Living in a Microbial World
| Module title | Living in a Microbial World |
|---|---|
| Module code | BIO3097 |
| Academic year | 2024/5 |
| Credits | 15 |
| Module staff | Dr Dan Bebber (Convenor) |
| Duration: Term | 1 | 2 | 3 |
|---|---|---|---|
| Duration: Weeks | 11 |
| Number students taking module (anticipated) | 80 |
|---|
Module description
To a first approximation, life on Earth is microbial, and the transformation of Earth from a barren ball of rock to the oxygen-rich environment that supports us is a direct result of microbial metabolism. Even the human body contains more microbial than human cells. From global biogeochemical cycles to the interactions between individual organisms and their retinue of beneficial and detrimental microorganisms, understanding microbial ecology – the interactions between microbes and their environment – is key to understanding the functioning of Planet Earth. We will study carbon and nutrient cycling in the oceans, how terrestrial ecosystems are formed and controlled by microbes, the impact of plant-microbe interactions on forests and food production, and the applications of microbial biotechnology.
Pre-lecture content will be delivered online and followed with interactive student-staff sessions, where we will improve your core data science skills: how to analyse and present data in R; how to deal with Big Data; how to interpret statistical results in the light of the scientific literature – all while learning about the roles that microbes play in natural and managed ecosystems.
Module aims - intentions of the module
You will learn about the ecology and important roles of microbes in marine and terrestrial ecosystems. You will uncover the role of microbes in major global challenges such as climate change and food security. The growth of nucleotide sequencing and other technologies has led to an explosion of data and knowledge on microbes. Understanding these technologies will be an important outcome of the module..
The classes will be structured around training in core data science skills for biologists, using the R environment for statistical analysis and programming. For example, you will learn about methods of exploring large datasets using statistical methods that reveal the structures within Big Data. Rather than listening passively to lectures, you will work in class to produce the kinds of outputs (e.g. data summaries, statistical tests, publication-quality figures) that are key tools in research.
Through the practicals and assessments, you will develop skills relevant to future employment:
- How to load and handle different data types in R.
- How to conduct univariate, bivariate and multivariate data analysis and statistical modelling.
- How to identify problems in experimental designs and address data quality issues.
- How to present data informatively and professionally.
- How to critically interpret the results of data analysis in the light of the scientific literature.
Intended Learning Outcomes (ILOs)
ILO: Module-specific skills
On successfully completing the module you will be able to...
- 1. Discuss the role of microbes in processes like nutrient cycling in marine and terrestrial ecosystems
- 2. Discuss the roles of microbes in forestry and sustainable agriculture
ILO: Discipline-specific skills
On successfully completing the module you will be able to...
- 3. Discuss microbial diversity and its determinants, and understand phylogenetic relationships among microbes
- 4. Discuss the role of microbes in biogeochemical cycles and biosphere-climate feedback mechanisms
ILO: Personal and key skills
On successfully completing the module you will be able to...
- 5. Analyse data and make elegant figures
- 6. Interpret the results of statistical analyses and tests.
- 7. Critically analyse scientific evidence in published literature.
Syllabus plan
Classes will develop your data handling, data analysis, statistical modelling and critical thinking skills. We will work on datasets relating to various aspects of microbial ecology, for example estimating microbial diversity in soil samples or modelling seasonal fluctuations in marine bacterial abundance. Each class will focus on writing R code to achieve particular tasks, preparing statistical results and figures, and interpreting those results in the light of the published scientific literature on that topic.
Learning activities and teaching methods (given in hours of study time)
| Scheduled Learning and Teaching Activities | Guided independent study | Placement / study abroad |
|---|---|---|
| 22 | 128 | 0 |
Details of learning activities and teaching methods
| Category | Hours of study time | Description |
|---|---|---|
| Scheduled learning and teaching | 22 | In-person classes working on core skills |
| Guided independent study | 66 | Class consolidation and associated reading |
| Guided independent study | 40 | Practising core skills and completion of assignments |
| Guided independent study | 22 | Pre-recorded lectures and bespoke online content |
Formative assessment
| Form of assessment | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|
| Graphical and statistical data analysis, critical thinking and interpretation | 1 hour | All | Written |
Summative assessment (% of credit)
| Coursework | Written exams | Practical exams |
|---|---|---|
| 100 | 0 | 0 |
Details of summative assessment
| Form of assessment | % of credit | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|---|
| Assessed coursework (data analysis, figure(s), critical interpretation) | 100 | R code to achieve required task, Figure(s), interpretation with references (1000 words) | All | Written |
Details of re-assessment (where required by referral or deferral)
| Original form of assessment | Form of re-assessment | ILOs re-assessed | Timescale for re-assessment |
|---|---|---|---|
| Assessed coursework | Assessed coursework (100 %) | All | Aug Ref/Def |
Re-assessment notes
Deferral – if you miss an assessment for certificated reasons that are approved by the Mitigation Committee, you will normally be either deferred in the assessment or an extension may be granted. If deferred, the format and timing of the re-assessment for each of the summative assessments is detailed in the table above ('Details of re-assessment'). The mark given for a deferred assessment will not be capped and will be treated as it would be if it were your first attempt at the assessment.
Referral - if you have failed the module (i.e. a final overall module mark of less than 40%) and the module cannot be condoned, you will be required to complete a re-assessment for each of the failed components on the module. The format and timing of the re-assessment for each of the summative assessments is detailed in the table above ('Details of re-assessment'). If you pass the module following re-assessment, your module mark will be capped at 40%.
Indicative learning resources - Basic reading
Overview of the module aims:
- Blaser MJ, et al. 2016. Toward a Predictive Understanding of Earth’s Microbiomes to Address 21st Century Challenges. mBio 7: e00714-16. http://dx.doi.org/10.1128/mBio.00714-16
Some introductory reading:
- Earth System Science: A Very Short Introduction by Tim Lenton, OUP (2016)
Main textbooks:
- R For Data Science, available online at https://r4ds.had.co.nz
- Modern Statistics with R, available online at https://www.modernstatisticswithR.com
Indicative learning resources - Web based and electronic resources
ELE page
Indicative learning resources - Other resources
- Primary research publications and review articles covering various topics will made available.
| Credit value | 15 |
|---|---|
| Module ECTS | 7.5 |
| Module pre-requisites | BIO2076 Ecology and Environment |
| Module co-requisites | None |
| NQF level (module) | 6 |
| Available as distance learning? | No |
| Origin date | 01/02/2016 |
| Last revision date | 01/03/2024 |
