensity and photosynthesis rate.

Chromatography (Separation of Chloroplast Pigments)

Defining the Problem

Aim: To separate and identify pigments in a leaf.
Independent variable: Type of leaf.
Dependent variable: Rf values of pigments.
Controlled variables: Volume of solvent, temperature.

Planning the Experiment

1. Crush a fresh leaf with a mortar and pestle.

2. Spot the extract onto chromatography paper.

3. Place in a beaker with a solvent (acetone).

4. Allow solvent to rise and separate pigments.

5. Measure Rf values.

Control Experiment

Use a filter paper with no extract to check for solvent movement.

Safety Precautions

Acetone is flammable; keep away from flames.
OR mention that its a low risk experiment

Data Analysis

Calculate Rf values using

Estimating Population Size Using Random Sampling with Frame Quadrats

Defining the Problem

Aim: To estimate the population density of a plant species in a given area using frame quadrats.
Independent variable: Sampling location (randomly selected points).
Dependent variable: Number of plants per quadrat.
Controlled variables: Size of quadrat, sampling method, time of day, weather conditions/seasons/time of year.

Planning the Experiment

1. Select a large area (e.g., a field).

2. Use a random number generator to determine quadrat placement.

3. Place a 1 m² quadrat on the ground and count the number of target plants within the quadrat. (identify each species using a nature guide)

4. Repeat at least 10 times for reliability.

5. Calculate population density using:

Control Experiment

Compare quadrat counts in an area with no plant growth to ensure no external bias.

Safety Precautions

Wear gloves if handling thorny plants.
Be cautious of insect bites and uneven terrain.

Data Analysis

Use Simpson’s Diversity Index to

assess species diversity.

Compare density values between different locations.

MARK SCHEMES:

10. Investigating Biodiversity Using Line and Belt Transects

Defining the Problem

Aim: To assess changes in species distribution along an environmental gradient (e.g., from a pond to dry land).
Independent variable: Distance from starting point.
Dependent variable: Number of species observed.
Controlled variables: Time of day, sampling method, quadrat size.

Planning the Experiment

1. Lay a measuring tape from the water’s edge into dry land.

2. For line transect, record every plant species touching the tape at regular intervals (e.g., every 50 cm).

3. For belt transect, place quadrats (e.g., 1 m²) at set intervals along the tape and count species inside each.

4. Repeat for different transects to improve accuracy.

Control Experiment

Use a control transect in an area with uniform habitat conditions.

Safety Precautions

Avoid disturbing wildlife and tripping over uneven ground.
Wear PPE (gloves, mask, shoes) to avoid insect bites/ pollen and plant allergy

Data Analysis

Calculate percentage cover for each species. (named identification scale- ACFOR or just say use a key/ nature guide to identify species)
Use Spearman’s rank correlation to analyze species distribution trends.

11. Mark-Release-Recapture (Population Estimation)

Defining the Problem

Aim: To estimate population size using the Lincoln Index.
Independent variable: Time between sampling.
Dependent variable: Number of recaptured marked individuals.
Controlled variables: Marking technique, sampling area.

Planning the Experiment

1. Capture a random sample of organisms.

2. Mark them with a non-toxic marker and release.

3. After a set period, recapture and count marked and unmarked individuals.

4. Use the Lincoln Index to estimate population size:

Control Experiment

Use an area with no organisms as a negative control.

Safety Precautions

Allergy to plants (risk)-----> wear gloves or PPE
Dangerous environments

Data Analysis

Compare estimated populations under different conditions

12. Gel Electrophoresis to Separate DNA Fragments

Defining the Problem

Aim: To separate DNA fragments based on size using gel electrophoresis.
Independent variable: DNA sample used.
Dependent variable: Distance traveled by DNA fragments.
Controlled variables: Voltage, gel concentration, loading dye volume.

Planning the Experiment

1. Prepare an agarose gel and place it in an electrophoresis tank.

2. Mix DNA samples with loading dye and pipette into wells.

3. Run the gel at 100V for 30 minutes.

4. Stain the gel with ethidium bromide and visualize under UV light.

5. Measure the distance traveled by DNA bands.

Control Experiment

Run a DNA ladder (standard size marker) alongside samples for comparison.

