2.5 Enzymes

Experimental design—accurate, quantitative measurements in enzyme experiments require replicates to ensure reliability. (3.2)
This links to Practical 3 – Experimental investigation of a factor affecting enzyme activity.

Working with enzymes is something that all biology students get very familiar with over their studies!  This is particular true in the new syllabus as Practical 3 requires an enzyme experiment and they are popular as topics for the IA.

Testing the effect of substrate concentration on potato catalase.

Although there are several other practical-themed NOS statements, this one makes particular reference to the idea of reliability and replicates.  In terms of assessment this is most likely to appear in the Section A of Paper 3, when students are provided with experimental scenarios and have to apply their knowledge. However, it is also important for the IA.  The chosen investigation must design a methodology that will collect sufficient data, the data must be processed with appropriate awareness of uncertainties, and the reliability of the results reviewed and evaluated in the conclusion and evaluation.

This is thus an important lesson to not just experience the practical side of biology, but to understand the importance of replicates and how this impacts the IA.  So what could this look like? Here are a few ideas:

• If an enzyme-based experiment, aim for five variations of the independent variable (five different pHs; five different temperatures etc). As enzyme experiments are invariably time-based, this will allow you to plot a graph with more confidence (five data points rather than, say, three).
• Try to repeat each variation five times.  This will provide enough data to calculate the average and standard deviation. Of course there are more processing options than this (think rate of reaction) but these two are the basics.
• The conclusion/evaluation needs to then assess how the range of the independent variable, the sample size and the processed data contribute to the reliability of the experiment. The more replicates you have, the more robust this section will be.
• There are always time constraints on how many replicates you can collect – so factor this into your methods.  If your experiment is only collecting data for three minutes for each run, then you should be able to get more replicates (and you will be expected to collect more data).  If, in contrast, you are collecting data for more than an hour per experiment, then you will need to be aware of this.
• Finally, remember that design, processing and evaluation are all relative to the specific experiment you carried out – so always think in terms of the context for your investigation and the resources available to you.

 

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3.4 Inheritance

Making quantitative measurements with replicates to ensure reliability. Mendel’s genetic crosses with pea plants generated numerical data.

Gregor Mendel, the “Father of Genetics”, made his discoveries on inheritance by using the garden pea, Pisum sativum. Mendel’s experiments and data collection over eight years formed the foundation of theoretical genetics and were able to be used in diagnosing and explaining genetic diseases at the turn of he 20th-century.  Just as important as his discoveries, though, was his meticulous following of the scientific methods, illustrating perfectly that replicates in quantitative experiments allow for greater reliability in the conclusions.

The seven traits Mendel studied (Griffiths et al.)

Mendel worked with the seven traits outlined above and bred them for two years to establish pure, or homozygous, breeding strains. He then pollinated the parental flowers that showed variation in the trait – for example, crossing purple flowers with white flowers.  This produced in the F1 generation 100% purple flowers.  When these flowers were self-pollinated, Mendel noticed a curious relationship in the F2 offspring: a ratio of almost exactly 3:1 in the phenotype.

The data of the F2 ratio from Mendel’s experiments, a total of over   18, 000 breeding experiments. (Griffiths et al.)

On the basis of this data, Mendel was able to draw key conclusions about the nature of inheritance.  These were:

1. The existence of what we now know as genes.
2. That these genes come in pairs
3. Gene pairs segregate during the production of gametes
4. Each gamete thus only contains one gene of a pair.
5. Fertilisation is random

These statements were able to be tested by a new round of experiments, because they were based on quantitative data, had significant repetition and suggested certain patterns in inheritance.  His subsequent experiments provided confirmation of his analysis.  Not bad, considering the structure of DNA wouldn’t be determined for another century!

Mendel also did some interesting work on dihybrids, but that’s for a later topic!

Sources:

Griffiths AJF, Miller JH, Suzuki DT, et al. An Introduction to Genetic Analysis. 7th edition. New York: W. H. Freeman; 2000. Mendel’s experiments. Web. May 2, 2016.

Miko, I. Gregor Mendel and the principles of inheritance. Nature Education 1(1):134. 2008. Web. May 2, 2016.

1.4 Membrane Transport

Experimental design—accurate quantitative measurement in osmosis experiments are essential. (3.1)

This is one of the NOS that is relatively easy to incorporate into your learning.  Prescribed practical 2 is Estimation of osmolarity in tissues by bathing samples in hypotonic and hypertonic solutions; when you do this lab, you get a first-hand experience in why these measurements are important.

In my class we use potatoes and sucrose solutions of 0.1-0.5M, plus distilled water. We cut them into approximately equal-sized “chips” and place them in test tubes containing each of the six solutions.  Next class (or within 24h) we then remove them, measure again and investigate the changes.

While the potatoes are bathing in the solutions, we discuss the NOS as a class.  Here are some of our talking points:

• The movement of water, which will influence the change in size of the potatoes, is likely to be small. Thus accurate measurements are needed to demonstrate that there has indeed been change, rather than just random variation.
• Following on from this, the more measurements that can contribute to the data, the more accurate picture we might have – thus we need to accurately measure length, height, width and mass.
• Accurate replicates will enable us to process the data to investigate any changes with confidence.
• Measuring grams/mm requires careful attention to the uncertainties attached to those measurements.
• While qualitative data is still important, it is less objective than accurate quantitative measurements.
• These measurements need to be coupled with carefully controlled variables to allow the most accurate conclusion to be drawn.

 

4.3 Carbon Cycling and 4.4 Climate Change

Making accurate, quantitative measurements—it is important to obtain reliable data on the concentration of carbon dioxide and methane in the atmosphere. (IBO, 63) 

Assessing claims—assessment of the claims that human activities are producing climate change. (IBO, 65)

Climate change continues to be the subject of much debate in certain sections of the media and the world, despite the proliferation of evidence that supports both rapid climate change and the human role in driving it.  As these two NOS states, and as we learn in TOK, claims must be assessed and their evidence evaluated to determine their truth.

The best place to begin is to explore the website for the Intergovernmental Panel on Climate Change (IPCC), the global body that synthesises the climate research and produces reports at intervals of 5-6 years.  The Fifth Assessment Report was published between September 2013 and November 2014. The reports are provided in PDF and can be daunting!  However, the Summary for Policymakers  provides a nice overview of the main results of the review, including details on greenhouse gas emissions, temperature change, ocean acidification, snow and ice cover and changes in animal and plant behaviour and life-cycles.

A good activity for the students is adapted from Stephen Taylor’s page at i-biology.  Students can access a range of different databases from the CDIAC to examine carbon emissions.  I have the students collect data for both the last 5 years and for the entirety of the database they have chosen.  The longer the database, the more clearly the trend is displayed.  Students can then practice their graphing and analysis skills using spreadsheets.  With the new IA Guidelines allowing for database analysis, this could be a good starting point for an investigation.

Student Task Sheet

We finished our sequence of classes with a great discussion on the precautionary principle.  This is no longer explicitly required in the new syllabus but has great links to TOK and also to the idea of verifying data. We used the Visible Thinking Truth Routine – Claim, Support, Question– to examine two different articles with an opposing view of the PP. We ended up debating what certainty level is required for proof in the natural sciences – a nice end to the topic.

Source:

Biology Guide: First Assessment 2016. Cardiff: IBO, 2014. Print.

“Carbon Cycle.” BioNinja, 2017, http://www.ib.bioninja.com.au/standard-level/topic-4-ecology/43-carbon-cycling/carbon-cycle.html.