The teaching is organized as follows:

Learning outcomes

Il corso di Ingegneria proteica si propone di fornire allo studente le basi, teoriche e applicative, riguardanti gli algoritmi, i programmi e i protocolli sperimentali utilizzati nel disegno razionale di proteine. Verranno proposti alcuni casi-modello selezionati dalla letteratura più recente.
Il corso si divide in due moduli: A) Ingegneria proteica in vitro e B) Ingegneria proteica in silico.

To provide a molecular basis of life processes for students who are willing to use informatics and calculus.
The course is directed to students/graduates in physics, chemistry, informatics or biology

Program

Ingegneria Proteica
 Struttura delle proteine, motivi, folding
 Disegno razionalizzato delle proteine: mutagenesi sito-specifica
 Evoluzione diretta: mutagenesi random, DNA-shuffling
 Ingegneria de novo di proteine
 Applicazioni di disegno razionalizzato per ottimizzare le proprietà di catalisi di un enzima, o per l'inserimento di proteine in biosensori

Bioinformatica
• Protein structure prediction and structural genomics, which attempt to systematically produce accurate structural models for three-dimensional protein structures that have not been determined experimentally.
• Computational biochemistry and biophysics, which make extensive use of structural modeling and simulation methods such as molecular dynamics and Monte Carlo method-inspired Boltzmann sampling methods in an attempt to elucidate the kinetics and thermodynamics of protein functions. Computational Biophysics is concerned with solving biological and biomedical problems using physical, mathematical and computational methods.Computational Biophysics is recognized as an essential element in modern biological and biomedical research. There have been fundamental changes in biology and medicine, over the past decade, due to spectacular advances in biomedical imaging, genomics, and proteomics. The nature of these changes demands the application of novel theories and advanced computational tools to decipher the implications of these data, and to devise methods of controlling or modifying biological function. Consequently, Computational Biologists must be well trained and grounded in biology, physics, mathematics, and computer science. Interdisciplinary field that applies the principles and techniques of computer science, chemistry, applied mathematics, statistics and engineering to address biological problems. Some of these problems involve the development of computational and statistical data analysis methods and in developing mathematical modeling and computational simulation techniques. By these means it addresses scientific research topics with their theoretical and experimental questions without a laboratory. It is connected to the following fields:
• Computational biomodeling, a field concerned with building computer models of biological systems.
• Bioinformatics, which applies algorithms and statistical techniques to the interpretation, classification and understanding of biological datasets. These typically consist of large numbers of DNA, RNA, or protein sequences. Sequence alignment is used to assemble the datasets for analysis. Comparisons of homologous sequences, gene finding, and prediction of gene expression are the most common techniques used on assembled datasets; however, analysis of such datasets have many applications throughout all fields of biology.
• Mathematical biology aims at the mathematical representation, treatment and modeling of biological processes, using a variety of applied mathematical techniques and tools.
• Computational genomics, a field within genomics which studies the genomes of cells and organisms. High-throughput genome sequencing produces lots of data, which requires extensive post-processing (genome assembly) and uses DNA microarray technologies to perform statistical analyses on the genes expressed in individual cell types. This can help find genes of interest for certain diseases or conditions. This field also studies the mathematical foundations of sequencing. Advances in many areas of genomics research are heavily rooted in engineering technology, from the capillary electrophoresis units used in large-scale DNA sequencing projects. [1]
• Molecular modeling, which consists of modelling the behaviour of molecules of biological importance.

Examination Methods

Scritto