Safety Precautions

Ethidium bromide is carcinogenic; wear gloves and dispose of properly.
Handle UV light with caution to avoid eye damage.

Data Analysis

Calculate fragment size using the formula:
Compare band patterns between samples.

Other concept info

ENZYMES

PHOTOSYNTHESIS graphs:

1. Enzymes (Topic 2.1)

Competitive vs non-competitive inhibition (graph shapes + explanation)
Effect of temperature, pH, and substrate concentration on enzyme activity
Shape of rate of reaction curves
Explanation of Vmax and limiting factors
Lock and key / induced fit theory

Common in P5: Graphs showing rate of reaction, enzyme inhibition, or substrate concentration effects.

2. Cell Membranes and Transport (2.3)

Osmosis, diffusion, active transport — often tested via % mass change in plant tissue
Water potential terminology: hypertonic, hypotonic
Graph interpretation of osmosis experiments
Role of channel and carrier proteins

Common in P5: Potato strip experiments or Visking tubing to estimate water potential.

4. Cell Structure (1.2)

Organelles visible under a microscope
Differences between prokaryotes and eukaryotes
Use of stage micrometer and eyepiece graticule

🔁 Common in microscope drawing & calibration Qs — e.g., calculating actual size.

6. Transport in Plants (7.1)

Xylem vs phloem structure
Transpiration experiments using potometers
Effect of light, temperature, humidity on transpiration

Very common practical setup in Paper 5.

8. Biological Molecules (2.2)

Tests for reducing sugars, starch, proteins, lipids
Structure and function of carbohydrates, lipids, proteins

Often tested indirectly, e.g., explain results of Benedict’s or Biuret test.

Revise the graph shapes, variables, and explanations for the above

Be able to explain why results look a certain way, based on AS knowledge

-good to brush up on these topics for those random questions about a certain effect.

INTERPRETING DATA and REACHING CONCLUSIONS: (common questions)

Points to mention to evaluate if graph supports conclusion or not:
comment on the gradient, how it shows the hypothesis (+data quote)
comment on if range is limited (doesnt support)
replications/ mean HAVE to be taken and then plotted for more accuracy.
the graph needs to have plateaued to tell accurate MAX reading of y axis
(eg. max vol of gas produced)
(gas may not be only 02)

If they provide a table of data and ask you to give a conclusion:

always give a numerical relationship between datasets to prove

hypothesis.

eg: it increases by 12.3% or its 5.7 times more

(to find the times divide larger val by smaller)

→Varying the independent variable:

keep 5 values of IV and state how you will change the conditions.

→Control variable:

always state the control + how its conditions are made

(if its environmental, example a fish or plant in water, same pH, temp, 02

conc)

Reason for calculating percentage:

allaws for better comparisons for a general population
very useful for cases where there are different sample sizes.

Null hypothesis:

for chi square and t test:

There is no significant difference between data (O and E for chi square, 2

data sets for t test)

-for pearsons and spearmans correlation:

There is no significant correlation between the two sets of data

accept/reject null hyp? (diff for chi square and t test, relation for pearson and spearman)

-if p<0.5; AND your value is greater than the critical value at p=0.05

REJECT NULL HYPOTHESIS —----> p of diff/relation by chance is < 5%,

so the diff/relation is NOT by chance, they ARE significantly diff/related

-if p>0.5 AND your value is lesser than critical value at p=0.05,

ACCEPT NULL HYPOTHESIS —----> p of diff/relation by chance is > 5%,

so the diff/relation is by chance, they ARE NOT significantly diff/related

CONFIDENCE INTERVALS:

used to get a measure of how close a calculated mean is to the true mean.
if it says “error bars show 95% CI, means theres a 95% chance the values are in this range
if all the error bars overlap; theres NO significant difference in data. BUT if
error bars distinctly have diff ranges, they are significantly different

