Computational Methods for Biology

Knowledge and understanding. At the end of the course the students will be aware of concepts such as:

Protein and RNA structure, RNA-seq data, RIP and CLIP-seq

Algorithms for structure analysis;

Grand Challenges in Biology that can be addressed computationally;

Methods for the analysis of biological data: modelling and predictive approaches


Applying knowledge and understanding

At the end of the course the students will be able to:

Build the key elements to develop a software system for molecular biology;

Master the statistics behind the calculations and reproduce parts of the analysis


Communication skills. The class will interact with the teacher with the aim of learning the way to formulate 1)  the theoretical approach that leads to 2)  the building of a computational model for biological data. 


Learning skills. The formal language to formulate 1) a statistical hypothesis and 2) to build a model will be the key learning point of this class. All the soft skills associated with the learning will make the student able to apply this forma mentis to other scientific fields.


The original papers from which the information is going to be presented will be discussed critically and the tools to reproduce the key parts of the analysis will be acquired. The open dialogue with the students will ensure that they will be able to communicate what they learn. Slides and link to webpages will help them towards autonomous digesting of what is presented in the class.