First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Engineering
Course unit
INP4062795, A.A. 2018/19

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

Information on the course unit
Degree course Second cycle degree in
IN0532, Degree course structure A.Y. 2011/12, A.Y. 2018/19
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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 CLAUDIO COBELLI 000000000000

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

Course unit organization
Period Second 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 25/02/2019
End of activities 14/06/2019
Show course schedule 2019/20 Reg.2011 course timetable

Examination board
Board From To Members of the board
6 A.A. 2018/2019 01/10/2018 15/03/2020 COBELLI CLAUDIO (Presidente)
VISENTIN ROBERTO (Membro Effettivo)
5 A.A. 2017/2018 01/10/2017 15/03/2019 COBELLI CLAUDIO (Presidente)

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


1. To know why modeling, i.e. the purpose of modeling
2. To know the difference bewteen 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

Case Study: Glucose Metabolism and Diabetes

7. To build the whole-body compartmental model of glucose kinetics and quantitate the effect of insulin
8. To build the minimal model to estimate insulin sensitivity from IVGTT data
9. To build the mucle and brain compartmental model of glucose metabolism from PET imaging data
10. To build a cellular stochastic model of insulin secretion
11.To build the minimal model to estimate beta-cell responsivity from IVGTT data
12.To build the key ingredients of the Type 1 diabetes simulation model
13. To use the type 1 simulation model in artificial pancreas and pharma research.
14. To implement a MPC closed-loop glucose control of type 1 diabetes.
Examination methods: The verification of knowledge and skills will be assessed during the course by two intermediate tests based on the material presented in the lectures and in the laboratory. The regular exam consists of a written text, based on the material presented in the lectures and the laboratory, 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: Lezione
1 - Riepilogo di "Sistemi e modelli"
2 - Stima dei parametri: Bayes
3 - Fondamenti di metabolismo del glucosio
4 - Modelli di cinetica del glucosio
5 - Modelli di controllo dell'insulina da dati IVGTT
6 - Modelli di cinetica del glucosio nel muscolo e nel cervello da dati PET
7 - Un modello stocastico della secrezione insulinica
8 - Dal modello cellulare al modello whole-body della secrezione di insulina durante IVGTT
9 - Dal modello minimo al modello massimo del metabolismo del glucosio: il simulatore di diabete di tipo 1 approvato dalla FDA
10 - Applicazioni in farmacologia e biotech del simulatore di diabete di tipo 1: nuove molecole di insulina e sensori di glucosio
11 – Il diabete di tipo 1: dal controllo aperto a quello chiuso della glicemia (pancreas artificiale)
12 – Model predictive control per il pancreas artificiale
13- In Silico clinical trials con il simulatore di diabete di tipo 1 per la progettazione robusta di algoritmi di controllo e scopi regolatori
14 – Una review dei trial clinici con pancreas artificiale sia inpatient che outpatient

1. Identificazione di modelli lineari con minimi quadrati
2. Identificazione di modelli non-lineari con minimi quadrati
3. Identificazione in presenza di informazioni a priori utilizzando massimo a posteriori
4. Modelli compartimentali
5. Identificazione con pesi relativi
6. Uso della funzione forzante
7. Simulink e controllo proporzionale-integrale-derivativo (PID)
8. Applicazione del controllo PID al simulatore del diabete di tipo 1
9. Identificazione di modelli di molecole di insulina


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 program methodologies seen during the lectures.
Additional notes about suggested reading: All the teaching material presented during the lectures is made available on the moodle platform (powerpoint file)
Textbooks (and optional supplementary readings)
  • E. Carson & C. Cobelli, “Introduction to modeling in physiology and medicine”. ---: Academic Press, 2008. Cerca nel catalogo
  • E. Carson & C. Cobelli, “Introduzione alla modellistica in fisiologia e medicina”. Bologna: Patron Editore, 2012. Cerca nel catalogo
  • E. Carson & C. Cobelli, “Modeling methodology for physiology and medicine”,. --: Academic Press, 2001. Cerca nel catalogo