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1049265 -
BIOETHICS
(obiettivi)
The Bioethics course provides the students with tools to understand, discuss, present and address ethical issues relevant to bioinformatics, at the intersection between biological and technological sciences.
In order to respond to the course requirements, the students will also acquire general skills such as doing a bibliographic research on academic databases, speaking and arguing in public by using specialized bioethical concepts and theories, and writing a little paper in an academic format including a bibliography.
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SIRGIOVANNI ELISABETTA
( programma)
Il corso copre i seguenti argomenti: introduzione generale all'etica per bioscienziati; fondamenti di logica e psicologia del ragionamento per la bioetica; storia della bioetica; teoria etiche per la bioetica, naturalismo/anti-naturalismo e pragmatismo in bioetica; approcci su principi e su casi; etica della ricerca e integrità della ricerca; etica della genetica; potenziamento genetico e cerebrale; biobanche e uso dei dati genetici nei contesti giuridici; etica delle information technologies e nuove sfide dall'Intelligenza Artificiale; neuroetica; etica delle neurotecnologie; basi biologiche della moralità.
Esempi, casi studio e attività pratiche riguarderanno argomenti popolari nei dibattiti bioetici contemporanei (cellule staminali, clonazione, GMOs, fine vita, vaccini, privacy (genetica), sperimentazione animale, protesi, riproduzione assistita, ecc.).
Alcune questioni innovative nelle biotecnologie verranno presentate e discusse da un punto di vista etico: organoidi cerebrali, macchine morali, chimere, interfaccia uomo-macchina, editing genetico, ecc.
 Testi richiesti:
- slide del docente
- lista bibliografica obbligatoria (vedere sotto).
Le letture (articoli, capitoli di libro in lingua inglese) vengono fornite agli studenti prima, durante o al termine delle lezioni.
Le slide e tutta la bibliografia obbligatoria viene caricata dal docente in pdf sulla pagina E-learning Sapienza (Moodle) al termine di ogni lezione.
(Date degli appelli d'esame)
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6
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MED/02
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48
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-
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-
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-
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Attività formative caratterizzanti
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ENG |
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1049258 -
MOLECULAR BIOLOGY AND GENOMICS
(obiettivi)
General skills
The new generation of sequencing technologies has provided unforeseen chances for high-throughput functional genomic studies. These technologies have been applied in a variety of contexts, including whole-genome sequencing, discovery of transcription factor binding sites, mapping out the DNA accessibility and RNA expression profiling. Intriguingly, recent annotation efforts focused on the discovery of novel noncoding RNA genes and regulatory elements that control temporal or spatial gene expression along cell differentiation. The course of Molecular Biology and Genomics is designed to provide students with an introduction to the structure and function of genomes and transcripts in humans and in other model organisms. Topics discussed will include modern genome sequencing technologies, as well the recent in silico and in vivo approaches used for functional genomics and for the functional role of emerging non-coding RNA classes (practical examples taken from recent literature will be used). The course also provides students with basic knowledge for accessing browsers and public databases for the analysis of gene expression data, GO and miRNA target prediction software.
By the end of the course, students will be able to apply the acquired knowledge to the study of the basic mechanisms of gene expression, as well as of complex processes such as development, cell division and differentiation, and to exploit them for a practical use in both basic and applied research.
Specific skills
The students who have passed the exam will be able to know and to understand (acquired knowledge)
- the origin and the maintenance of the biological complexity;
- the structure and function of the genome in humans and in the main model systems;
- the problems and technologies of genome-wide analyses applied to biological processes;
- the influence of the modern sequencing technologies for a better description and for the study of transcriptome dynamics in humans and in the main model systems;
- the network of interactions between the biological molecules in the mechanisms of regulation of gene expression.
The students who have passed the exam will be able to (acquired expertise):
- interpret the biological phenomena in a multi-scale and multi-factorial context;
- interpret the results of genomic studies and to discriminate which techniques to apply according to the different problems to be dealt with in the genomic field;
- report works already present in the literature in the form of an oral presentation.
