MSc Bioinformatics and Systems Biology

Year of entry: 2021

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Course unit details:
Computational Approaches to Biology

Unit code BIOL66021
Credit rating 15
Unit level FHEQ level 7 – master's degree or fourth year of an integrated master's degree
Teaching period(s) Semester 1
Offered by School of Biological Sciences
Available as a free choice unit? No


Computational methods are increasingly used in all areas of biology in order to better understand complex living systems and develop models that generate testable predictions. This unit introduces a range of computational techniques, including differential equations, machine-learning, network and constraint-based analysis, which are used for a wide range of biomedical and biotechnological applications, from understanding how intracellular signalling pathways are disrupted in diseases to redesigning organisms by metabolic engineering.


This unit aims to introduce students to a wide range of computational methods and tools required to carry out interdisciplinary research in the biological sciences.


The unit will start with an introduction to essential mathematical concepts required to understand methods used for biological modelling. Students will be introduced to the Jupyter Notebook system, a widely used online application allowing the development of code for data analysis and numerical simulation based on the Python language, which will be used throughout lectures and practicals.

The core of the unit will be structured along three main sections, each covering a particular set of techniques and applications:

Section 1: Dynamic models

·      Introduction to differential equations-based modelling

·      Classical models in theoretical ecology I: single-species population dynamics and Lotka Volterra competition models.

·      Classical models in theoretical ecology II: Lotka-Volterra models for predator-prey systems.

·      Modelling gene regulatory pathways I: gene transcription

·      Modelling gene regulatory pathways II: cellular signalling pathways

Section 2: Models of large cellular systems

·      Network reconstruction and analysis: protein-protein interaction networks, metrics for network analysis, integration of high-throughput biological data.

·      Logical modelling: Boolean models, logical steady state analysis, applications to cancer systems.

Section 3: Dimensionality reduction and clustering

·      Dimensionality reduction: Principal Component Analysis (PCA) and interactive plotting with applications to visualising single-cell expression data

·      Clustering: hierarchical, k-means and mixture model clustering

·      Non-linear dimensionality reduction methods: GPLVM and t-SNE for non-linear dimensionality reduction and visualisation of single-cell data

Teaching and learning methods

The unit will be delivered as a succession of lectures (1 hour) and practical sessions (2 hours), where each lecture will introduce the theory behind a method/tool, and students will apply the method/tool to solve a particular biological problem in the practical.

Students will be assessed by completing three in-sessional online modules, one for each of the main sections of the unit, taking place during the last practical of the respective section. These modules will consist of a series of multiple choice questions and short questions, some of which will require a short piece of code to be written.

MSc students will additionally complete a research project after the end of the taught sessions. The project will require the construction of a model of a specific biological system and will be assessed through a written report in the form of a research article.


Knowledge and understanding

Students should: 

  • Understand essential mathematical concepts required for biological research.
  • Understand and apply differential equations and stochastic modelling of intracellular systems.
  • Understand and apply constraint-based modelling of metabolic systems.
  • Understand and apply network analysis and logical modelling of molecular systems.
  • Understand and apply evolutionary and ecological modelling.
  • Understand and apply Bayesian inference and machine learning.
  • Understand the applications and limitations of different modelling techniques and tools.

Intellectual skills

Students should: 

  • Develop problem-solving skills.
  • Construct models and design experiments to test biological hypotheses.

Practical skills

Students should: 

  • Use the Python language and develop models using the Jupyter Notebook system.
  • Construct models of signalling, regulatory, metabolic and environmental systems.
  • Use Bayesian methods to infer computational models from biological data.

Transferable skills and personal qualities

Students should: 

  • Develop computational skills.
  • Develop report writing skills.
  • Learn to communicate computational results.

Assessment methods

Assessment task


Weighting within unit (if relevant)

Three in-sessional online modules 

1 hour each


Research project report

6000 words



Feedback methods

Verbal feedback will be communicated during the practical sessions.

Written feedback will be communicated through annotated comments for each online assessment and report submission.

Recommended reading

Specific material will be provided with each lecture.

Study hours

Independent study hours
Independent study 0

Teaching staff

Staff member Role
Jean Marc Schwartz Unit coordinator

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