Investigating Genetics and Evolution
In 1988, Michigan State University's Richard Lenski began evolving E. coli bacteria, by 2024 the same experiment had run 80,000 generations and documented 19 new traits.
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Q1 · You want to find out whether antibiotic resistance is increasing in Australian hospitals. Where would you look for reliable data, and what would make a source trustworthy?
Think about the difference between peer-reviewed research, government reports and social media posts.
Q2 · A social media post claims "GM foods cause cancer" with no linked study. What red flags tell you this is not a reliable scientific source?
Consider what reliable scientific claims should include.
● Know
- The steps in designing a secondary-source investigation
- How to identify reliable data sources for genetics and evolution topics
- Methods for processing and representing data (tables, graphs, calculations)
● Understand
- How to analyse data to identify trends, patterns and relationships
- The difference between correlation and causation in biological data
- How to draw evidence-based conclusions and acknowledge limitations
● Can do
- Design a valid, ethical secondary-source investigation on genetics or evolution
- Process raw data and create appropriate graphical representations
- Write a conclusion that links data to the original research question
In 1988, biologist Richard Lenski placed 12 identical flasks of E. coli in an incubator at Michigan State University and checked back every day, and kept checking for 36 years, watching bacteria evolve new abilities right before his eyes. Evolution is often perceived as too slow to observe, but many organisms reproduce so rapidly that evolutionary change can be detected in days or weeks. Bacteria such as Escherichia coli divide every 20 minutes under ideal conditions, producing over 70 generations per day. Fruit flies (Drosophila melanogaster) produce a new generation in about 10 days. These organisms are the workhorses of experimental evolution because they allow researchers to watch natural selection in real time.
To design a valid evolution experiment, you need a clear hypothesis, a selective pressure, and proper controls. The selective pressure is the environmental challenge you impose, high temperature, limited food, presence of a predator, or exposure to an antibiotic. The control group experiences normal conditions. By comparing the experimental and control populations over many generations, you can measure whether the trait of interest changed in response to selection.
The Lenski Long-Term Evolution Experiment, running since 1988, has tracked 12 populations of E. coli for over 75,000 generations. All populations were started from the same ancestor and grown in identical environments except for subtle differences. One population evolved the ability to digest citrate, a nutrient it previously could not use, due to a rare combination of mutations. This experiment directly demonstrates that new traits can evolve through natural selection acting on random mutations.
Australian research: Evolutionary biologists at Monash University run experimental evolution studies using native Australian organisms such as mosquitofish and seed beetles. By exposing populations to predators or changing temperatures, they measure how quickly traits such as body shape, behaviour and reproduction evolve, providing insights into how Australian wildlife might respond to climate change.
Students often think evolution cannot be tested experimentally because it takes millions of years. This confuses the timescale of major evolutionary transitions with the timescale of selection acting on existing variation. Evolutionary change in allele frequencies happens every generation, and with fast-reproducing organisms, we can measure it directly. Experimental evolution is a thriving field with rigorous, repeatable methods.
Put the steps of designing an evolution experiment in the correct order.
- Set up control and experimental groups that differ only in the selective pressure.
- Measure the trait in each generation and calculate changes in frequency.
- Analyse data and decide whether the results support or refute the hypothesis.
- Run multiple replicates to ensure results are due to selection, not chance.
- Identify a variable trait in a fast-reproducing organism such as bacteria or fruit flies.
- Formulate a hypothesis about how a selective pressure will change the trait over generations.
Designing a valid experiment requires careful attention to variables. The independent variable is the factor you deliberately change, in our fruit fly example, this might be the selective pressure such as temperature or food type. The dependent variable is what you measure, perhaps body size, development time or survival rate. All other factors must be kept constant between groups; these are controlled variables.
Replication is essential. A single flask or cage might yield odd results due to chance contamination, a stray draft, or an unusually robust individual. By running multiple replicates, typically at least three, preferably more, you can average out random variation and be more confident that your results reflect the treatment effect rather than luck. Evolution experiments also need enough generations for selection to produce measurable change; a single generation is rarely sufficient.
In a classic fruit fly experiment, researchers selected for increased bristle number by allowing only the flies with the most bristles to reproduce. Over 20 generations, average bristle number increased significantly compared to unselected control lines. The experiment demonstrated that artificial selection can produce rapid evolutionary change in a visible trait, mirroring how natural selection operates in the wild.
Australian science education: The Australian Museum runs citizen science projects where students can participate in real evolutionary research, such as studying how native plant species adapt to urban environments. These projects teach experimental design, data collection and analysis while contributing to genuine scientific knowledge.
Here's a student's experimental design. One line has an error, click it.
- Flask A is at 25 C with antibiotic; Flask B is at 37 C without antibiotic.
- The student changes both temperature and antibiotic presence between the two flasks.
- Because two variables differ, the student cannot tell whether any result is due to temperature or antibiotic.
- A correct design would keep antibiotic presence the same in both flasks and vary only temperature.
Not all evolution experiments use living organisms. Digital evolution uses computer programs that replicate, mutate and compete in virtual environments. These 'digital organisms' can evolve complex behaviours such as logic operations, cooperation and even parasitism. Digital evolution is powerful because every mutation and selection event can be recorded perfectly, which is impossible with living cells.
