Educational objectives 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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Educational objectives 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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Educational objectives 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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Educational objectives 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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Educational objectives 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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Educational objectives 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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Educational objectives General objectives:
Acquire basic knowledge on fundamental algorithm design techniques and
techniques to analyze the correctness and the complexity of an
algorithm.
Specific objectives:
Knowledge and understanding:
At the end of the course, the student knows:
- fundamental algorithm design techniques,
- techniques to analyze the correctness and the efficiency of an algorithm.
Apply knowledge and understanding:
At the end of the course, the student is able to:
- analyze the complexity of a problem using rigorous mathematical tools,
- analyze existing algorithms and data structures,
- design and analyze new algorithms and data structures for simple
real-life-problems.
Critical and judgmental skills:
The student, at the end of the course, should be able to autonomously
choose which algorithmic technique is best suited for a given problem
and to evaluate among several algorithmic solutions for a certain
problem which one should prefer.
Communication skills:
The student will acquire the ability to express an algorithmic idea
through the use of pseudocode.
Learning ability:
The student will acquire the ability to analyze a problem, and to
design the necessary data structures and the correct and efficient
algorithms for the problem.
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