
Course unit
QUANTITATIVE LIFE SCIENCE
SCP8082720, A.A. 2019/20
Information concerning the students who enrolled in A.Y. 2018/19
Lecturers
No lecturer assigned to this course unit
ECTS: details
Type 
ScientificDisciplinary 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 
Hours of Individual study 
Shifts 
Lecture 
6.0 
48 
102.0 
No turn 
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 12 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
 ConsumerResource Models
3. Physical Models in Biology
 Virus Dynamics
 Bacterial Genetics
 Molecular Population Dynamics
 Gene expressions
 Criticality in generegulation 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, .

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.

Dayan, Peter; Abbott, L.F., Theoretical neurosciencecomputational and mathematical modeling of neural systemsPeter Dayan and L.F. Abbott. Cambridge: London, MIT press, .

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)