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BALLARINO MONICA
( programma)
Il corso prevede 48 ore di lezioni teoriche, esercizi al computer e seminari.
Biologia Molecolare (24 ore)
Introduzione al Genoma ed alla complessità funzionale dei sistemi eucarioti. Cenni sui meccanismi di maturazione dell’RNA: capping, splicing e poliadenilazione. Splicing alternativo e regolazione dello splicing; Elementi di terapia genica (Distrofia Muscolare di Duchenne); Esporto e stabilità dell’RNA; Struttura della cromatina e modificazioni istoniche; Il modello dell’mRNA Factory: RNA non codificanti (ncRNA); RNA interference: scoperta, meccanismi di azione e fattori coinvolti; microRNA (miRNA): biogenesi, meccanismi di azione e ruolo nel differenziamento e nella proliferazione; long non-coding RNA (lncRNA); RNA circolari RNA (circRNAs); RNAi e cromatina.
Genomica Strutturale (12 ore).
Evoluzione del concetto di gene: dalle teorie di Darwin sull’ereditarietà all’Era post-genomica; il Progetto Genoma Umano; il Progetto Encode.
Tecnologie di sequenziamento del DNA e del genoma (Sanger, Maxam-Gilbert, BAC). Sequenziamento automatizzato e Next-Generation Sequencing (NGS). La tecnologia "Illumina". Anatomia dei genomi, grandezza del genoma e numero di geni. Il DNA non codificante (ncDNA).
Tecnologie di sequenziamento dell’RNA. Il trascrittoma e l’analisi high-throughput dell’espressione genica; WET lab e DRYLAB applicati all’analisi di un tipico esperimento di RNA-seq (FASTQ, PHRED quality score, FASTQC). Identification de-novo e analisi dell’espressione differenziale di mRNA e RNAs non-codificanti (ncRNA).
Approcci genetici e biochimici per lo studio dell’interattoma. Il sistema del “two-hybrid”; Immunoprecipitazione, Co-immunoprecipitazione, Protein tagging e saggi di pull-down. Studio delle interazioni tra PROTEINE (ChIP) e RNA (ChIRP) con la cromatina: le tecniche di i) Chromatin Immunoprecipitation (ChIP) e di ii) Chromatin Isolation by RNA Purification (ChIRP). Identificazione e studio di domini topologici (TADs). 3C, 5C, Hi-C e ChIA-PET.
Genomica Funzionale (6 ore).
Approcci di genetica Forward e Reverse. Sistemi modello: pros e cons. Editing Genomico: il sistema CRISPR/CAS9. Screening omici basati su RNAi e CRISPR. Analisi funzionale di mRNA e non-coding RNA (ncRNA) in sistemi di differenziamento muscolari e neuronali (topo e uomo).
Risorse web per l’analisi in silico di dati genomici (6 ore).
Esercizi in aula informatizzata. Elaborazione ed interpretazione di dati genomici. Database biologici (primari, secondari, specializzati); il formato FLAT; NCBI: accesso via Taxonomy, Gene; Protein Map Viewer, Pubmed and Pubmed MeSH, Entrez; Genome browsers (UCSC), Ensembl, DDBJ, UniProt; Elementi di gene ontology (GO). miRBase, TargetScan e miRTARBASE.
Un libro di testo scelto tra i seguenti:
R.F. Weaver Molecular Biology, Mc Graw Hill, V Edition
Watson J. D et al Molecular Biology of the Gene, Zanichelli VII Edition
Arthur M. Lesk 2009, “INTRODUZIONE ALLA GENOMICA”, Zanichelli
Greg Gibson, Spencer V. Muse 2004, “INTRODUZIONE ALLA GENOMICA”, Zanichelli
Tom Strachan, Judith Goodship, Patrick Chinnery 2016, “GENETICA E GENOMICA”, Zanichelli
Per un immediato aggiornamento dei testi o del materiale didattico distribuiti dai docenti consultare la pagina web del corso: https://elearning2.uniroma1.it
(Date degli appelli d'esame)
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6
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BIO/11
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48
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-
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-
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-
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Attività formative caratterizzanti
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ENG |
Gruppo opzionale:
Gruppo OPZIONALE - (visualizza)
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12
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1049267 -
MODELLING AND SIMULATION OF BIOMOLECULAR DYNAMICAL SYSTEMS
(obiettivi)
This course aims to provide students with a practical and hands-on experience with common modeling and simulation tools in molecular biology. It would be expected that after completing this course a student would be able to model and simulate using Matlab a biomolecular systems like, for example, a gene regulatory network using the appropriate methodology. Further, students will understand the basic theory behind these modeling tecniques and critically analyze the results of their analysis.
