Servizi per la didattica
PORTALE DELLA DIDATTICA

Inference in biological systems

01TYLPF

A.A. 2019/20

Course Language

English

Course degree

Master of science-level of the Bologna process in Physics Of Complex Systems - Torino/Trieste/Parigi

Course structure
Teaching Hours
Lezioni 60
Teachers
Teacher Status SSD h.Les h.Ex h.Lab h.Tut Years teaching
Gamba Andrea Antonio   Professore Associato MAT/07 20 0 0 0 1
Teaching assistant
Espandi

Context
SSD CFU Activities Area context
ING-INF/05 6 B - Caratterizzanti Discipline ingegneristiche
2019/20
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 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.
• 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.
• 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.
Basics of probability theory, principles of statistical physics, basic programming skills (any language).
Basics of probability theory, principles of statistical physics, basic programming skills (any language).
• 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).
• 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).
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 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.
• 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 • 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
Modalitΰ di esame: prova orale obbligatoria; progetto individuale;
The oral test will consist of 2-3 broad questions on the main topics of the lectures. The individual project will consist of the application of computational and machine-learning techniques to a given biological problem, and will be presented as ppt slides in an oral discussion. The grading of the project will contribute to 1/3 of the overall assessment.
Exam: compulsory oral exam; individual project;
The oral test will consist of 2-3 broad questions on the main topics of the lectures. The individual project will consist of the application of computational and machine-learning techniques to a given biological problem, and will be presented as ppt slides in an oral discussion. The grading of the project will contribute to 1/3 of the overall assessment.


© Politecnico di Torino
Corso Duca degli Abruzzi, 24 - 10129 Torino, ITALY
m@il