First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Science
Course unit
SCP7081078, A.A. 2017/18

Information concerning the students who enrolled in A.Y. 2017/18

Information on the course unit
Degree course Second cycle degree in
SC1731, Degree course structure A.Y. 2014/15, A.Y. 2017/18
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Number of ECTS credits allocated 6.0
Type of assessment Mark
Website of the academic structure
Department of reference Department of Biology
Mandatory attendance
Language of instruction Italian
Single Course unit The Course unit can be attended under the option Single Course unit attendance
Optional Course unit The Course unit can be chosen as Optional Course unit

Teacher in charge FRANCESCO FILIPPINI BIO/11
Other lecturers CHIARA ROMUALDI BIO/11

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Core courses BIO/11 Molecular Biology 3.0
Core courses SECS-S/02 Statistics for Experimental and Technological Research 3.0

Course unit organization
Period First semester
Year 1st Year
Teaching method frontal

Type of hours Credits Teaching
Hours of
Individual study
Laboratory 3.0 48 27.0 No turn
Lecture 3.0 24 51.0 No turn

Start of activities 02/10/2017
End of activities 19/01/2018
Show course schedule 2019/20 Reg.2014 course timetable

Examination board
Board From To Members of the board
ROMUALDI CHIARA (Membro Effettivo)
ROMUALDI CHIARA (Membro Effettivo)

Prerequisites: In order to properly follow the themes of this course, students are expected to possess basic knowledge of bioinformatics:
(1) database and data mining;
(2) sequence alignment and homology search using BLAST and other programs;
(3) regular expressions (patterns) and sequence profiles based on matrices;
(4) secondary structure predictions, PDB;
(5) regulatory networks and gene expression;
(6) statistical inference (hypothesis testing, one and two samples t-test, analysis of variance).
Target skills and knowledge: In addition to knowledge of scientific and methodological bases of bioinformatics, systems biology, synthetic biology and biotechnology, the student will acquire application skills related to the course content (discussed in detail in the "Contents" section), expendable in particular in the field of biotechnology.
Examination methods: The exam includes tests on both the practical part (carried out as practice tests in the computer lab) and the theoretical notions, through oral and/or written tests.
Assessment criteria: Consistent with the expected acquisition by students of both theoretical knowledge and operational skills, the assessment takes into account both the knowledge of the scientific basis of the topics covered in the course and the ability shown in the practicals.
Course unit contents: Computational Synthetic Biotechnology combines advanced and applied Bioinformatics, Systems Biology and Synthetic Biology, being scientifically associated to front end Biotechnology and, in didactic terms, to advanced recombinant technologies (course of Molecular Synthetic Biotechnology), Functional Genomics, Cellular and Immunological Biotechnologies, Regenerative Medicine and Bioengineering. This course takes into account current trends in relationships - in biotechnological, biomedical and life science research - between in silico and 'wet lab' research, as well as links to scientific areas of the graduation programme. In particular, the course consists of three main modules focusing on (i) regulatory networks and systems biology, (ii) integrative approaches and bioinformatics for computer-aided biotechnologies, (iii) synthetic biology and biotechnology.
(i) Regulatory networks and systems biology
Introduction of biological network and description of the network topology complexity. Definition of transcriptional, signal transduction and developmental network. Definition of network motif. Description of the dynamics of autoregulation motif, feed-forward loop (coherent and incoherent). Combination of motifs. Introduction of biological network in complex organism. Reverse engineering approach. Transcriptomic data and inference of regulatory network.
The practical part will be done in computer lab. Introduction to the use of R platform for gene expression data analysis and reconstruction of small regulatory circuits.
(ii) Integrative approaches and bioinformatics for computer-aided biotechnologies
Genomics, metagenomics and bioinformatics: sequence assembly, gene prediction and genome annotation, genome browsers. Metagenomics and microbiomes as markers for health and environmental variation and pollution. Structural bioinformatics: functional analyses and predictions via integration of sequence, motif, fold, structure and surface comparison. Structure superposition, 3D structure prediction methods (homology modeling, threading, ab initio), molecular dynamics, docking, surface patch analysis (electrostatics, hydropathy). Cellular bioinformatics: topology and subcellular localization predictions (generative, HMM and discriminative, SVM methods), interactomes and elements/domains networks. Immuno-informatics and Reverse Vaccinology: epitope prediction, RV approach and software, pan-vaccines.
(iii) Synthetic biology and biotechnology
Protein engineering, industry and environment: from fine analysis of motifs to functional modulation: rationale design and computational design in biocatalysis, bioremediation and phytoremediation. Synthetic biology and development of biomimetics: identification and engineering of interaction motifs: design of agonists and antagonists. Biomimetics for regenerative medicine, drug delivery. Combination with self-assembling peptides or lipids. Protein engineering and immune defense: design of oligoclonal antibodies (prediction of specific and immunogenic reagions, selection and optimization of regions to be synthesized as peptides); mAb humanization, design of DARPins and other pseudoantibodies. Synthetic genes and promoters: design of synthetic genes for the characterization and/or engineering; optimization of expression and purification tasks. Promoter design.
Practical sessions in the computer lab will concern both parts (ii) and (iii) focusing on a structural bioinformatics and synthetic biotechnology project for enzyme engineering.
Planned learning activities and teaching methods: Students acquire knowledge and skills by attending the activities (lectures and exercises), by interacting with the teachers, and through the study of teaching materials made ‚Äč‚Äčavailable by the teachers (handouts and web content).
The course includes lectures with examples, interaction with questions and answers during the course, practical simulations of problem solving, tutorials with training steps and subsequent test, pre-exam simulations with questions, answers and examples of answer evaluation.
Additional notes about suggested reading: Teachers provide students with the learning material, which is updated annually (lecture notes).
Students can also - through appropriate web pages - access the online guide to tutorials, download educational material, view the schedule of lectures and exercises, alerts, etc.., as well as connect to useful remote resources (server websites with databases and public tools for bioinformatic and statistical analyses).
Textbooks (and optional supplementary readings)