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Inference in biological systems
01TYLPF
A.A. 2020/21
Course Language
Inglese
Degree programme(s)
Master of science-level of the Bologna process in Physics Of Complex Systems (Fisica Dei Sistemi Complessi) - Torino/Trieste/Parigi
Course structure
| Teaching | Hours |
|---|---|
| Lezioni | 60 |
Lecturers
| Teacher | Status | SSD | h.Les | h.Ex | h.Lab | h.Tut | Years teaching |
|---|---|---|---|---|---|---|---|
| Gamba Andrea Antonio | Professore Associato | MATH-04/A | 30 | 0 | 0 | 0 | 6 |
Co-lectures
Context
| SSD | CFU | Activities | Area context |
|---|---|---|---|
| ING-INF/05 | 6 | B - Caratterizzanti | Discipline ingegneristiche |
2020/21
The course provides an introduction to quantitative methods that allow to extract information from complex biological systems. These include the analysis of DNA, RNA and protein sequences, the reconstruction of phylogenetic trees, and the study of the cell inner workings via quantitative models of gene regulation, cell compartimentalization and metabolism.
Students will acquire knowledge about the basics of molecular biology, standard approaches to sequence alignment and inference of protein structures, physical modeling of cell functions.
Basics of probability theory, principles of statistical physics, basic programming skills.
• Elements of molecular biology: DNA, RNA, proteins.
• Inference techniques: sequence alignments, structural inference, phylogeny reconstruction.
• Physical biology of the cell: gene regulation, cell compartments, vesicle trafficking, metabolism.
The course alternates lectures on theoretical topics (approximately 48 hours) and hands-on computer lab (approximately 12 hours), where the students will be invited to apply theoretical ideas and algorithms to selected problems.
• Course handouts
• R. Phillips et al, Physical Biology of the Cell, Garland Science, 2012
• P. Nelson, Biological physics, Freeman, 2004
• M. Kardar and L. Mirny, Statistical Physics in biology, MIT OpenCourseWare 8.592J / HST.452J
• B. Alberts et al., Molecular Biology of the Cell, Garland Science, 2015
• R. Durbin et al., Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids, Cambridge Un. Press, 2002
• H.C. Nguyen, R. Zecchina and J. Berg, Inverse statistical problems: from the inverse Ising problem to data science, Adv. Phys., 66 (2017) 197-261.
• S. Cocco et al., Inverse statistical physics of protein sequences: a key issues review, Rep. Progr. Phys. 81 (2018) 032601.
• J. Felsenstein, Inferring phylogenies, Sinauer Associates, 2004
• Suggested scientific publications
Exam: Compulsory oral exam;
The oral exam will consist of 2-3 broad questions on the main topics of the lectures.
Exam: Compulsory oral exam;
The oral exam will consist of 2-3 broad questions on the main topics of the lectures.