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FARINA LORENZO
( programma)
Introduzione alla modellistica matematica. Elementi di teoria dei sistemi. Modelli di interazione genica e reti. Identificazione di geni regolati dal ciclo cellulare.
Materiale didattico fornito dal docente
(Date degli appelli d'esame)
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6
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ING-INF/06
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48
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-
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-
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-
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Attività formative affini ed integrative
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ENG |
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1049268 -
SIGNAL PROCESSING AND INFORMATION THEORY
(obiettivi)
The course consists in a introduction to signal processing fundamentals. It is intended to provide an understanding and working familiarity with the fundamentals of signal processing and is suitable for a wide range of people involved with and/or interested in signal processing applications. Its goals are to enable students to apply digital signal processing concepts to their own field of interest, to make it possible for them to read the technical literature on digital signal processing, and to provide the background for the study of more advanced topics and applications.
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COLONNESE STEFANIA
( programma)
Rappresentazione, misura ed estrazione dell’informazione contenuta nei dati. Sorgenti di informazioni, entropia, informazione mutua. Fondamenti di codifica di canale. Decodifica di codici senza memoria e con memoria. Acquisizione di segnali ed immagini, campionamento e quantizzazione. Il rumore di osservazione. Filtraggio. Rappresentazione di segnali ed immagini nel dominio di Fourier. Rappresentazione compatta di segnali ed immagini: Trasformata di Karounen Loewe, analisi a componenti principali (PCA). Principi di rivelazione e stima di segnale. Riconoscimento di pattern mediante correlazione per stima di ritardo e spostamento.
DISPENSE: DISPONIBILI SULLA PAGINA MOODLE DEL CORSO
J.G. Proakis, D.G. Manolakis, "Digital Signal Processing", Prentice Hall. (in inglese).
G. Scarano, "Elaborazione statistica dei segnali", Università La Sapienza (in italiano).
(Date degli appelli d'esame)
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6
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ING-INF/03
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48
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-
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-
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-
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Attività formative affini ed integrative
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ENG |
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1049269 -
ALGORITHMS
(obiettivi)
At the end of the course the student will have developed a basic understanding of the algorithmic principles at the foundation of bioinformatics, and will have acquired the ability to choose the appropriate algorithmic tools for solving problems arising in bioinformatics.
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RODOLA' EMANUELE
( programma)
- Algoritmi e strutture dati: generalità ed esempi. Introduzione alla nozione di costo (tempo e spazio di memoria).
- Notazioni asintotiche per le funzioni di costo e metodi di analisi (caso peggiore, medio, migliore).
- Metodi di analisi di algoritmi ricorsivi: albero della ricorsione, iterazione, sostituzione, Master Theorem.
- Tipi di dato astratto (pile, code, alberi) e loro implementazioni. Algoritmi di visita di un albero.
- Algoritmi di ordinamento incrementali (descrizione, implementazione e analisi).
- Grafi e loro rappresentazione. Algoritmi di visita (descrizione, implementazione e costo).
- Tecnica algoritmica greedy. Minimo albero ricoprente e rispettivo calcolo basato su algoritmo greedy. Algoritmi di Kruskal e Prim.
- Cammini minimi su grafi e relativi algoritmi (descrizione, implementazione e analisi): calcolo delle distanze, calcolo dei cammini minimi a sorgente singola su grafi aciclici, algoritmo di Dijkstra.
- Clustering gerarchico e k-means.
- Algoritmi di bioinformatica.
- Principi di deep learning su immagini e grafi.
Il materiale di studio viene fornito completamente sul sito di riferimento del corso.
