Caricamento in corso...
Inference in biological systems
01TYLPF, 01TYLYR
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 |
|---|---|
| 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 |
|---|
2025/26
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 introduces quantitative methods for extracting meaningful insights from complex biological systems. Key topics include: Fundamental concepts in molecular biology, Quantitative modeling of gene regulation, Probabilistic sequence analysis (DNA, RNA, and proteins), Phylogenetic tree reconstruction, Physical modeling of pattern formation on cell membranes.
• 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 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.
• Physical modeling of gene regulatory circuits.
• Inference techniques: DNA, RNA, and protein sequence alignments, phylogeny reconstruction of phylogenetic trees.
• Physical modeling of pattern formation on cell membranes.
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
• B. Alberts et al., Molecular Biology of the Cell, Garland Science, 2015
• R. Phillips et al, Physical Biology of the Cell, Garland Science, 2012
• P. Nelson, Biological physics, Freeman, 2004
• M. Scott, Tutorial: Genetic circuits and noise, 2006 (https://www.math.uwaterloo.ca/~mscott/NoiseTutorial.pdf)
• R. Durbin et al., Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids, Cambridge Un. Press, 2002
• 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 qualify for the oral exam, students must individually complete and submit the computing assignments assigned during laboratory sessions. These assignments cover: a) modeling of regulatory circuits, b) DNA sequence analysis, and c) pattern formation on cell membranes. The exam consists of a presentation of a scientific work selected by the student. Students may chose from either works made available in the 'Material' section of the course site, or other relevant works (subject to instructor approval). Following the presentation, there will be 2-3 broad questions on key topics from 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 gene regulatory circuits and 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.