Study-unit QUANTITATIVE BIOLOGY
| Course name | Biology |
|---|---|
| Study-unit Code | 55191206 |
| Curriculum | Biomolecolare |
| Lecturer | Francesco Morena |
| Lecturers |
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| Hours |
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| CFU | 6 |
| Course Regulation | Coorte 2022 |
| Supplied | 2022/23 |
| Supplied other course regulation | |
| Learning activities | Affine/integrativa |
| Area | Attività formative affini o integrative |
| Sector | BIO/11 |
| Type of study-unit | Opzionale (Optional) |
| Type of learning activities | Attività formativa monodisciplinare |
| Language of instruction | Italian |
| Contents | Computational Biology for : (i) the quantitative and qualitative analysis of data, (ii) the investigation of the relationship between protein sequence and structure/function, and (iii) bioinformatics applications to molecular and industrial biotechnologies. |
| Reference texts | Fondamenti di bioinformatica. Manuela Helmer, Citterich,Fabrizio Ferrè, Giulio Pavesi. Zanichelli Bioinformatica. Stefano Pascarella, Alessandro Paiardini. Zanichelli TEACHING MATERIAL PROVIDED BY THE TEACHER Materiale elettronico da banche dati (https://pubmed.ncbi.nlm.nih.gov/; https://www.uniprot.org/blast/; https://scholar.google.com/) |
| Educational objectives | To introduce them to the value and potential of computational biology, as well as to provide them with the concepts and bioinformatics methodologies required for data analysis, such as Big Data, the prediction of three-dimensional protein structures, protein-protein interactions, and protein-RNA interactions. |
| Prerequisites | Molecular Biology, Biochemistry, Chemistry, Cellular Biology. |
| Teaching methods | Lectures will be made by using slides and interactive computer lessons. They will focus on fundamental methodology for data analysis, machine learning approaches, and structural biology of DNA and proteins. |
| Other information | It is planned a tutorial activity during the course and for students who request help for the preparation of the exam. The student reception dates are decided upon with the students. |
| Learning verification modality | Written and oral exam. The exam grade will be given by the average of the two tests. For information on support services for students with disabilities and / or SLD, visit the page http://www.unipg.it/disabilita-e-dsa |
| Extended program | Bioinformatics: general characteristics. Data and Database: archiving and main query systems. Principles of programming: Unix and Python. Statistical techniques and algorithms: Basic concepts on the calculation of probabilities. Typical probability distributions and statistical tests (t-test, ANOVA). Data science and data mining: an overview of the data, questions, and tools used by a data scientist. How to use R for effective data analysis. Database processing and data cleaning. Exploratory data analysis. Statistical inference. Regression models. Machine Learning approaches and main algorithms (KNN, Decision Trees, Random Forest, Neural Networks). Next Generation Sequencing (NGS) data analysis. Applications of bioinformatics to molecular and industrial biotechnologies: Analysis of genomic sequences and amino acid sequences. Search for genes and proteins. Search for patterns within a sequence (nucleotide, protein). Proteins and their evolution. Alignment of sequences and similarity matrices. Similarity searches in databases. Prediction of the three-dimensional structure of a protein. Models for homology and recognition of folding. Computational and visualization techniques for structural bioinformatics. Molecular complex prediction: Molecular Docking. Principles of Docking and Drug Designing methods. Applications of computational biology to analytical microscopy and images. |