Come materiale integrativo vengono suggeriti di volta in volta capitoli selezionati dal seguente testo:
T.H. Cormen, C.Papadimitriou, U. Vazirani. Introduzione agli algoritmi
(Date degli appelli d'esame)
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6
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INF/01
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48
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-
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-
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-
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Attività formative affini ed integrative
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ENG |
|
1049270 -
COMPLEX BIOMOLECULAR NETWORKS
(obiettivi)
The course aims to provide basics concepts and tools for complex networks analysis. The attendee will be able to apply complex networks concepts to biological networks and explore the underlying process and molecular related issues.
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MARCHETTI SPACCAMELA ALBERTO
( programma)
1.Algorithms and Complexity
Computational complexity of algorithms and programs - Fast versus Slow Algorithms - Big-O Notation - Algorithm Design Techniques – Tractable versus Intractable Problems - NP-complete problems.
2. Algoritmic Techniques
Greedy Algorithms - Dynamic Programming - Exhaustive Search - Branch-and-Bound Algorithms - Heuristics – Branch and Bound (these techniques are introduced when they are applied in solving problems considered in sections 4-6).
3. Graph Algorithms
Basic definition on Graphs – Memory representation – Connectivity - Shortest path, Eulerian path, Traveling Salesmna problem, Hamiltonia path.
4. DNA Assembling
Shortest Superstring Problem - DNA Arrays as an Alternative Sequencing Technique - Sequencing by Hybridization - SBH as a Hamiltonian Path Problem - SBH as an Eulerian Path Problem - Fragment Assembly in DNA Sequencing – Overlap-Layout-Consensus assembly- De Bruijn graph assembly – Scaffolding.
5. HiddenMarkovModels
CG-Islands and the “Fair Bet Casino” – Definition of Hidden Markov Models - Decoding Algorithm - HMM Parameter Estimation.
6. Clustering and Trees
Gene Expression Analysis - Hierarchical Clustering - k-Means Clustering - Clustering and Corrupted Cliques - Evolutionary Trees - Distance-Based Tree Reconstruction - Reconstructing Trees from Additive Matrices - Evolutionary Trees and Hierarchical Clustering - Character-Based Tree Reconstruction - Small Parsimony Problem - Large Parsimony Problem.
7. Advanced topics in large networks and phylogenetic recontructions.
Libro di Testo
Neil C. Jones, Pavel A. Pevzner: An Introduction to Bioinformatics Algorithms, MIT Press
(capp. 2,5.1-5.3, 6, 8,10, 11)
Altri testi distribuiti in classe e disponibili sulla piattaforma e-learning
Martin Vingron, Jens Stoye, Hannes Luz Algorithms for Phylogenetic Reconstructions, Notes
Slides delle lezioni
Materiale (suggerito) per i seminari
(Date degli appelli d'esame)
|
6
|
ING-INF/05
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48
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-
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-
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-
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Attività formative affini ed integrative
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ENG |
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1049271 -
PLANT FUNCTIONAL GENOMICS
(obiettivi)
General skills
The course of Plant Functional Genomics aims to provide advanced knowledge of plant genomes, with particular attention to the use of this knowledge in order to identify new genes and determine their function.
Specific skills
A) Knowledge and understanding
To acquire detailed knowledge of:
- methods of analysis of plant genomes and the peculiar difficulties related to these organisms (polyploidy, repetitive DNA);
- the structure of the plant nuclear and plastidic genomes;
- genome comparison methods, with particular attention to the identification of homologous, orthologue and paralogue genes;
- methods of transfer of information on genes from model species to species of agricultural interest;
- methods of integration of genomics and gene expression analysis data;
- methods and approaches to study of the function of genes in model species and in crops, with approaches of direct and reverse genetics;
- methods of transient and stable transformation;
- identification and use of molecular markers in plant genetics;
- use of genomic data to identify genes involved in agronomic traits.
