The student will be introduced to the main approaches and methodologies used within bioengineering. Some mathematical and computational models for the analysis of biological systems will be introduced. The student will learn some mathematical and computational tools for the treatment of these models.
The course aims at presenting linear programming problems in healthcare. The course provides students with the analytic tools to model and to foresee situations in which a single decision-maker must find the best choice. At the end of the course, the students will be able to formulate mathematically linear programming problems in healthcare, solve numerically the problems, and realize what the optimal choice is.
The goals of the course are:
Knowledge and understanding: to acquire base knowledge that allows students to study optimization problems and apply opportune techniques to solve the decision-making problems. The students will be able to use algorithms for linear programming problems.
Applying knowledge and understanding: to identify and model real-life decision-making problems. In addition, through real examples, the student will be able to find correct solutions for complex problems.
Making judgments: to choose and solve autonomously complex decision-making problems and to interpret the solutions.
Communication skills: to acquire base communication and reading skills using technical language.
Learning skills: to provides students with theoretical and practical methodologies and skills to deal with optimization problems in healthcare; to acquire further knowledge on mathematics applied to healthcare.
The module includes lectures and tutorials.
Should teaching be carried out in mixed mode or remotely, it may be necessary to introduce changes with respect to previous statements, in line with the programme planned and outlined in the syllabus.
There will be classroom lessons and exercises.
Should teaching be carried out in mixed mode or remotely, it may be necessary to introduce changes with respect to previous statements, in line with the programme planned and outlined in the syllabus.
Learning assessment may also be carried out on line, should the conditions require it.
Introduction to Bioengineering.
Complex Networks.
Complex Networks for Biological Circuits.
Analysis of Genetic, Protein and Metabolic Networks.
Examples of Artificial Intelligence and Bioengineering.
LINEAR PROGRAMMING
Linear programming models. Graphical method. Primal simplex method.
INTEGER PROGRAMMING
Interger programming models. Branch and Bound method in integer programming, Knapsack problem.
EXCEL FOR LINEAR PROGRAMMING PROBLEMS
APPLICATIONS TO HEALTHCARE
Textbooks:
1. W. Mark Saltzman, Biomedical Engineering: Bridging Medicine and Technology, Cambridge Texts in Biomedical Engineering, Cambridge University Press, 2nd Edition, June 2015.
2. Silvio Cavalcanti (A cura di), Biologia Sintetica, Gruppo Nazionale di Bioingegneria, N. 29, Pàtron Editore.
Consultations:
3. Bernhard O. Palsson, Systems Biology Constraint-based Reconstruction and Analysis, Cambridge University Press, 2015.
4. M. A. Marchisio, Introduction in Synthetic Biology – About Modeling, Computation, and Circuit Design, Springer, 2018.
5. Luigi Landini, Nicola Vanello, Analisi e Modelli di Segnali Biomedici, Pisa University Press, 2016.
6. Giuseppe Coppini, Stefano Diciotti, Guido Valli, Bioimmagini, Collana di Ingegneria Biomedica (Diretta da Emanuele Biondi e Claudio Cobelli) Pàtron Editore, 2012.
F.S. Hillier, G.J. Lieberman, Introduction to Operations Research, Mc Graw Hill.
Other teaching material will be available on the platform Studium.