First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Engineering
Course unit
INP9087771, 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
IN0532, Degree course structure A.Y. 2011/12, A.Y. 2019/20
bring this page
with you
Number of ECTS credits allocated 9.0
Type of assessment Mark
Department of reference Department of Information Engineering
E-Learning website
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 CHIARA DALLA MAN ING-INF/06

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Core courses ING-INF/06 Electronic and Information Bioengineering 9.0

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

Type of hours Credits Teaching
Hours of
Individual study
Lecture 9.0 72 153.0 No turn

Start of activities 30/09/2019
End of activities 18/01/2020
Show course schedule 2019/20 Reg.2011 course timetable

Examination board
Board From To Members of the board
1 A.A. 2019/2020 01/10/2019 15/03/2021 DALLA MAN CHIARA (Presidente)
SCHIAVON MICHELE (Membro Effettivo)

Prerequisites: Courses of Sistemi e Modelli and Fondamenti di Automatica
Target skills and knowledge: Methodological

1. To know why modeling, i.e. the purpose of modeling
2. To know the difference between model of data and model of system
3. To know the ingredients of the modeling process: model formulation, model identification and model validation
4. To know the difference between models-to-measure and models –to-simulate
5. To know which parameter estimation technique is the most appropriate fro a given model: weighted nonlinear least squares, maximum Llikelihood , Bayes
6. To know how to validate a model in relation to its purpose
7. To be able to build a model of a biological system, given the data and the knowledge of the literature
8. To be able to assess the goodness of a model in relation to the modeling aim.

Case Study: Glucose Metabolism and Diabetes

9. To build the whole-body compartmental model of glucose kinetics and quantitate the effect of insulin
10. To build the minimal model to estimate insulin sensitivity from IVGTT data
11. To build the mucle and brain compartmental model of glucose metabolism from PET imaging data
12. To build a cellular stochastic model of insulin secretion
13.To build the minimal model to estimate beta-cell responsivity from IVGTT data
14.To build the key ingredients of the Type 1 diabetes simulation model
15. To use the type 1 simulation model in artificial pancreas and pharma research.
16. To implement a MPC closed-loop glucose control of type 1 diabetes.
Examination methods: The acquired knowledge and skills will be assessed with a written exam, based on the material presented in the lectures and proposing a practical problem to be solved using PC, based on the material presented during the laboratory, possibly followed by an oral exam based on the material presented in the lectures.
Assessment criteria: Completeness of the knowledge acquired on the topics of the course.
Understanding of the arguments treated in the course, ability to rework the concepts and ability to correctly solve the proposed problems of modeling and control of biological systems.
Course unit contents: Lecture

1 - Recap from “Sistemi e Modelli”
2 - Parameter estimation: Bayes
3 - Fundamentals on glucose metabolism
4 - Models of glucose kinetics
5 - Models of insulin control from IVGTT data
6 - Models of glucose kinetics in muscle and brain from PET Imaging
7 - A cellular stochastic model of insulin secretion
8 - From a cellular to a whole-body model of insulin secretion during IVGTT
9 - From minimal to maximal models of glucose metabolism: the FDA accepted Type 1 diabetes Ssmulator
10 - Pharma and biotech applications of the Type 1 diabetes simulator: new insulin molecules and glucose sensors
11 - Type 1 diabetes: from open- to closed- loop (artificial pancreas) glucose control
12 - Model predictive control of glucose
13- In silico clinical trials with the Type 1 diabetes simulator for robust design of control algorithms and regulatory purposes
14 – Survey of artificial pancreas clinical trials both inpatient and outpatient

1. Linear least squares identification
2. Nonlinear least squares identification
3. Identification in presence of a-priori information using maximum a posteriori
4. Compartmental models
5. Identification with relative weights
6. Use of forcing function
7. Simulink and proportional-integrative-derivative (PID) control
8. Application of PID control on the type 1 diabetes simulator
9. Model identification for new insulin molecules
Planned learning activities and teaching methods: The teaching activity includes classroom lessons where the theoretical contents of the course are dealt with (through powerpoint files). The course includes a series of laboratories in the computer room (Matlab environment) that aims to teach how to implement the methodologies seen during the lectures.
Additional notes about suggested reading: All the teaching material presented during the lectures is made available (in pdf format) on the moodle platform.
Textbooks (and optional supplementary readings)
  • Carson E., Cobelli, .C, Introduction to modeling in physiology and medicine. --: Academic Press, 2008. Cerca nel catalogo
  • Carson E., Cobelli, .C, Introduzione alla modellistica in fisiologia e medicina. --: Patron, 2012. Cerca nel catalogo

Innovative teaching methods: Teaching and learning strategies
  • Lecturing
  • Laboratory
  • Problem based learning
  • Case study
  • Problem solving
  • Active quizzes for Concept Verification Tests and class discussions

Innovative teaching methods: Software or applications used
  • Moodle (files, quizzes, workshops, ...)
  • Matlab

Sustainable Development Goals (SDGs)
Good Health and Well-Being Quality Education Gender Equality