- the mechanisms of epigenetic regulation in plants and the methods to study them;
- silencing and "genome editing" mechanisms in plant organisms;
B) Applying knowledge and understanding
- design experiments aimed at defining the function of a gene through reverse genetic approaches;
- design genetic screening in plant model systems and outline the main lines of identification of a mutation;
- understand and critically discuss the different approaches used to alter the expression of a gene in a plant and choose the most appropriate one according to the needs and the experimental model;
- designing the engineering of new traits in plant organisms.
C) Making judgements
- Critical judgment skills, through the study of reviews and scientific articles on key aspects of the field and in-depth discussions;
- Ability to evaluate the correctness and scientific rigor in the topics related to the topics covered by the course.
D) Communication skills
- Acquisition of adequate skills and useful tools for communication in Italian and in foreign languages (English), through the use of graphic and formal languages, with particular regard to the scientific language.
E) Learning skills
- Ability to interpret and deepen knowledge;
- Ability to use cognitive tools for continuous updating of knowledge;
- Ability to compare for the consolidation and improvement of knowledge.
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DE LORENZO GIULIA
( programma)
Module I (3 cfu)
INTRODUCTORY CONCEPTS
- Plant organisms. The plant cell. Nuclear, mitochondrial and plastid genome of plants. Reproduction of plant organisms.
-Basic concepts of genetics. Genetic polymorphisms. Genetic and molecular markers. Genetic and physical maps. Backcross and inbreeding. Heterosis. Recombinant Inbred Lines. Quantitative Trait Loci.
ANALYSIS OF PLANT GENOMES AND USE OF GENOMICS IN PLANT BREEDING
- Evolution of plant genomes. Repetitive DNA; polyploidy; sequencing of genomes; the genome of Arabidopsis thaliana.
- Comparative genomics; identification of orthologous genes in different species, synteny. Databases of plant genomes. Genome-wide association studies. Development of new plant varieties through marker assisted selection.
PLANT EPIGENETICS AND EPIGENOMICS
- Epigenetic modifications: DNA methylation; histone changes; Non-coding RNAs: siRNA, microRNA; transcriptional and post-transcriptional gene silencing; role of silencing in maintaining the integrity of the genome.
- Generation and maintenance of epigenetic modifications; epialleles; large-scale analysis of the epigenome; transposons.
Module II (3 cfu)
DIRECT AND REVERSE GENETICS
- Analysis of gene function: direct and reverse genetic approaches in plants. Mutagenesis by insertion in plants. Overexpression and silencing. Map-based and deep sequence-based identification of mutations. (4 h).
- From mutants to transgenic plants of biotechnological interest. Natural genetic variability as a source of characteristics of biotechnological interest. TILLING and ecoTILLING.. Genome-Wide Association Studies to identify regions of DNA important for influencing a desired phenotype. (4 h)
- Genome editing: CRISPR/Cas9 (2 h)
- Chemical genomics in plant biology. "Click" chemistry. Single cell genomics. (2 h)
TRANSCRIPTOME AND INTERACTOME ANALYSES IN PLANTS
- ESTs, cDNA-AFLPs, Microarrays, DNA Chips, SAGE. RNA sequencing, sequence analysis and their applications (4 h)
-Transcriptomic analysis on a single cell or individual tissues: laser capture; FACS on protoplasts labeled with fluorescent proteins. Databases of plant transcriptomes. (4 h)
- Examples of networks and correlation analysis of the expression of multiple genes. (2 h)
- Interactomics (2 h)
- Teaching material provided through E-Learning (articles, reviews, lectures slides);
- Biotecnologie Sostenibili - Galbiati et al. – EDAGRICOLE
- Biotecnologie e Genomica delle Piante - Rao e Leone - Idelson-Gnocchi (acronimo BGP)
- Biochemistry and Molecular Biology of Plants, - Buchanan, Gruissem e Jones, 2° ed., Wiley Blackwell
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6
|
BIO/04
|
48
|
-
|
-
|
-
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Attività formative affini ed integrative
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ENG |
|
1049272 -
PRINCIPLES OF GENERAL PATHOLOGY
(obiettivi)
OVERALL OBJECTIVES:
The general aim of this course is to give to the student the basic knowledge concerning:
1) the fundamental molecular mechanisms that regulate human disease processes;
2) how recent biotechnological advances and next generation sequencing approaches can be integrated in the characterization of the pathologies;
3) the different types of genetically modified murine models for the study and the cure of human pathologies;
4) the main bioinformatic tools in this field.