STATS TESTS: important info

Chi square T test pearsons spearmans
Conditions to use
(+all data by random sampling and all
data independent from eachother)
-Discrete
-f> 5 values
-Cont
->15
-Cont
->5
-Discrete
-10 to 30
Degrees of freedom n-1 Ntot -2 n-2 n-2
Conclusion from calculated value
Compare your value to the critical value at p=0.05 (for specific degrees of freedom)
THEN make conclusion based on if its greater or lesser (logic is above in
null hypothesis section)
Value always between -1 to +1
→ Closer to +1 or -1 = stronger correlation
→ +1 = perfect positive correlation (more closer to +1, more positive correlation)
→ 0 = no linear correlation

ALSO:

SIMPSONS INDEX: from 0 till 1
Closer to 1 = high diversity,
Closer to 0 = low diversity
Lincoln index: to estimate population size:
Conditions: no migration, no births/deaths, marks don’t wear off or affect
behaviour.
Magnification: Image size/ actual size

4. Potometer Experiment (Transpiration Rate)

Defining the Problem

Aim: To measure the rate of transpiration in a plant.
Independent variable: Environmental condition (light intensity, humidity, wind speed).
Dependent variable: Rate of water uptake (measured via movement of air bubble).
Controlled variables: Leaf surface area, temperature, species of plant.

Planning the Experiment

1. Cut a healthy shoot underwater and insert it into a potometer.

2. Seal joints with petroleum jelly to prevent air leaks.

3. Introduce an air bubble into the capillary tube.

4. Record the distance moved by the air bubble every minute for 5 minutes.

5. Repeat under different conditions (e.g., fan for wind, lamp for light, mist spray for humidity).

Control Experiment

Use a potometer without a plant to check for evaporation losses.

Safety Precautions

Use a razor blade with caution when cutting stems.

Data Analysis

Plot a graph of environmental factor vs. transpiration rate.

5. Respirometer Experiment (Rate of Respiration)

Defining the Problem

Aim: To measure the rate of respiration in germinating seeds.
Independent variable: Temperature or type of organism.
Dependent variable: Oxygen uptake (measured using manometer).
Controlled variables: Same mass of seeds, same time duration.

Planning the Experiment

1. Place germinating seeds in a respirometer.

2. Add potassium hydroxide (KOH) to absorb CO₂.

3. Use a manometer to measure the drop in oxygen level.

4. Record readings every 5 minutes for 20 minutes.

5. Repeat at different temperatures.

Control Experiment

Use boiled seeds to confirm changes are due to respiration.

Safety Precautions

KOH is corrosive; handle with gloves.

Data Analysis

Calculate respiration rate using the formula:

Investigating Yeast Respiration Using Redox Indicators (DCPIP/Methylene Blue)

Defining the Problem

Aim: To determine the effect of temperature on the rate of respiration in yeast using redox indicators.
Independent variable: Temperature (e.g., 20°C, 30°C, 40°C, 50°C, 60°C).
Dependent variable: Time taken for the indicator to change color (from blue to colorless for methylene blue or DCPIP).
Controlled variables: Same yeast concentration, same volume of indicator, same volume of glucose solution.

Planning the Experiment

1. Prepare a yeast suspension by mixing yeast with glucose solution.

2. Divide the solution into five test tubes.

3. Add 1 cm³ of methylene blue/DCPIP to each test tube.

4. Place each tube in a water bath set at different temperatures.

5. Start the stopwatch and record the time taken for the indicator to become colorless.

6. Repeat each experiment three times and calculate the mean.

Control Experiment

Use a boiled yeast solution to confirm that any color change is due to respiration.

Safety Precautions

Methylene blue/DCPIP can stain skin and clothing; wear gloves and goggles.
Handle glassware carefully to prevent breakage.

Data Analysis

Plot a graph of temperature vs. time taken for the indicator to change color.
Use Spearman’s rank correlation to determine the relationship between temperature and respiration rate.

Investigating the effect of light intensity on the rate of photosynthesis in Elodea

Defining the Problem

Aim: To determine how different light intensities affect the rate of photosynthesis in Elodea.
Independent variable: Light intensity (measured as 1/distance from the lamp²)
Dependent variable: Rate of oxygen production (measured by counting oxygen bubbles per minute or using a gas syringe to measure volume of gas collected)
Controlled variables: Temperature (use a thermostatically controlled water bath), CO₂ concentration (use a fixed concentration of sodium hydrogen carbonate), species and size of Elodea, same light wavelength (use same bulb).

