Inverse Statistical Physics of Protein Sequences a Key Issues Review
Inference in biological systems
01TYLPF
A.A. 2020/21
Course Language
Inglese
Course caste
Master of scientific discipline-level of the Bologna process in Physics Of Complex Systems (Fisica Dei Sistemi Complessi) - Torino/Trieste/Parigi
Course structure
| Instruction | Hours |
|---|---|
| Lezioni | 60 |
Teachers
| Teacher | Status | SSD | h.Les | h.Ex | h.Lab | h.Tut | Years teaching |
|---|---|---|---|---|---|---|---|
| Gamba Andrea Antonio | Professore Associato | MAT/07 | xxx | 0 | 0 | 0 | 3 |
Instruction banana
Espandi Riduci
Instructor Status SSD h.Les h.Ex h.Lab h.Tut Bosia Carla Ricercatore a tempo det. Fifty.240/ten art.24-B FIS/02 30 0 0 0
Context
| SSD | CFU | Activities | Expanse context |
|---|---|---|---|
| ING-INF/05 | 6 | B - Caratterizzanti | Discipline ingegneristiche |
2020/21
Inference in biological systems
The course provides an introduction to molecular biology and to quantitative methods that tin be used to excerpt 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 factor and poly peptide networks.
Inference in biological systems
The course provides an introduction to quantitative methods that allow to extract information from complex biological systems. These include the assay of DNA, RNA and poly peptide sequences, the reconstruction of phylogenetic copse, and the study of the cell inner workings via quantitative models of gene regulation, cell compartimentalization and metabolism.
Inference in biological systems
� understanding nuts notions of molecular biological science; � agreement basic approaches to sequence alignment; � being familiar with structural inference and maximum entropy techniques; � being able to code uncomplicated sequence-alignment algorithms; � being able to apply basic machine-learning methods to given genetic and biological problems.
Inference in biological systems
Students volition acquire knowledge almost the basics of molecular biological science, standard approaches to sequence alignment and inference of poly peptide structures, physical modeling of cell functions.
Inference in biological systems
Basics of probability theory, principles of statistical physics, bones programming skills (whatever language).
Inference in biological systems
Basics of probability theory, principles of statistical physics, basic programming skills.
Inference in biological systems
� Introduction to Molecular Biology: central dogma, DNA, RNA, proteins; factor regulation; metabolism; (20 h, C. Bosia) � Hidden Markov models: from pairwise to multiple sequence alignments; inference in poly peptide families; phylogeny reconstruction; RNA folding; (20 h, A. Gamba) � Auto learning techniques: introduction to widely used methodologies (e.thousand. neural networks, random forests, convolution neural networks, Bayesian neural networks, recurrent neural networks, LSTM); application to genetic and biological studies; (xx h, E. Ficarra).
Inference in biological systems
� Elements of molecular biological science: Deoxyribonucleic acid, RNA, proteins. � Inference techniques: sequence alignments, structural inference, phylogeny reconstruction. � Physical biology of the jail cell: factor regulation, cell compartments, vesicle trafficking, metabolism.
Inference in biological systems
Inference in biological systems
Inference in biological systems
The grade alternates lectures on theoretical topics (approximately 45 hours) and easily-on computer lab (approximately 15 hours), where the students volition be asked to apply theoretical ideas and algorithms to selected problems.
Inference in biological systems
The course alternates lectures on theoretical topics (approximately 48 hours) and hands-on figurer lab (approximately 12 hours), where the students will exist invited to apply theoretical ideas and algorithms to selected problems.
Inference in biological systems
� Form 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 United nations. Press, 2002 � H.C. Nguyen, R. Zecchina and J. Berg, Changed statistical issues: from the changed Ising trouble to information science, Adv. Phys., 66 (2017) 197-261. � S. Cocco et al., Changed statistical physics of protein sequences: a central issues review, Rep. Progr. Phys. 81 (2018) 032601. � J. Felsenstein, Inferring phylogenies, Sinauer Assembly, 2004 � C.Grand. Bishop, Pattern Recognition and Machine Learning, Springer, 2011 � Suggested scientific publications
Inference in biological systems
� Class handouts � R. Phillips et al, Physical Biological science of the Cell, Garland Science, 2012 � P. Nelson, Biological physics, Freeman, 2004 � M. Kardar and L. Mirny, Statistical Physics in biology, MIT OpenCourseWare eight.592J / HST.452J � B. Alberts et al., Molecular Biology of the Cell, Garland Science, 2015 � R. Durbin et al., Biological Sequence Assay: Probabilistic Models of Proteins and Nucleic Acids, Cambridge Un. Press, 2002 � H.C. Nguyen, R. Zecchina and J. Berg, Changed statistical bug: from the inverse Ising problem to data scientific discipline, Adv. Phys., 66 (2017) 197-261. � S. Cocco et al., Changed statistical physics of protein sequences: a key problems review, Rep. Progr. Phys. 81 (2018) 032601. � J. Felsenstein, Inferring phylogenies, Sinauer Associates, 2004 � Suggested scientific publications
Inference in biological systems
Modalit� di esame: Prova orale obbligatoria;
Inference in biological systems
The oral exam will consist of 2-3 broad questions on the chief topics of the lectures.
Inference in biological systems
Test: Compulsory oral exam;
Inference in biological systems
The oral exam will consist of two-3 broad questions on the master topics of the lectures.
Inference in biological systems
Modalit� di esame: Prova orale obbligatoria;
Inference in biological systems
La prova orale prevede 2 o 3 domande di carattere generale sugli argomenti del corso.
Inference in biological systems
Exam: Compulsory oral exam;
Inference in biological systems
The oral exam will consist of 2-3 broad questions on the main topics of the lectures.
Esporta Word
Source: https://didattica.polito.it/pls/portal30/sviluppo.guide.visualizza?p_cod_ins=01TYLPF&p_a_acc=2021&p_lang=EN
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