SPECIFIC OBJECTIVES:
At the end of the course the student will be able, by applying the knowledge acquired during the course:
1) perform bibliographic searches on international databases; 2) perform data mining on most widely used databases
3) integrate notions acquired during lectures and international scientific literature; 4) understand the principal mechanisms of most common pathologies and how these can be studied with the aid of next generation sequencing approach; 4) to hypothesize the generation of animal models for the pathophysiological study of human diseases and for the identification of therapeutic targets; 5) to critically evaluate the best bioinformatic tools for achieving these results or alternatively, to pursue the replacement of animal experimentation.
KNOWLEDGE AND UNDERSTANDING:
At the end of the course the student woud be able to know:
Concept and causes of alteration in the cell, from homeostasis to disease; Next generation Sequencing (NGS) technique used for different applications, from the study of genomes, chromatin accessibility and trascriptome; Molecular and cellular pathology of cancer; Pathogenetic mechanisms of non-coding RNAs; Stem cells: embryonic stem cells, tissue stem cells and cancer stem cells; advantages and limits of genetically modified murine models; the basic technical and bioinformatic tools concerning the generation, the characterization and the maintenance of murine colonies; the specific traits of the different types of genetically modified murine models, both conventional and conditional; the bioinformatic tools to potentially validate mouse models of human diseases.
APPLYING KNOWLEDGE AND UNDERSTANDING:
To apply the acquired knowledge to integrate information gathered from different sources (datasets, material obtained during lectures, and scientific literature); to understand different mechanisms that contribute to pathogenesis and how these mechanisms can be studied, with particular focus on NGS-based technologies; to discriminate advantages and limits in generating and using different types of genetically modified murine models for the study and the cure of human pathologies; to critically evaluate the bioinformatic means available to pursue these aims.
MAKING JUDGEMENTS:
The student will be able to link the different types of notions acquired during the course to elaborate the most appropriate experimental strategy based on bioinformatic tools and able to solve research problems in the field of general pathology.
COMMUNICATION:
The student will be able to perform oral presentation of scientific data, with the aid of Power Point software.
Notions acquired during the course will be evaluated during the exam.
LIFELONG LEARNING SKILLS:
The notions, the tools and the notes available during the course will contribute in developing the competence for the autonomous study and continuous updating in the field of the Bioinformatics applied to the general pathology.
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CAMPESE ANTONIO FRANCESCO
( programma)
-Scopi, vantaggi e limiti della generazione e dell’utilizzo dei modelli murini geneticamente modificati per lo studio e la cura delle patologie umane (2 ore)
-Aspetti legislativi della sperimentazione animale (Regola delle 3R, dLGS 26/2014) e relative ricadute sulla bioinformatica: i metodi alternativi; i protocolli di sperimentazione animale; i registri e i database informatici (3 ore);
-Mantenimento delle colonie di animali geneticamente modificati e relativi strumenti bioinformatici (2 ore)
-Metodologie e strumenti informatici per la generazione di modelli murini geneticamente modificati ‘convenzionali’: topi transgenici e topi ‘knock-out’ (4 ore) e ‘condizionali’ e/o inducibili: il sistema Cre/LoxP; topi ‘knock-in’ condizionali; geni ‘reporter’ (4 ore)
-Esempi di modelli murini per lo studio di patologie umane: leucemie, tumori solidi, malattie autoimmuni (2 ore)
-Strumenti bioinformatici per la potenziale validazione di modelli murini di patologia umana (2 ore)
-Pricipi di citofluorimetria e di applicazioni informatiche relative (3 ore)
-Seminari sulle applicazioni bioinformatiche nell'utilizzo dei modelli murini di patologie umane (2 ore)
Materiale didattico e pubblicazioni scientifiche segnalate durante il corso.
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PO AGNESE
( programma)
The topics will be:
1) Getting started and general aspects of molecular and cellular pathology.
