2021/22 Undergraduate Module Catalogue

BLGY1325 Biology Practicals and Data Analysis

20 creditsClass Size: 200

Module manager: Jurgen Denecke
Email: j.denecke@leeds.ac.uk

Taught: Semesters 1 & 2 (Sep to Jun) View Timetable

Year running 2021/22

Module replaces

BLGY1125 Biology Practicals and Data Analysis, 20c, S1

This module is not approved as a discovery module

Module summary

This module offers a thorough introduction to laboratory experiments addressing processes at the molecular-, cellular-, whole organism and population level within the broader remit of biology. A series of lectures will first lead to a theoretical familiarisation with a broad range of standard laboratory practices including routine calculations, analytical methods to quantify biochemical processes and study bio-molecular and genetic interactions in vivo and in vitro. This also includes concepts of evolution, physiology, and morphology. Students will acquire an understanding of scientific experimentation design and process, to the extent that they appreciate the value of suitable controls and critical thinking.The selection of practicals focuses specifically on manual skills and safe working practices in a laboratory environment, trouble-shooting when unexpected difficulties arise and sufficient repetition to rectify mistakes. Students will also be introduced to keeping a laboratory book with accurate records of findings that can be understood by third parties as well as ways to disseminate results professionally.In the subsequent data analysis part of the module, students will develop understanding and comfort with curating and handling data in spreadsheets, choosing and using appropriate statistical tests and accompanying graphs, and how to present these in the style used by scientific papers. Students will learn to appreciate and understand orders of magnitude and appropriate levels of accuracy required in scientific experiments, as well as critical thinking and thorough statistical analysis to test reproducibility and significance of observations.

Objectives

The module provides an opportunity to gain understanding of routine laboratory techniques and offers a chance to learn basic experimental routines associated with the study of Biology at all levels. This includes a range of analytical methods to monitor biochemical reactions in a quantitative or qualitative manner, observe single cells and tissues as well as subcellular structures, and carry out research on whole organisms and populations. There will be opportunities to meet postgraduate researchers who will assist with the handling of basic pieces of equipment via demonstration, supervision and critical discussion.
Students are to be familiarised with scientific principles such as the difference between experimental science and pure observation, data recording, the difference between accuracy and precision as well as distinguishing technical replicates from biological replicates. One of the key objectives is to allow students to become aware of the importance of critical analysis presentational skills as well as the formulation of models and testing a hypothesis. The course will prepare students for the use of basic pieces of laboratory equipment and will obtain face to face guidance to complete regular exercises and familiarise students with basic equipment, analytical techniques and common work practices in order to help them to acquire basic conceptual and practical skills to carry out scientific research as well as adequate recording of experimental setups and obtained research data. This course also includes risk assessments for selected key activities. Students will be made aware of how to apply and interpret a range of statistical tests to scientific data and be taught how to import data into data-handling programs (i.e. Excel, SPSS), manipulate and graph them, and perform a number of parametric and non- parametric statistical tests. On completion of the course students should show an understanding of scientific experimentation, to the extent that they can begin to design experiments, appreciate the value of suitable controls and understand appropriate levels of accuracy required.

Learning outcomes
On completion of this module, students should:
1. Have a basic appreciation of good experimental design principles, as well as the definition of a working model and the principle that science cannot absolutely "prove" models, although a single type of experiment can disprove or refute a model, requiring a modification of the working model so that the previous and the new data can be explained. It is important that students understand that models are valuable until they are disproven;
2. Appreciate the complexity of day to day data/sample collection, the use of adequate records not only to have clear and transparent documentation of experimental conditions, but also to keep intermediate samples save and accessible for future research by labelling and constant cross-reference to laboratory books.
3. Have acquired basic conceptual understanding and practical experience of laboratory techniques for Cell Biology and Biochemistry, as well as whole organism and population studies.
4. Be able to carry out quick and reliable routine calculations in the laboratory including percentages, proportionalities, molarity, concentration and dilution factors, and appreciate a range of rational steps to streamline routine laboratory work, i.e. via the use of concentrated stock solutions and sample collections of all kind.
5. Understand how to manage and interpret data (numbers but also images and sounds) and apply statistical tests to address issues of significance and reproducibility.
This course teaches literacy in statistics and the use of Excel, both of which are skills that are highly sought after by employers.
Professional development opportunities include personal time management, good record keeping, common sense and action planning for multi-task procedures, as well as standard presentational skills (spreadsheets, powerpoint presentations and word documents with embedded figures and legends).

