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Inference in biological systems

01TYLPF

A.A. 2022/23

Course Language

Inglese

Course degree

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
Teachers
Teacher Status SSD h.Les h.Ex h.Lab h.Tut Years teaching
Bosia Carla   Ricercatore a tempo det. L.240/10 art.24-B FIS/02 30 0 0 0 1
Teaching assistant
Espandi

Context
SSD CFU Activities Area context
ING-INF/05 6 B - Caratterizzanti Discipline ingegneristiche
2022/23
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 allowing to extract meaningful 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 become acquainted with basic notions of molecular biology, probabilistic approaches to sequence alignment and inference of protein structures, and 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;
Exam: Compulsory oral exam;
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.
Gli studenti e le studentesse con disabilità o con Disturbi Specifici di Apprendimento (DSA), oltre alla segnalazione tramite procedura informatizzata, sono invitati a comunicare anche direttamente al/la docente titolare dell'insegnamento, con un preavviso non inferiore ad una settimana dall'avvio della sessione d'esame, gli strumenti compensativi concordati con l'Unità Special Needs, al fine di permettere al/la docente la declinazione più idonea in riferimento alla specifica tipologia di esame.
Exam: Compulsory oral exam;
The oral test will consist of the in-depth presentation of a scientific work chosen by the Student among those discussed in the course and uploaded in the 'Material' section of the course site (or among other works otherwise related to the topics of the course), and of 2-3 broad questions on the main topics of the lectures. The grading of the presentation will contribute to 1/3 of the overall assessment (the maximum grade being 30L). The purpose of the exam will be to verify the Student's understanding of basic notions of molecular biology, probabilistic approaches to sequence alignment and inference of protein structures, and physical approaches to the modeling of cell functions.
In addition to the message sent by the online system, students with disabilities or Specific Learning Disorders (SLD) are invited to directly inform the professor in charge of the course about the special arrangements for the exam that have been agreed with the Special Needs Unit. The professor has to be informed at least one week before the beginning of the examination session in order to provide students with the most suitable arrangements for each specific type of exam.
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