First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Science
Course unit
SCP9088063, A.A. 2019/20

Information concerning the students who enrolled in A.Y. 2019/20

Information on the course unit
Degree course Second cycle degree in
SC1731, Degree course structure A.Y. 2014/15, A.Y. 2019/20
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Number of ECTS credits allocated 8.0
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

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Other -- -- 2.0
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 Annual
Year 1st Year
Teaching method frontal

Type of hours Credits Teaching
Hours of
Individual study
Laboratory 2.0 32 18.0 No turn
Lecture 6.0 48 102.0 No turn

Start of activities 30/09/2019
End of activities 20/06/2020
Show course schedule 2019/20 Reg.2014 course timetable

Examination board
Board From To Members of the board

Prerequisites: Computational part:
(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.

Molecular part:
Basic knowledge of molecular biology (Molecular Biology of prokaryotic and eukaryotic organisms); principles of genetic engineering.

The Industrial Biotechnologies programme favours incoming students from multidisciplinary environment. Several students obtain BSc degrees in universities or programmes other than 'our own' bachelor in Biotechnology. Therefore, all materials from the relevant Bioinformatics course from the Biotechnology programme are kept on the web site dedicated to this course to be of help to such students and rescue eventual gaps in compulsory pre-knowledge.
Target skills and knowledge: Computational part:
- analysis of regulatory networks and interactomes, integrative approaches to compound problems, cellular bioinformatics, analysis of metabolomes and metabolic engineering, molecular modelling and protein engineering, rational and computational design, biocatalysis and biomimetics, microbiome analysis, computational toxicology, bioremediation, immunoinformatics and reverse vaccinology;
- ability to integrate aforementioned approaches for functional inference, smart experimental design and in projects of biotechnological engineering.

Molecular part:
- principles and techniques of genetic manipulation of organisms, with particular regards to the production of useful molecules and recombinant proteins in both well-established and innovative systems. Different host systems will be described, as used in the lab-scale but also in their extension to the industrial application;
- basic techniques, innovative developments and examples of recent applications in cell-free protein synthesis, in protein engineering and in metabolic engineering (in microorganisms).
Examination methods: Computational part:
In the laboratory classroom experience, interactive training steps - as team work and by interacting with the teacher and each other - are followed by a problem solving phase in which students write question-driven reports. Students are provided with peer feedback to improve the reports and their presentation. Reports from the computer classroom determine half the grade (15/30) + possible bonus. Theoretical knowledge is evaluated by oral exam (further 15/30), in which methods can also be linked to activities performed in the computer classroom, as well as to scientific articles selected by the students and to putative projects proposed by the student and/or the teacher.

Molecular part:
Written exams with open questions.
Assessment criteria: Consistent with the theoretical and practical nature of this course, evaluation will take into account acquisition of both knowledge and problem solving skills.
Concerning theoretical knowledge, evaluation will focus on:
- knowledge of molecular and computational tools presented in this course;
- knowledge of analytical methods, their potential and limits;
- knowledge of best strategies to combine and integrate use of computational and molecular tools and methods.
Concerning practical skills, evaluation will focus on:
- problem solving skills, i.e. capacity to properly use and integrate computational and molecular resources, showing awareness about their potential and limits;
- congruence and completeness in responses to driving questions;
- capacity to focus on elements crucial for inferring relevant information;
- capacity to present data and analyses rigorously and with complete and clear format;
- capacity to take advantage from teachers feedback;
- team working skills.
Course unit contents: Molecular and Computational Synthetic Biotechnology combines the most advanced approaches in Bioinformatics and Recombinant Technologies for applications in Systems and Synthetic Biology projects. 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 program.

Computational part:
(1) Integrative approaches and bioinformatics for computer-aided biotechnologies.
Comparative genomics and bioinformatics; genome annotation. 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. Immuno-informatics and Reverse Vaccinology: epitope prediction, RV approach and software, pan-vaccines.
(2) Synthetic biology and biotechnology
Protein engineering, industry and environment: from fine analysis of motifs to functional modulation: rational 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 focus on a structural bioinformatics and synthetic biotechnology project for enzyme engineering.

Molecular part:
- Protein expression in E.coli: analysis, design and modification of factors affecting expression of cloned genes.
- Protein expression in yeast: Saccharomyces cerevisiae vs Pichia pastoris, similarities and peculiarities. The problem of protein glycosylation.
- Protein expression in insect cells: elements of the molecular biology of baculovirus, engineering of its genome; bacmids; ‘humanized’ insect cells.
- Protein synthesis in cell-free systems.
- Protein engineering: rational-, semirational-design and directed evolution. Examples in biocatalysis, bioremediation, useful proteins for research and in biomedicine (inteins and elastin-like proteins).
- Genome editing: meganucleases, ZF-nucleases, TALEN and CRISPR/Cas technology. Cre and FLP recombinases.-
- Metabolic engineering in microorganisms and animal cells (CHO).
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 available by the teachers (handouts and web content).
In the first meeting with students, full details are presented concerning course topics and teaching/learning approach, as well as on available online resources.
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. Students are provided with online tutorials in the pages of the web site created for this course and with eliciting questions to use remote tools for performing functional inference and biotechnological engineering steps simulating real projects.
Students are also provided with peer feedback from the teachers and interactive problem solving activities.
Team work, results comparison and brainstorming and case studies are also part of this course, mediated by both the teacher and the students.
Finally, pre-exam simulations with questions, answers and examples of answer evaluation at the end of the course.
Additional notes about suggested reading: Teachers provide students with the learning material, which is updated annually (lecture notes). Teaching material is available at personal web pages and E-learning platform.
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 web sites with databases and public tools for computational and molecular analyses).
Textbooks (and optional supplementary readings)
  • Glick, Pasternak, Patten., Molecular Biotecnology – principles and applications of recombinant DNA - 4th edition.. --: ASM press, --. Cerca nel catalogo

Innovative teaching methods: Teaching and learning strategies
  • Lecturing
  • Laboratory
  • Problem based learning
  • Case study
  • Working in group
  • Questioning
  • Problem solving
  • Work-integrated learning
  • Peer feedback
  • Loading of files and pages (web pages, Moodle, ...)
  • Integrative approach via remote tools

Innovative teaching methods: Software or applications used
  • Moodle (files, quizzes, workshops, ...)
  • Kahoot / Poll Everywhere

Sustainable Development Goals (SDGs)
Good Health and Well-Being Quality Education Industry, Innovation and Infrastructure Responsible Consumption and Production