MMI-L1-24-Moodle

Introduction to the Course

  • Course Title: MVLS: Life Sciences Level 2 - Contemporary Issues in Biology 2

  • Focus: Me, myself and I

  • Lecturers:

    • Dr. Stevie A Bain (she/her)

    • Dr. Paul Capewell (he/him)

Lecturers' Backgrounds

Dr. Stevie A Bain

  • Role: Lecturer in Biomolecular Sciences

  • Specialization: Deputy Quantitative Skills Lead for Statistics

  • Expertise: Evolutionary Genetics (unique sexual reproduction systems)

  • Contact: stevie.bain@glasgow.ac.uk

Dr. Paul Capewell

  • Role: Lecturer in Molecular Genetics

  • Specialization: Population Genetics and Host/Pathogen Co-evolution

  • Experience: Worked as a Bioinformatician in the healthcare sector

  • Contact: paul.capewell@glasgow.ac.uk

Course Structure

  • Emphasis on biology as a quantitative subject.

  • Use of class data for statistical analyses.

  • Introduction to RStudio for data visualization and analysis. (Note: Not a programming course)

Learning Outcomes

  • Understanding Biology as Quantitative:

    • Describe why biology involves quantitative analysis.

  • Statistical Analysis Importance:

    • Explain the significance of statistical analyses in biological research.

  • Descriptive Statistics:

    • Define key descriptive statistics: mean, median, range.

Biological Data

  • Biological data is foundational in qualitative and quantifiable research.

Variation within Species

Understanding Variation

  • Variation Example: Human height.

Key Quote on Measurement

  • "When you can measure what you are speaking about and express it in numbers, you know something about it. But when you cannot measure it, your knowledge is meagre and unsatisfactory."

    • William Thomson, Lord Kelvin (1824-1907)

Importance of Data

  • Understanding data allows for establishing causes and making predictions.

Observed Data Charts

  • Observed variations in temperature data from 1900 to 2100 establish the importance of predictive analytics.

Quantifying Biological Data

  • Accurate measurements of biological parameters are essential for reliable results.

    • Parameters: length, mass, concentration, time, genetic sequence.

  • Importance of manipulating variables and controlling experimental conditions.

  • Role of mathematical models and statistical analyses in genetic research.

Variation in Human Height

Class Experiment Design

  • Explore research questions, hypotheses, experimental design, data collection/visualization, and results presentation.

Data Collection Guidelines

  • Participation: Data entry is voluntary and anonymous.

  • Use of SI Units:

    • Length measured in metres (cm for height).

Data Entry Instructions

  • Enter height in cm only (number without units).

Post Data Collection

  • Examine raw data in Excel.

  • Clean data for analysis in labs.

Initial Data Analysis

  • Identify variables and choose necessary statistics and graphs.

Using R and RStudio

  • R Software Overview:

    • Open-source, free statistical computing environment running on different operating systems.

  • Recommended analytical package for Life Sciences courses.

RStudio Functionality

  • Provides user-friendly interface for R development.

  • Both are available on the University student desktop and personal installations encouraged.

Descriptive Statistics Practice

  • Focus of lab sessions on understanding and analyzing statistical output.

Important Notes on R Usage

  • No need to memorize R code for exams, understand statistical analysis outputs instead.

Influential Factors in Human Height Variation

  • Identify potential variables that contribute to height diversity.

Statistics Support Availability

  • Resources accessible through Moodle for additional help in statistical methods related to life sciences.

Conclusion of Intended Learning Outcomes

  • Reinforce learning goals regarding biology as a quantitative field and key statistical concepts.