Whether using bacteria, fruit flies or computer code, the logic of experimental evolution is the same: impose a selective pressure, allow replication with variation, and measure what changes. Computer simulations are particularly useful for testing theoretical predictions: how does population size affect the speed of adaptation? How much genetic variation is needed for a population to survive environmental change? The answers inform conservation biology, agriculture and medicine.
Avida is a digital evolution platform where self-replicating computer programs compete for CPU time. Researchers have shown that these programs can evolve the ability to perform complex logic functions through a sequence of simpler steps, each slightly beneficial. The evolutionary trajectory mirrors what we see in biological systems, demonstrating that the principles of natural selection are substrate-independent, they work on DNA, computer code or any system with replication, variation and selection.
Australian computational biology: Researchers at the University of Sydney use computational models to predict how pathogens such as influenza and SARS-CoV-2 will evolve in response to vaccines and treatments. By simulating mutation and selection in viral populations, they can anticipate which variants are likely to emerge and advise public health strategies accordingly.
The Australian Commission on Safety and Quality in Health Care (ACSQHC) publishes annual reports on antimicrobial resistance and usage in Australia. These reports are freely available and contain high-quality data suitable for student investigations. For example, the 2023 report shows that while MRSA bloodstream infections have declined by 23% since 2011, resistance to carbapenems (last-resort antibiotics) in E. coli has increased by 47%. This mixed pattern makes for excellent investigation material, there is no simple story, and you must analyse multiple factors.
Even when you are not collecting data from living humans, ethics matter. Secondary-source investigations in genetics and evolution raise several ethical issues:
- Data integritynever fabricate, alter or selectively quote data to support a predetermined conclusion
- Source credibilitydistinguish peer-reviewed research from opinion blogs, social media and pseudoscience
- Aboriginal and Torres Strait Islander datagenetic and archaeological research involving Indigenous peoples requires community consent and benefit-sharing
- Attributionalways cite your sources properly; plagiarism is scientific misconduct
- Interpretation limitsdo not overstate what the data shows; acknowledge uncertainty and alternative explanations
Swimming Australia uses data analysis techniques similar to those in scientific research to track athlete performance over time. Coaches collect secondary data on stroke rates, turn times and split times across competitions, then analyse trends to identify where athletes are improving or plateauing. In 2021, data analysts working with swimmers at the Australian Institute of Sport identified that small improvements in turn technique, just 0.1 seconds per turn, could make the difference between medal positions at the Tokyo Olympics. The same analytical skills you use in science, spotting trends, quantifying change, drawing evidence-based conclusions, are used by elite sports scientists every day.
Design Your Investigation
Option A: How has the rate of MRSA infections in Australian hospitals changed over the past 15 years?
Option B: What does the fossil record reveal about the evolution of horse body size over the past 50 million years?
Option C: How do mutation rates compare between RNA viruses (e.g., influenza) and DNA-based organisms (e.g., humans)?
1 Write a clear research question and a testable hypothesis for your chosen topic.
2 Identify at least two reliable sources you would use. For each, explain why it is reliable.
3 Describe how you would process and represent your data (table format, calculations, graph type). Justify your choices.
Interpreting Antibiotic Resistance Data
| Year | Total isolates tested | Resistant to amoxicillin (%) | Resistant to ciprofloxacin (%) | Resistant to carbapenems (%) |
|---|---|---|---|---|
| 2014 | 420 | 52 | 8 | 0.5 |
| 2016 | 445 | 55 | 11 | 0.7 |
| 2018 | 480 | 58 | 14 | 1.1 |
| 2020 | 510 | 61 | 17 | 1.6 |
| 2022 | 535 | 64 | 21 | 2.3 |
| 2024 | 560 | 67 | 25 | 3.1 |
1 Calculate the percentage increase in resistance to each antibiotic from 2014 to 2024. Show your working.
2 Which antibiotic shows the fastest rate of resistance increase? Suggest one evolutionary mechanism that explains why resistance to this antibiotic might be spreading faster than the others.
3 Identify one limitation of this dataset and explain how it affects the conclusions you can draw.
Copy Into Your Book
▼Investigation Steps
- 1. Question and hypothesis
- 2. Source identification
- 3. Data selection
- 4. Processing and representation
- 5. Analysis
- 6. Conclusion
- 7. Evaluation
Data Processing Skills
- Organise into tables with units
- Calculate means and ranges
- Calculate percentages and rates
- Choose appropriate graph types
Analysis Skills
- Identify trends and patterns
- Find relationships (correlation)
- Do not confuse correlation with causation
- Flag anomalies
Ethical Considerations
- Never fabricate or alter data
- Use credible, peer-reviewed sources
- Respect ICIP for Indigenous data
- Cite all sources properly
- Acknowledge limitations and uncertainty
At the start of this lesson you were told that a 2021 survey found 55% of Australians had encountered health misinformation online in the previous week. That statistic was chosen to show you that being able to evaluate genetic and evolutionary claims is a real, everyday skill, not just a school exercise.
