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METHODS IN COMPUTATIONAL BIOLOGY

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METHODS IN COMPUTATIONAL BIOLOGY

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Academic year 2023/2024

Course ID
BIO0221 Pds GEN
Teachers
Ugo Ala
Luigi Bertolotti
Davide Marnetto
Degree course
[0101M22] Molecular Biotechnology
Year
2nd year
Teaching period
Second semester
Type
Elective
Credits/Recognition
6
Course disciplinary sector (SSD)
BIO/11 - molecular biology
BIO/13 - experimental biology
VET/05 - infectious diseases of domestic animals
Delivery
Formal authority
Language
English
Attendance
Obligatory
Type of examination
Written follewed by oral
Type of learning unit
corso
Prerequisites

Basic knowledge in molecular biology, cellular biology and mendelian genetics is required.


Sono necessarie conoscenze generali in biologia molecolare, biologia cellulare e genetica mendeliana.
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Sommario del corso

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Course objectives

This class is aimed at students that want to deepen their knowledge about computational and quantitative methods applied to biology. In particular, we will cover some topics traditionally investigated through modeling and quantitative analysis: system biology and network analysis on gene regulatory pathways, population genetics, epidemiology, phylogenetics, the study of the microbiome, and the analysis of specific modeling approaches using both statistical and Machine Learning techniques. These topics will be discussed in the context of the scientific method and studied through the analysis of methods presented in scientific papers. The aim is to inform the student about potential and applications of computational methods in biology research.

L’insegnamento si rivolge a chi e’ intenzionato ad approfondire lo studio di metodi computazionali e quantitativi applicati alla biologia. In particolare verranno affrontati alcuni argomenti tradizionalmente studiati tramite modellizzazione e analisi quantitativa: lo studio dei pathway regolatori con metodi di system biology e network analysis, la genetica delle popolazioni, l’epidemiologia, la filogenetica, lo studio del microbioma e l’analisi di specifici approcci di modellizzazione sia statistica sia con tecniche di Machine Learning. Queste tematiche verranno inserite nel contesto del metodo e del processo scientifico e approfondite attraverso l’analisi dei metodi presentati in articoli scientifici. L’obiettivo e’ di formare chi segue il corso su potenzialita’ e applicazioni dei metodi computazionali nella ricerca biologica.

 

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Results of learning outcomes

Knowledge of some of the most important computational methods used in gene regulatory pathway analysis, microbiome, population genetics, epidemiology, phylogenetics, including the most known models used to represent phenomena in these domains.  Understanding of the scope in which the concepts and methods above have been applied, also drawing examples from landmark literature papers. Capability to independently judge the potential of computational and mathematical methods in biology, and on the scientific method in general. Ability to read and learn from scientific literature about the covered topics and present the research content to peers. 

Conoscenza di alcuni dei più importanti metodi computazionali in uso nell’analisi dei pathway di regolazione genica, microbioma, genetica delle popolazioni, epidemiologia e filogenetica, inclusi i modelli più comunemente utilizzati per rappresentare i fenomeni relativi a questi campi. Comprensione di come i concetti e metodi di cui sopra siano stati applicati nella ricerca, prendendo spunto da importanti paper scientifici. Capacità di formare un proprio giudizio indipendente sul potenziale dei metodi computazionali e matematici nella biologia, e sul metodo scientifico in generale. Capacità di leggere ed imparare dalla letteratura scientifica relativa agli argomenti presentati, e presentarne i risultati della ricerca ivi contenuti.           

 

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Program

Part I

Methods to analyze genetic variation

  • Introduction to genetic variation: allele and genotype frequency, nucleotide diversity, heterozygosity, Hardy-Weinberg Equilibrium
  • Genetic Drift and its implications: the Wright-Fisher model
  • Population structure: isolation by distance, Fst and Wahlund effect 
  • Visualizing genetic diversity: Principal Component Analysis
  • Recombination, Linkage Disequilibrium and haplotypes
  • computational methods in the analysis of Archaic human genomes

 

Part II

Molecular epidemiology and Evolutionary inferences

  • Evolution of organisms
  • Mutation and selection
  • Phylogenetic and evolutionary methods
  • Molecular epidemiology of infectious diseases


Part III

Modeling in science and Scientific method

  • Scientific method and its evolution
  • Experimental design
  • Computational modeling in life sciences
  • Mathematical models in biotechnology: e.g. ceRNA cross-talk
  • Microbiome analysis
  • Machine Learning approaches in biology 
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Course delivery

Formal lessons, seminars and discussion of scientific papers.

Lezioni frontali, seminari e discussione di articoli scientifici.

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Learning assessment methods

Multiple choice quiz, followed by an oral presentation on one of the the topics covered by the lectures.

Quiz a risposta multipla, seguito da una presentazione orale su uno dei temi coperti a lezione.

Suggested readings and bibliography

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Molecular evolution: A phylogenetic approach

Autore: Page RDM and Holmes EC

Casa editrice: Blackwell Science

ISBN: 978-0-865-42889-8

 

Artificial Intelligence in Bioinformatics - From Omics Analysis to Deep Learning and Network Mining

Autori: Mario Cannataro, Pietro Hiram Guzzi, Giuseppe Agapito, Chiara Zucco, Marianna Milano

Casa editrice: Elsevier

ISBN: 978-0-12-822952-1

 



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Teaching Modules

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    Enrollment opening date
    01/03/2020 at 00:00
    Enrollment closing date
    31/12/2022 at 23:55
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