Skills outcomes
Students will be expected to work following health & safety guidelines for manual handling, and know how to plan complex experiments well in advance, based on written protocols that may be incomplete and require independent thinking. Students will also apply reliable recording, critical thinking, trouble-shooting, mathematics, comprehensive data analysis and various presentational skills.

Syllabus

This module is split over two semesters, with semester 1 devoted to the theory and practice of experimental Science in Biology, and semester 2 focussed specifically on data analysis.

Semester 1: In the first weeks, students will first attend Lectures on practical, analytical skills to cover experiments in biological sciences, starting from common laboratory numeracy, a thorough understanding of the terms molarity, molecular weight/mass, percentages, and basic units of volumes and weight, concentration, dilution as well as solving simple equations with single unknowns (respecting the units). Students will then explore/develop competence in preparing stock solutions and accurate dilutions/pipetting, using various forms of data analysis/display methods and record keeping. The basic principles behind a variety of biochemical, genetic and microscopy techniques will be explored in conjunction with an introduction to standard laboratory equipment (balances, spectrophotometers/plate readers, centrifuges, incubators and microscopes, basic bench practices and sterile handling techniques). A solid appreciation of good experimental design principles and the ability to critically present findings to peers concludes the theory base for all future aspects of the practical skills envisaged.
The remainder of semester 1 is built around 7 practical sessions of 3 hours each on a weekly basis supplemented with a dedicated feedback seminar to help students learn from each practical session to build a long term portfolio of practical experience. The focus of the practicals is on acquiring the necessary manual skills to handle basic equipment used in biological sciences and to understand experimental approaches and limitations. The sessions offer ample opportunity to learn from mistakes, to repeat specific steps to gradually improve, and to actively explore problem-solving and trouble shooting. The final practical is used to use all the experience gained to participate in an accuracy contest.

Semester 2: Data analysis and statistics will be taught via 9 one hour lectures and 9 one hour computer practicals slots. The lectures will focus on a thorough understanding of the various statistical methods to analyse different types of often highly variable biological data. This includes a thorough introduction to normality, means, medians and variability, subsampling, variance, transformations and non-parametric tests. Students will also become familiar with Hypothesis testing, Probability, Chi-squared, Comparing two groups, Comparing more than 2 means, Correlation and Regression. A strong focus is on Experimental design and choosing appropriate tests.
A linked set of dedicated in silico practicals and computer classes will allow ample opportunity to practice specific IT skills required for statistical analysis.

The assessment for the data analysis will be an in-course problem-based report. In this, students are given a dataset and asked broad questions about the data. They form specific biologically relevant hypotheses and test them with the data. They are not given step-by-step instructions on the analysis. To answer the questions, they must choose the appropriate parts of the dataset for each question, explore the data to choose appropriate tests and manipulate the data if necessary (e.g. perform transformations), perform the tests and extract the necessary information from outputs, present the data as appropriate graphs and present statistical results in the style of a paper, finally drawing conclusions from their analysis. This assesses their overall understanding of data analysis and their ability to use and present it as they will in future work.

Teaching methods

Delivery type	Number	Length hours	Student hours
Practical Demonstration	9	1.00	9.00
Lectures	9	1.00	9.00
seminars	7	1.00	7.00
Practical (computer based)	9	1.00	9.00
Practicals	7	3.00	21.00
Independent online learning hours			20.00
Private study hours			125.00
Total Contact hours			55.00
Total hours (100hr per 10 credits)			200.00

Private study

Private study should be restricted to specific exercises in mathematics and statistics (50 hours), as well as the preparation of personal notes and private exam revision (50 hours), supplemented by any curiosity-driven extra reading on the subjects covered in the module (45 hours).

Opportunities for Formative Feedback

Weekly formative in-course assessments will be held in weeks 5 and 9 in each semester. Specific feedback will be provided on-line for each assessment to highlight class weaknesses and strengths.

Methods of assessment

Coursework

Assessment type	Notes	% of formal assessment
In-course MCQ	Formative in-course MCQs (week 5 & 9)	0.00
In-course Assessment	Lab task (In-course final week practical challenge (accuracy contest & report)	25.00
In-course Assessment	Data analysis report (In-course problem-based dataset analysis)	50.00
Total percentage (Assessment Coursework)		75.00

Resits will be assessed in the same way, except that the in-course final week practical challenge and report (25%) will be replaced by a practical problem-based dataset analysis based on the practical classes.

Exams

Exam type	Exam duration	% of formal assessment
Online Time-Limited assessment	24 hr	25.00
Total percentage (Assessment Exams)		25.00

Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated

Reading list

The reading list is available from the Library website

Last updated: 06/10/2021 10:45:04

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