Caricamento in corso...
Inference in biological systems
01TYLPF
A.A. 2025/26
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 |
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Lecturers
| Teacher | Status | SSD | h.Les | h.Ex | h.Lab | h.Tut | Years teaching |
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Co-lectures
Context
| SSD | CFU | Activities | Area context | ING-INF/05 | 6 | B - Caratterizzanti | Discipline ingegneristiche |
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2024/25
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 offers an introduction to quantitative methods that enable the extraction of meaningful information from complex biological systems. These methods encompass the analysis of DNA, RNA, and protein sequences, the reconstruction of phylogenetic trees, and the study of cellular mechanisms through quantitative models of gene regulation, cellular compartmentalization, 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 gain familiarity with fundamental concepts in molecular biology, probabilistic approaches for sequence alignment and protein structure inference, as well as the application of physical models to understand cellular 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). During the computer labs, students will have the opportunity to apply theoretical concepts and algorithms to specific problem scenarios.
• 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
Libro di testo;
Text book;
E' possibile sostenere l’esame in anticipo rispetto all’acquisizione della frequenza
You can take this exam before attending the course
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;
To gain access to the oral exam, students will be required to individually complete and submit the results of the computing assignments assigned during the laboratory sessions. The oral exam entails a comprehensive presentation of a scientific work chosen by the student from the ones discussed in the course and uploaded in the 'Material' section of the course site. Alternatively, students may select other works related to the course topics, subject to approval by the instructors. The presentation will be followed by 2-3 broad questions covering the main topics covered in the lectures. The grading of the presentation contributes to one-third of the overall assessment, with a maximum grade of 30L. The purpose of the exam is to assess the student's comprehension of fundamental concepts in molecular biology, probabilistic approaches to sequence alignment, inference of protein structures, and physical modeling of cellular 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.