Now that you have practised evaluating data, identifying reliable sources and distinguishing evidence from opinion, go back to an example from earlier in this unit, perhaps a claim about GM food safety, antibiotic resistance or human evolution. How would you now evaluate it more rigorously than you could at the start of this lesson?
Q1. Distinguish between reliability and validity in the context of a secondary-source investigation. Give one example of how an investigation could be reliable but invalid. 4 MARKS
Q2. Using the antibiotic resistance data from Activity 2, describe the trend for each antibiotic and explain how natural selection could produce these patterns. 5 MARKS
Q3. Evaluate the following claim: "If we stop using antibiotics, antibiotic resistance will disappear within a few years." Use your knowledge of natural selection, mutation and evolutionary change to support your evaluation. 6 MARKS
Revisit Your Initial Thinking
Go back to your Think First responses at the top of the lesson.
- Did you identify that evaluating a scientific claim requires checking the source credibility, sample size, methodology and whether the data supports the conclusion?
- Did you recognise that a well-designed investigation has a clear question, reliable data, appropriate analysis and acknowledged limitations?
- Write one sentence summarising the most important skill you learned for conducting a secondary-source investigation.
Model answers (click to reveal)
Comprehensive Answers
▼Activity 1, Design Your Investigation
Sample answers will vary by topic. A strong response includes: a specific, testable question [1 mark]; a hypothesis that predicts a direction or relationship [1 mark]; at least two credible sources with justification [1 mark]; a clear plan for processing and representing data with justified graph choice [1 mark].
Activity 2, Interpreting Antibiotic Resistance Data
1. Percentage increases (2014 to 2024):
Amoxicillin: ((67 - 52) / 52) x 100 = 28.8% increase [1 mark]
Ciprofloxacin: ((25 - 8) / 8) x 100 = 212.5% increase [1 mark]
Carbapenems: ((3.1 - 0.5) / 0.5) x 100 = 520% increase [1 mark]
2. Fastest increase: Carbapenems show the fastest rate of increase (520% over 10 years) [1 mark]. This may be because carbapenem resistance genes are carried on mobile genetic elements (plasmids) that spread rapidly between bacteria, or because carbapenems are used as last-resort antibiotics, creating strong selection pressure [1 mark].
3. Limitation: The data comes from a single hospital, so it may not represent all Australian hospitals or the community setting [1 mark]. This limits how broadly the conclusions can be generalised [1 mark]. Another valid limitation: the total number of isolates tested increased over time, which could affect percentage calculations if testing practices changed.
Multiple Choice
1. BSecondary-source investigations use existing data. Options A, C and D describe primary data collection.
2. CCorrelation does not prove causation. Both ice cream and sunburn are linked to hot weather (confounding variable).
3. AGovernment health commissions are credible, independent and evidence-based. Social media, anonymous blogs and promotional materials are not reliable.
4. DCarbapenems are last-resort antibiotics. Resistance is most concerning when it leaves few alternatives.
5. BAcknowledging scope limitations is a key scientific skill. Options A, C and D show poor scientific reasoning.
Short Answer Model Answers
Q6 (4 marks): Reliability refers to the consistency of results, if the investigation were repeated with the same sources, would it produce the same findings? [1 mark]. Validity refers to whether the investigation actually answers the research question it claims to answer [1 mark]. An investigation could be reliable but invalid if it consistently measures the wrong thing, for example, a student investigating "antibiotic resistance in Australian hospitals" who only uses data from one hospital in Sydney [1 mark]. The data might be consistently reported (reliable), but it does not represent all Australian hospitals, so the conclusion is invalid [1 mark].
Q7 (5 marks): All three antibiotics show an increasing trend in resistance from 2014 to 2024 [1 mark]. Amoxicillin resistance increased from 52% to 67% [1 mark]. Ciprofloxacin resistance increased most dramatically in relative terms, from 8% to 25% [1 mark]. Carbapenem resistance increased from 0.5% to 3.1% [1 mark]. Natural selection explains this because bacteria with random mutations conferring antibiotic resistance survive and reproduce when antibiotics are present [1 mark]. Over time, resistant alleles increase in frequency in the bacterial population, especially when antibiotic use creates strong selection pressure [1 mark].
Q8 (6 marks): This claim is partially true but overly simplistic [1 mark]. If antibiotic use were dramatically reduced, the selection pressure favouring resistant bacteria would decrease, and sensitive bacteria might outcompete resistant strains in the absence of antibiotics [1 mark]. However, resistance would not disappear completely because mutations that confer resistance already exist in bacterial populations and will continue to arise randomly [1 mark]. Additionally, many resistance genes are carried on plasmids that can persist in bacterial populations even without direct selection [1 mark]. Furthermore, some resistance genes have no fitness cost, so they are not selected against when antibiotics are removed [1 mark]. Therefore, while reducing antibiotic use is essential for slowing resistance, it is unlikely to eliminate it entirely, a combination of reduced use, better infection control and new treatments is needed [1 mark].
Jump Through Investigations!
Climb platforms using your knowledge of data analysis, ethics and working scientifically. Pool: Lesson 18.