Planning the Experiment

1. Cut equal lengths of Elodea (5 cm each) and place them in test tubes filled with 0.1M sodium hydrogen carbonate solution.

2. Place a lamp at different distances from the Elodea (e.g., 10 cm, 20 cm, 30 cm, 40 cm, 50 cm).

3. Allow the plant to acclimate for 5 minutes before measuring oxygen production.

4. Count the number of oxygen bubbles released per minute or collect gas in a graduated syringe.

5. Repeat each distance 3 times and calculate the mean rate of bubble production.

6. Plot a graph of light intensity (1/distance²) against the rate of photosynthesis.

Control Experiment

Set up an identical test tube but keep it in the dark to confirm that oxygen production is due to photosynthesis.

Safety Precautions

Avoid handling lamps with wet hands (risk of electrocution).
Use goggles to prevent sodium hydrogen carbonate solution from splashing into the eyes.

Data Analysis

Compare mean rates of oxygen production across different distances.
Perform Spearman’s rank correlation to determine the relationship between light intensity and photosynthesis rate.

Chromatography (Separation of Chloroplast Pigments)

Defining the Problem

Aim: To separate and identify pigments in a leaf.
Independent variable: Type of leaf.
Dependent variable: Rf values of pigments.
Controlled variables: Volume of solvent, temperature.

Planning the Experiment

1. Crush a fresh leaf with a mortar and pestle.

2. Spot the extract onto chromatography paper.

3. Place in a beaker with a solvent (acetone).

4. Allow solvent to rise and separate pigments.

5. Measure Rf values.

Control Experiment

Use a filter paper with no extract to check for solvent movement.

Safety Precautions

Acetone is flammable; keep away from flames.
OR mention that its a low risk experiment

Data Analysis

Calculate Rf values using

Estimating Population Size Using Random Sampling with Frame Quadrats

Defining the Problem

Aim: To estimate the population density of a plant species in a given area using frame quadrats.
Independent variable: Sampling location (randomly selected points).
Dependent variable: Number of plants per quadrat.
Controlled variables: Size of quadrat, sampling method, time of day, weather conditions/seasons/time of year.

Planning the Experiment

1. Select a large area (e.g., a field).

2. Use a random number generator to determine quadrat placement.

3. Place a 1 m² quadrat on the ground and count the number of target plants within the quadrat. (identify each species using a nature guide)

4. Repeat at least 10 times for reliability.

5. Calculate population density using:

Control Experiment

Compare quadrat counts in an area with no plant growth to ensure no external bias.

Safety Precautions

Wear gloves if handling thorny plants.
Be cautious of insect bites and uneven terrain.

Data Analysis

Use Simpson’s Diversity Index to

assess species diversity.

Compare density values between different locations.

MARK SCHEMES:

10. Investigating Biodiversity Using Line and Belt Transects

Defining the Problem

Aim: To assess changes in species distribution along an environmental gradient (e.g., from a pond to dry land).
Independent variable: Distance from starting point.
Dependent variable: Number of species observed.
Controlled variables: Time of day, sampling method, quadrat size.

Planning the Experiment

1. Lay a measuring tape from the water’s edge into dry land.

2. For line transect, record every plant species touching the tape at regular intervals (e.g., every 50 cm).

3. For belt transect, place quadrats (e.g., 1 m²) at set intervals along the tape and count species inside each.

4. Repeat for different transects to improve accuracy.

Control Experiment

Use a control transect in an area with uniform habitat conditions.

Safety Precautions

Avoid disturbing wildlife and tripping over uneven ground.
Wear PPE (gloves, mask, shoes) to avoid insect bites/ pollen and plant allergy

Data Analysis

Calculate percentage cover for each species. (named identification scale- ACFOR or just say use a key/ nature guide to identify species)
Use Spearman’s rank correlation to analyze species distribution trends.