Concept and causes of alteration in the cell, cellular and tissue homeostasis, Cellular damage: causes, molecular mechanisms, responses and adaptations, Cell death and its manifestations: necrosis and apoptosis
2) Next generation Sequencing (NGS): introduction to NGS tecniques. Illumina platform, Ion Torrent. Applications: Genome Sequencing, Whole exome sequencing, Targeted sequencing, NGS-based comprehensive genomics: DNA methylation, chromatin accessibility and modifications.
Analysis of NGS derived data: bioinformatics analysis, analysis of the transcriptome and of the mirnome. Examples of the use of NGS for biomedical research. Data mining on available databases.
3) Molecular and cellular pathology of cancer: Definition and classification of tumors, Stages of tumor progression: initiation, promotion, progression, invasiveness and metastasis, Dominant and recessive mutations: oncogenes and tumor suppressor, molecular basis of neoplastic transformation, invasiveness, metastasization, Identification of tumor markers. Significance of tumor markers in the prevention and staging/classification of tumors
4) Chronic degenerative diseases: diabetes mellitus, metabolic diseases
5) Pathogenetic mechanisms of non-coding RNAs: microRNAs and long non-coding RNAs.
microRNA: biogenesis, deregulation mechanisms in pathologies, circulating microRNAs; long non-coding RNA: biogenesis, mechanisms of action, examples of long non-coding deregulated in pathologies. Circular RNAs.
6) Stem cells: embryonic stem cells, adult stem cells. Therapeutic applications of stem cells. Cancer stem cells: biological features and epigenetic regulation.
7) Circulating nucleic acids as markers for diseases. The discovery, challenges an approaches for the identification and evaluation of circulating RNAs and DNAs.
notes and scientific papers given by the teacher
(Date degli appelli d'esame)
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6
|
MED/46
|
48
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
1049273 -
OPTIMIZATION METHODS FOR COMPUTATIONAL BIOLOGY
(obiettivi)
The course gives an introduction on the basic tools for mathematical modeling and solving decision and optimization problems that arise in bioinformatics. At the end of the course, students should be able to recognize such problems, build mathematical models for them, and solve them using a number of modeling techniques and solution algorithms, also by means of specific software tools.
Expected learning outcomes (Dublin Descriptors):
1. Understand all basic mathematical aspects of solving linear, linear integer, and nonlinear convex optimization problems. Understand main modeling techniques in mathematical programming.
2. Be able to develop an optimization model from a decision problem with quantitative data. Be able to select and use suitable software to solve such model.
3. Be able to identify weaknesses of optimization models and limits of numerical solvers (students develop these abilities also during any practical test of the course when they practically solve relevant decision problems).
4. Be able to describe any aspect of a mathematical program and of the main algorithms for the solution of linear, linear integer, and nonlinear programs (students develop these abilities also during any practical test of the course when they practically solve relevant decision problems by working in groups).
5. Get mathematical basis to self-study solution techniques for complex mathematical programs such as nonconvex and multi-objective programming.
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BRUNI RENATO
( programma)
