First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Engineering
Course unit
INP8084322, A.A. 2019/20

Information concerning the students who enrolled in A.Y. 2018/19

Information on the course unit
Degree course Second cycle degree in
IN2371, Degree course structure A.Y. 2017/18, A.Y. 2019/20
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Degree course track ICT FOR LIFE AND HEALTH [004PD]
Number of ECTS credits allocated 6.0
Type of assessment Mark
Course unit English denomination QUANTITATIVE LIFE SCIENCE
Department of reference Department of Information Engineering
Mandatory attendance No
Language of instruction English
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 SAMIR SIMON SUWEIS FIS/03

Course unit code Course unit name Teacher in charge Degree course code

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Educational activities in elective or integrative disciplines FIS/03 Material Physics 6.0

Course unit organization
Period First semester
Year 2nd Year
Teaching method frontal

Type of hours Credits Teaching
Hours of
Individual study
Lecture 6.0 48 102.0 No turn

Start of activities 30/09/2019
End of activities 18/01/2020
Show course schedule 2019/20 Reg.2019 course timetable

Examination board
Examination board not defined

Prerequisites: Basics of Stochastic processes. Thermodynamics of phase transitions.
If you never attended the class “Statistical Mechanics” or “Models of Theoretical Physics” we suggest also to follow the first 3 CFU of this exam
Target skills and knowledge: After completing the course the student should be able to understand and explain the basic concepts and the use of advanced techniques in statistical mechanics of complex systems..
In particular, the student will
1) Acquire the ability to build an appropriate phenomenological theoretical model based on the available data of the system
2) Give an account of the relevant and minimal amount of quantities needed to describe the system (use of null model).
3) Understand the use of generating functions.
4) Explain the concept of phase transitions in out of equilibrium interacting particle models as well as the physics at or near critical points.
5) Understand the strength and limitation of the models
6) Show an analytic ability to solve problems relevant to complex systems
Examination methods: The first part of the verification of the acquired knowledge will evaluated be through homework exercises (to do in groups) and the participation of the students in the class discussions The second part will takes place through, a common written test with 1-2 exercises to be solved and open questions to test the knowledge on basic concepts, the scientific vocabulary, the ability to synthesis and critical discussion acquired during the course. The third facultative part of the exam will be oral and will be based on a discussion on the various topics discussed during the course.
Assessment criteria: The criteria used to verify the knowledge and skills acquired are:
1) understanding of the topics covered;
2) critical ability to connect the acquired knowledge;
3) completeness of the acquired knowledge;
4) synthesis ability;
5) understanding of the terminology used
6) ability to use the analytical methodologies and computational techniques illustrated during the course to solve or at least to approach set problems on complex systems where statistical mechanics plays an important role.
Course unit contents: The program can be summarized as follow
Theoretical Neuroscience
- Basics in Neuroscience
- Neural circuits & structure and function of brain networks
- Wilson Cowan models
- Stochastic whole brain models
- Mean field approaches
- Criticality in the brain
- Controllability in brain networks

2. Statistical Mechanics of Ecological Systems
- Neutral theory and emergent patterns in ecology
- Dynamical Evolution of Ecosystems
- Upscaling and Downscaling biodiversity
- Species Interaction Networks
- Consumer-Resource Models

3. Physical Models in Biology
- Virus Dynamics
- Bacterial Genetics
- Molecular Population Dynamics
- Gene expressions
- Criticality in gene-regulation networks
- Robustness and Adaptability in Living Systems.

Please note that some topics may vary.
Planned learning activities and teaching methods: The course is organized in lectures whose contents are presented on the blackboard, sometimes with the help of images, diagrams and videos. The teaching is interactive, with questions and presentation of case studies, in order to promote discussion and critical thinking in the classroom.
Additional notes about suggested reading: Beyond some suggested books, materials (notes and published papers) will be available to the students in Moodle.
Textbooks (and optional supplementary readings)
  • May, Robert M., Stability and complexity in model ecosystemsRobert M. May. Princeton: Princeton university press, --. Cerca nel catalogo
  • Nelson, Philip; Bromberg, Sarina; Hermundstad, Ann; Prentice, Jason, Physical models of living systemsPhilip Nelsonwith the assistance of Sarina Bromberg, Ann Hermundstad, and Jason Prentice. New York: W. H. Freeman and Company, 2015. Cerca nel catalogo
  • Dayan, Peter; Abbott, L.F., Theoretical neurosciencecomputational and mathematical modeling of neural systemsPeter Dayan and L.F. Abbott. Cambridge: London, MIT press, --. Cerca nel catalogo

Innovative teaching methods: Teaching and learning strategies
  • Lecturing
  • Problem based learning
  • Interactive lecturing
  • Working in group
  • Video shooting made by the teacher/the students
  • Use of online videos
  • Loading of files and pages (web pages, Moodle, ...)
  • Learning journal

Innovative teaching methods: Software or applications used
  • Moodle (files, quizzes, workshops, ...)
  • One Note (digital ink)
  • Mathematica

Sustainable Development Goals (SDGs)
Quality Education Industry, Innovation and Infrastructure Life on Land