Fundamentals of Computational Biology


This course will provide the students with a detailed introduction to the main areas of Computational Biology, with more enpghasis on topics related with Genomics and Evolution. It is expected that the students will obtain an informed perspective of Computational Biology and its diverse applications and will be in a good position to choose their preferred branch and elective courses in the second semester. Some of the topics covered in FCB will be expanded in more advanced branch-mandatory or elective courses.
• Acquire basic knowledge on genomics and NGS data processing (high throughput sequencing)
• To learn fundamental concepts of Evolutionary Biology
• To get well acquainted with the various aspects of Computational Biology and how they can contribute to clarify problems in Biology

General characterization





Responsible teacher

José Paulo Nunes de Sousa Sampaio


Weekly - 4

Total - Available soon

Teaching language



Available soon


Available soon

Teaching method

Available soon

Evaluation method

Two written individual tests, whose average grade represents 70% of the final grade. A paper presentation and discussion (groups of 3-4 students) and other evaluation elements such as practical questionnaires, whose average grade represents 30% of the final grade.

For the 70% component of the grade there is an opportunity for improvement in a single exam “recurso”.

Subject matter

1. Genomics
1.1. High-throughput sequencing technologies and applications
1.2. High-throughput sequencing pre-processing and data alignment.
1.3. Genome assembly and annotation.
1.4. Multi-Omics Resources
2. Molecular Evolution and Phylogenomics
2.1. Introduction to genomics.
2.2. Phylogenetic signal and models of sequence evolution.
2.3. Algorithms of phylogenetic inference.
3. Proteomics and Metabolomics
3.1. Technologies and applications
3.2. Describe analysis pipelines
3.3. Show databases.
4. Structural Bioinformatics
4.1 Introduction to structural bioinformatics
4.2 Protein structure prediction
4.3 Modeling biomolecular interactions
5. Molecular Dynamics Simulations of Biomolecules
5.1 Introduction to molecular mechanics/molecular dynamics methods
5.2 Molecular dynamics simulations using atomistic models
5.3 Molecular dynamics simulations using coarse grain models
6. Systems Biology
6.1 Introduction to systems biology
6.2 Metabolic models
6.3 Metabolic engineering