11. Mark-Release-Recapture (Population Estimation)

Defining the Problem

Aim: To estimate population size using the Lincoln Index.
Independent variable: Time between sampling.
Dependent variable: Number of recaptured marked individuals.
Controlled variables: Marking technique, sampling area.

Planning the Experiment

1. Capture a random sample of organisms.

2. Mark them with a non-toxic marker and release.

3. After a set period, recapture and count marked and unmarked individuals.

4. Use the Lincoln Index to estimate population size:

Control Experiment

Use an area with no organisms as a negative control.

Safety Precautions

Allergy to plants (risk)-----> wear gloves or PPE
Dangerous environments

Data Analysis

Compare estimated populations under different conditions

12. Gel Electrophoresis to Separate DNA Fragments

Defining the Problem

Aim: To separate DNA fragments based on size using gel electrophoresis.
Independent variable: DNA sample used.
Dependent variable: Distance traveled by DNA fragments.
Controlled variables: Voltage, gel concentration, loading dye volume.

Planning the Experiment

1. Prepare an agarose gel and place it in an electrophoresis tank.

2. Mix DNA samples with loading dye and pipette into wells.

3. Run the gel at 100V for 30 minutes.

4. Stain the gel with ethidium bromide and visualize under UV light.

5. Measure the distance traveled by DNA bands.

Control Experiment

Run a DNA ladder (standard size marker) alongside samples for comparison.

Safety Precautions

Ethidium bromide is carcinogenic; wear gloves and dispose of properly.
Handle UV light with caution to avoid eye damage.

Data Analysis

Calculate fragment size using the formula:
Compare band patterns between samples.

Other concept info

ENZYMES

PHOTOSYNTHESIS graphs:

1. Enzymes (Topic 2.1)

Competitive vs non-competitive inhibition (graph shapes + explanation)
Effect of temperature, pH, and substrate concentration on enzyme activity
Shape of rate of reaction curves
Explanation of Vmax and limiting factors
Lock and key / induced fit theory

Common in P5: Graphs showing rate of reaction, enzyme inhibition, or substrate concentration effects.

2. Cell Membranes and Transport (2.3)

Osmosis, diffusion, active transport — often tested via % mass change in plant tissue
Water potential terminology: hypertonic, hypotonic
Graph interpretation of osmosis experiments
Role of channel and carrier proteins

Common in P5: Potato strip experiments or Visking tubing to estimate water potential.

4. Cell Structure (1.2)

Organelles visible under a microscope
Differences between prokaryotes and eukaryotes
Use of stage micrometer and eyepiece graticule

🔁 Common in microscope drawing & calibration Qs — e.g., calculating actual size.

6. Transport in Plants (7.1)

Xylem vs phloem structure
Transpiration experiments using potometers
Effect of light, temperature, humidity on transpiration

Very common practical setup in Paper 5.

8. Biological Molecules (2.2)

Tests for reducing sugars, starch, proteins, lipids
Structure and function of carbohydrates, lipids, proteins

Often tested indirectly, e.g., explain results of Benedict’s or Biuret test.

Revise the graph shapes, variables, and explanations for the above

Be able to explain why results look a certain way, based on AS knowledge

-good to brush up on these topics for those random questions about a certain effect.

INTERPRETING DATA and REACHING CONCLUSIONS: (common questions)

Points to mention to evaluate if graph supports conclusion or not:
comment on the gradient, how it shows the hypothesis (+data quote)
comment on if range is limited (doesnt support)
replications/ mean HAVE to be taken and then plotted for more accuracy.
the graph needs to have plateaued to tell accurate MAX reading of y axis
(eg. max vol of gas produced)
(gas may not be only 02)

If they provide a table of data and ask you to give a conclusion:

always give a numerical relationship between datasets to prove

hypothesis.

eg: it increases by 12.3% or its 5.7 times more

(to find the times divide larger val by smaller)

→Varying the independent variable:

keep 5 values of IV and state how you will change the conditions.

→Control variable:

always state the control + how its conditions are made

(if its environmental, example a fish or plant in water, same pH, temp, 02

conc)

Reason for calculating percentage:

allows for better comparisons for a general population
very useful for cases where there are different sample sizes.

Null hypothesis:

for chi square and t test: