Introductory description

This introductory undergraduate module provides a comprehensive overview of computational biology, equipping students with essential skills and knowledge in utilizing computational tools to analyze biological data. The course is designed to integrate theoretical understanding with practical application, fostering a holistic approach to the burgeoning field of computational biology.

This module provides a solid foundation for students to navigate the interdisciplinary field of computational biology, preparing them for more advanced studies and real-world applications in biological research and data analysis.

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

15 lectures and 6 practical workshops to:

• Provide students with a comprehensive understanding of fundamental concepts in computational biology, including the measurement of cellular entities and processes.

• Equip students with the skills to navigate and analyze biological databases, emphasizing effective data retrieval and interpretation.

• Develop proficiency in sequence searching and matching techniques, enabling students to analyze biological sequences and draw meaningful comparisons.

• Introduce the principles of phylogenetics, enabling students to interpret evolutionary relationships and construct phylogenetic trees.

• Familiarize students with artificial intelligence and machine learning applications in computational biology, fostering an understanding of their role in data analysis.

• Enable students to predict and analyze protein structures using computational tools, emphasizing the importance of structural insights in biological research.

• Introduce students to the principles of metagenomics, transcriptomics, and proteomics, providing a holistic view of high-throughput biological data analysis.

• Develop a solid grounding in statistical methods applied to computational biology, enabling students to critically evaluate and interpret biological data.

• Introduce students to static and dynamic systems in biology, emphasizing network analysis and computational modelling.

• Instil basic programming skills in students, allowing them to apply computational methods to biological data analysis and interpretation.

• Provide practical workshops to reinforce theoretical concepts, allowing students hands-on experience in utilizing computational tools for biological data analysis.

• Foster an interdisciplinary perspective by integrating biological principles with computational methodologies, preparing students for collaborative research endeavours.

• Develop critical thinking skills in students, enabling them to assess and solve complex biological problems using computational approaches.

• Cultivate effective communication skills in conveying computational biology concepts, results, and insights both orally and in writing.

• Raise awareness of ethical considerations in computational biology research and data analysis, encouraging responsible conduct in scientific practice.

Learning outcomes

By the end of the module, students should be able to:

• Provide students with a comprehensive understanding of fundamental concepts in computational biology, including the measurement of cellular entities and processes.
• Equip students with the skills to navigate and analyze biological databases, emphasizing effective data retrieval and interpretation.
• Develop proficiency in sequence searching and matching techniques, enabling students to analyze biological sequences and draw meaningful comparisons.
• Introduce the principles of phylogenetics, enabling students to interpret evolutionary relationships and construct phylogenetic trees.
• Familiarize students with artificial intelligence and machine learning applications in computational biology, fostering an understanding of their role in data analysis.
• Enable students to predict and analyze protein structures using computational tools, emphasizing the importance of structural insights in biological research.
• Introduce students to the principles of metagenomics, transcriptomics, and proteomics, providing a holistic view of high-throughput biological data analysis.
• Develop a solid grounding in statistical methods applied to computational biology, enabling students to critically evaluate and interpret biological data.
• Introduce students to static and dynamic systems in biology, emphasizing network analysis and computational modelling.
• Provide practical workshops to reinforce theoretical concepts, allowing students hands-on experience in utilizing computational tools for biological data analysis.
• Foster an interdisciplinary perspective by integrating biological principles with computational methodologies, preparing students for collaborative research endeavours.
• Develop critical thinking skills in students, enabling them to assess and solve complex biological problems using computational approaches
• Cultivate effective communication skills in conveying computational biology concepts, results, and insights both orally and in writing

Subject specific skills

• Skill in measuring cellular entities and processes.
  • Proficiency in quantitative analysis methods for cellular measurements.
  • Competence in applying sequence searching and matching techniques to analyze biological sequences.
  • Proficiency in assessing the significance of sequence alignments and identifying homologous regions.
  • Skill in constructing and interpreting phylogenetic trees based on molecular data.
  • Competence in applying phylogenetic analysis methods to understand evolutionary relationships.
    • Proficiency in predicting protein structures using computational tools.
  • Skill in analyzing the implications of structural predictions for understanding biological systems.
  • Competence in understanding and analyzing data from metagenomics, transcriptomics, and proteomics.
  • Competence in assessing the reliability and significance of computational biology results statistically.
  • Ability to understand and analyze static and dynamic systems in biology.
  • Competence in writing basic programs for biological data analysis.
  • Ability to apply sequence searching and matching techniques in practical scenarios.
  • Proficiency in constructing phylogenetic trees using software tools.
  • Skill in interpreting and communicating phylogenetic data effectively.
  • Ability to apply machine learning techniques to analyze biological data.
  • Proficiency in predicting protein structures and analyzing their implications.
  • Skill in applying computational tools for structural biology analysis in practical scenarios.

Transferable skills

• Ability to navigate and query databases effectively.
• Skill in retrieving and interpreting relevant information from diverse databases.
• Knowledge of the applications of artificial intelligence and machine learning.
• Ability to apply machine learning techniques to analyze and interpret data.
• Skill in applying statistical methods to analyze and interpret biological data.
• Proficiency in utilizing network analysis for comprehending complex systems.
• Proficiency in using programming languages.
• Skill in interpreting and discussing results obtained through machine learning approaches.
• Competence in writing basic programs for biological data analysis.
• Proficiency in using programming languages relevant to computational biology.