1 Introduction to Optimization
2 Using Optimization Models
3. Types of Optimization Models: Linear Programming, Integer Programming, Nonlinear Programming.
4. Linear Programming examples
5. Geometry of Linear Programming
6. Duality in Linear Programming
7. Modeling Techniques
8. Solution softwares
9. AMPL Modelling language and Cplex solver
10 Combinatorial Optimization
11 Heuristics approaches for Combinatorial Optimization
12 Greedy algorithm
13 Local Search and Taboo search
14 Short overview of Machine Learning and Data Mining
15 Data Mining tasks: Classification, Regression, Clustering, Rule Learning and Summarization
slides of the course
(Date degli appelli d'esame)
|
6
|
MAT/09
|
48
|
-
|
-
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-
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Attività formative affini ed integrative
|
ENG |
|
1049285 -
BIOINFORMATICS IN PLANT PATHOLOGY
(obiettivi)
General skills
A modern plant pathologist has to face this complex reality, plan experiments at real scale, "sucks the marrow out of -omics" (par. Walt Whitman) by using bioinformatics tools, individuate biocontrol agents and stimulate plant self-defences. In relation to this, the main aim of this course is forming young scientists in managing plant diseases tout court by the mean of the -omics plus bioinformatics tools
Specific skills
A) Knowledge and understanding
- Introduction to Plant Pathology: the concept of disease
- The Pathogens: from virus to fungi, different strategies for different pathogens
- The Pathobiome concept
- Integrated Pest Management: how to couple food security with food safety
- Pathogenomics; how genomics meets pathogen
B) Applying knowledge and understanding
- how using specific terminology of a plant pathologist
- Identify the main factors causing disease in major crops
- Establish the salient features of a cycle of infection of a pathogen
- Identify important activities and genes in plant resistance
- Identify the important activities and genes in the virulence of pathogens
- Outline novel strategies for controlling plant diseases
C) Making judgements
- Identification of new perspectives / development strategies for the protection of crops
- Evaluation, interpretation and reprocessing of literature data in the field of molecular plant-microbe interactions
D) Communication skills
- Ability to illustrate the results of research and experimentation carried out in the context of the exercises
- Ability to understand manuscripts in English and to indicate the salient features of the oral exam
E) Learning skills
- Learn the specific terminology of plant pathologist
- Logically connect the acquired knowledge in the field of molecular plant-microbe interactions
- Identify the most relevant topics of the subjects dealt with
- how consulting specialist databases (e.g. ncbi, kegg, string, uniprot)
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FAINO LUIGI
( programma)
il programma prevede una parte di insegnamento di fondamenti di patologia vegetale ed un parte di progetto dove problemi reali di patologia vegetale sono portati agli studenti e loro devono trovare una soluzione
Per la prima parte, dispense e articoli saranno i testi da cui studiare mentre per la prate del progetto, siti specifici all bionformatica tipo stackoverflow saranno usati
(Date degli appelli d'esame)
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REVERBERI MASSIMO
( programma)
MODULO 1: PATOLOGIA vegetale (12 ORE)
1. Introduzione alla patologia vegetale: il concetto di malattia
2. Gli agenti patogeni: dal virus ai funghi, diverse strategie per diversi patogeni
3. Gestione integrata delle malattie: come coniugare la sicurezza alimentare con la salubrità alimentare
MODULO 2: PATOLOGIA VEGETALE MOLECOLARE (12 ORE)
1. Il concetto di Patobioma
2. PATOGENOMICA; come la genomica incontra il patogeno
MODULO 3: IDENTIFICAZIONE DI EFFETTORI (12 ore)
1. organizzazione dei genomi fungini
2. caratteristiche degli effettori
3. metodi di identificazione
MODULO 4: IDENTIFICAZIONE DI GENI DI RESISTENZA (12 ore)
1. i genomi delle piante
2. organizzazione dei genomi delle piante e cluster dei geni R
3. identificazione dei geni di resistenza
MODULO 1: PATOLOGIA VEGETALE GENERALE
1 testo a scelta tra i seguenti:
Fondamenti di patologia vegetale, Editore: Pàtron, Edizione:2; A cura di:F. Favaron, F. Scala; Data di Pubblicazione:2017; EAN:9788855533829; ISBN:8855533827
Elementi di Patologia Vegetale, di Belli G, Editore Piccin-Nuona Libraria. Edizione: 2; Data di Pubblicazione: 2011. ISBN: 9788829921294
Fisiologia Vegetale; AUTORI: Taiz Zeiger, ISBN: 978-88-299-2157-7; CODICE PICCIN: 2001230
MODULO 2: PATOLOGIA VEGETALE MOLECOLARE
1 testo a scelta tra i seguenti:
• Plant Pathology, Authors: George Agrios; ISBN: 9780120445653; eBook ISBN: 9780080473789; Imprint: Academic Press
Per un immediato aggiornamento dei testi o del materiale didattico distribuito dal docente consultare la pagina web del corso:
https://elearning2.uniroma1.it
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6
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AGR/12
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48
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Attività formative affini ed integrative
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ENG |
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