Biomedical Engineering

JLENIA TOPPI
JLENIA TOPPI
PIETRO ARICÒ
PIETRO ARICÒ

Knowledge and understanding:
Students will learn the fundamentals of the general modelling approach such as the between data model and system model, the importance of measurements, the
identification of model parameters in the absence and presence of noise, deconvolution techniques for estimating the model input from the output and its impulse and model
validation techniques, modelling for the artificial pancreas. Moreover, they will learn the basics of different approaches to neural modelling, ranging from circuit models of the
neuronal membrane to black box approaches used to solve the neural encoding/decoding problem, and the basics of neural networks.

Application of knowledge and understanding:
At the end of the course, students will be able to represent a set of measurements and study their statistical properties; identify the parameters of a data model in the absence and in the presence of noise; solve a discrete deconvolution problem in the absence and in the presence of noise; use a standardised model to simulate the glucose/insulin cycle in humans and its alterations (diabetes). They will also become familiar with the basics of tools used to build computational models of neural activity from experimental data.

Critical and judgement skills:
Students will learn how to select the most appropriate modelling approach for a given problem and to evaluate the complexity of the proposed solution.
Communication skills. Students will learn to communicate their modelling choices in a multidisciplinary context and to communicate possible design choices for this purpose.
Learning skills. Students will develop a mindset geared towards independent learning of advanced concepts not covered in the course.

The course aims to provide students with theoretical and practical training on the definition, construction and implementation of a model capable of replicating the behaviour of a biological system from measurements extracted from it.

In particular, topics related to:
1. definition of data model and system model
2. identification of parameters in the absence and presence of noise
3. deconvolution approaches
4. model validation techniques
5. modelling in the history of the artificial pancreas

By the end of the course the student will have acquired knowledge and skills regarding
1. general modelling approach
2. differences in the approach between data model and system model
3. importance of measurements and how their quality impacts on the accuracy of the constructed model
4. identification of model parameters in the absence and presence of noise
5. deconvolution techniques for estimating model input from output and its impulse response (in the absence and presence of noise)
6. model validation
7. matlab programming language (medium level)
8. glucose/insulin cycle and its alterations (diabetes)
9. functioning of the artificial pancreas and the contribution of modelling in its evolution

At the end of the course, the student will be able to use Matlab to:
1. represent a set of measurements and study their statistical properties
2. identify the parameters of a data model in the absence of noise
3. identify the parameters of a data model in the presence of noise
4. solve a discrete deconvolution problem in the absence and presence of noise
5. use a standardised model to simulate the glucose/insulin cycle in humans and its alterations (diabetes)

The main prerequisites needed for the course are:
1. Mathematical Analysis (essential):
1.1 Matric calculus
1.2 derivatives/integrals
1.3 Systems of algebraic and differential equations

2. Fundamentals of Automatic (essential):
2.1 Linear time-invariant systems
2.2 Laplace's transform
2.3 Counter-reacting systems and stability
2.3 Principles of optimal control

3. Basic principles of human anatomy and physiology (important)
3.1 basic anatomy of the endocrine system
3.2 hormone regulation/homeostats

1. Cobelli C, Carson E., «Introduction to Modeling in Physiology and Medicine», Elsevier, 2008
2. Attaway, “MATLAB: A Practical Introduction to Programming and Problem Solving”, 5th Edition, Butterworth-Heinemann, 2019

The second module of the course will be delivered for 60 hours (6CFU) organized as follows:
1. 45 hours (80%) of traditional lessons
2. 15 hours of practical lessons (Matlab)

The course is completely delivered according to the in-person modality (info about time and place could be find on the university web site). Students can attend lessons freely since any site attendance is recorded.

The examination consists of a written test organised as follows:
21 closed-answer questions (true/false) on 7 different topics covered throughout the course (3 per topic) --> max 21 points
1 exercise on verifying the a priori identifiability of structural models --> max 6 points
2 short open-ended questions --> max 6 points
For each of the 21 sentences in the assignment, the student must indicate whether the statement is true or false, or may choose not to answer. Will be awarded: 1pt for each correct answer, -0.5pt for each incorrect answer and 0pt for each answer not given. The exercise will be marked from 0 to 6 points.
The final mark for the written paper will be the sum of the marks taken in the three sections.
The test is passed with a mark >18. Those who pass the examination with a mark of more than 28 may request an oral examination (1 open question on the course syllabus) to which the lecturer may award or deduct a maximum of 3 points (from the written examination mark).

Multiple-choice questions sample all topics covered during the course delivery (3 questions each topic).
The open-ended questions relate to one of the topics covered.
The exercise consists of applying the transfer function method to investigate the a priori identifiability of a structural model. If the test fails, you are asked to suggest how to modify the model so that it can be transformed into an identifiable model.

• • Books: Cobelli C, Carson E., «Introduction to Modeling in Physiology and Medicine», Elsevier, 2008


• • Books: Attaway, “MATLAB: A Practical Introduction to Programming and Problem Solving”, 5th Edition, Butterworth-Heinemann, 2019