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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 | 5 |
Co-lectures
Context
| SSD | CFU | Activities | Area context | ING-INF/05 | 6 | B - Caratterizzanti | Discipline ingegneristiche |
|---|
2020/21
The course provides an introduction to molecular biology and to quantitative methods that can be used to extract information from complex biological systems, including the analysis of DNA, RNA and protein sequences, the reconstruction of phylogenetic trees, and the use of machine-learning techniques to analyze the structure of gene and protein networks.
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.
• understanding basics notions of molecular biology;
• understanding basic approaches to sequence alignment;
• being familiar with structural inference and maximum entropy techniques;
• being able to code simple sequence-alignment algorithms;
• being able to apply basic machine-learning methods to given genetic and biological problems.
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 (any language).
Basics of probability theory, principles of statistical physics, basic programming skills.
• Introduction to Molecular Biology: central dogma, DNA, RNA, proteins; gene regulation; metabolism; (20 h, C. Bosia)
• Hidden Markov models: from pairwise to multiple sequence alignments; inference in protein families; phylogeny reconstruction; RNA folding; (20 h, A. Gamba)
• Machine learning techniques: introduction to widely used methodologies (e.g. neural networks, random forests, convolution neural networks, Bayesian neural networks, recurrent neural networks, LSTM); application to genetic and biological studies; (20 h, E. Ficarra).
• 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 45 hours) and hands-on computer lab (approximately 15 hours), where the students will be asked to apply theoretical ideas and algorithms to selected problems.
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
• 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
• C.M. Bishop, Pattern Recognition and Machine Learning, Springer, 2011
• Suggested scientific publications
• 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
Modalità di esame: Prova orale obbligatoria;
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.
Modalità di esame: Prova orale obbligatoria;
La prova orale prevede 2 o 3 domande di carattere generale sugli argomenti del corso.
Exam: Compulsory oral exam;
The oral exam will consist of 2-3 broad questions on the main topics of the lectures.