- Programming knowledges
- Cell and molecular biology
- Biostatistics and computational modeling.
The course is divided into expository and laboratory classes. The first is dedicated to present the content in a more theoretical approach, without failing to include the active participation of students. The aim is to develop their’s reasoning ability and integration of knowledge and stimulate their critical thinking. Practical classes will enable the student to explore the acquired concepts. Those will follow a problem oriented approach by launching challenges that require knowledge integration, and wherever possible, the use of working groups and discussion.
Systematic comprehension of the main algorithms and tools used in Computational Biology. In particular, it is aim to focus on methods of analysis and annotation of sequences, application algorithms in proteomics and in the area of systems biology, and especially in genomic regulatory networks.
1. Introduction and Key Concepts
a. Computational Challenges in Computational Biology
b. Review of Molecular Biology concepts
c. Review of required programming skills
d. Databases and Bioinformatics libraries
2. Methods for sequence analysis
a. Global and local sequence aligment
b. Penalty functions and Heuristic methods
c. Multiple Sequence Alignments
d. Annotation of genomes
e. Genetic variations and diseases
3. Methods for analysis of gene expression
a. Microarrays and RNASeq
b. Clustering and classification
4. Prediction of protein structure
a. Ab initio methods
b. Fold Prediction and Threading methods
d. Modeling Post Translational Modifications
5. Systems Biology
a. Theoretical properties of Biological Networks
b. Discovery of patterns and signatures
c. Forecasting and simulation of biological networks (Regulatory and Protein networks)
d. Time series reconstruction
Laboratory work or Field work: 60.0%
Gusfield, Dan. Algorithms on Strings, Trees and Sequences: Computer Science and Computational Biology. Cambridge, UK: Cambridge University Press, 1997. ISBN: 0521585198.
Waterman, Michael. Introduction to Computational Biology: Maps, Sequences, and Genomes. Boca Raton, FL: CRC Press, 1995. ISBN: 0412993910.
Durbin, Richard, Graeme Mitchison, S. Eddy, A. Krogh, and G. Mitchison. Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids. Cambridge, UK: Cambridge University Press, 1997. ISBN: 0521629713.
Jones, Neil, and Pavel Pevzner. An Introduction to Bioinformatics Algorithms. Cambridge, MA: MIT Press , 2004. ISBN: 0262101068.