Artificial Intelligence for Biomedicine and Healthcare

Systems Biomedicine Approaches to Epidemiology and public health

Systems Biomedicine Approaches to Epidemiology and public health

Academic year 2024/2025

Sommario del corso

• Course objectives
• Results of learning outcomes
• Program
• Course delivery
• Learning assessment methods
• Teaching Modules
• Teaching tools
• Course card

Course objectives

Since this course is divided into four modules, we reported hereafter the objective for each module:

• Introduction to Computational Modelling and Simulations in Life Sciences (INF/01 - 5CFU - 40h)

  The objective of this course is to equip students with a comprehensive understanding and practical skills in computational modelling and simulations as applied to life sciences.

• Supervised learning applications to healthcare (MED/ 01 - 1CFU - 12h)

• Epidemiology (MED/ 42 - 1CFU - 12h)

The objective of this module is to provide the students with a general knowledge of the epidemiological instruments for studying diseases' etiology and treatments.

• Cancer as a model system for population dynamics  (BIO/17 - 2 CFU 24h)
  

The objective of this module is to provide the students with a comprehensive understanding of the basic principles that govern cancer biology and with the skills required to define and measure the quantitative variables needed for computational modelling of its dynamics.

Results of learning outcomes

Since this course is divided into four modules, we reported hereafter the learning outcomes for each module.

Introduction to Computational Modelling and Simulations in Life Sciences (INF/01 - 5CFU - 40h)

KNOWLEDGE AND UNDERSTANDING – Completing the course students will be able to:

1. Explain the foundational concepts and theoretical underpinnings of computational modelling and simulations in the context of life sciences;
2. Describe the differences between deterministic, stochastic, and agent-based models, and understand their appropriate applications in biological research;
3. Identify key biological problems that can be addressed using computational modelling and choose suitable modelling approaches for specific scenarios;
4. Recognize the role of randomness and uncertainty in biological systems and explain how stochastic models can incorporate these elements;
5. Understand the principles and mechanisms of agent-based models, including how they simulate interactions and behaviours within complex biological systems.
6. Demonstrate familiarity with various computational tools and programming languages commonly used in the development and analysis of biological models.
7. Critically evaluate the strengths and limitations of different modelling approaches and their implications for biological research and practical applications.

APPLYING KNOWLEDGE AND UNDERSTANDING – students will be able to apply computational modelling techniques to develop, simulate, and analyze biological systems, thereby solving complex biological problems and advancing scientific research.

• Supervised learning applications to healthcare (MED/ 01 - 1CFU - 12h)

• Epidemiology (MED/ 42 - 1CFU - 12h)

KNOWLEDGE AND UNDERSTANDING – Completing the course students will be able to:

1. Understand the difference among association and causality;
2. Measure diseases' prevalence, cumulative incidence, and incidence density
3. Recognize the differences among observational and experimental studies
4. Identify several type of epidemiological studies 
5. Critical read scientific epidemiological articles 

Cancer as a model system for population dynamics  (BIO/17 - 2 CFU 24h)

KNOWLEDGE AND UNDERSTANDING – Completing the course students will be able to:

1. Explain why cancer is, in essence, an evolving entity and how it differs from other pathological states;  
2. Understand the foundational concepts of cancer genetics and biology;
3. Understand the role of genetic instability and selection, immuno editing and phenotypic plasticity in shaping the dynamics of the cancer cell population
4. Recognize the role of cancer-stroma interactions in driving cancer development;
5. Apply those concepts to design rational models of cancer evolution;
6. Recognize how the foundamental biological properties of cancer have been integrated in state-of-the-art mathematical models of cancer evolution;
7. Critically discuss the models of cancer evolution proposed in the literature and propose new approaches.

APPLYING KNOWLEDGE AND UNDERSTANDING – students will be able to exploit the computational modelling knowledge acquired in the other course modules to the cancer scenario by translating the foundamental concepts of cancer biology into quantitative mathematical terms.

Program

Since this course is divided into four modules, we reported hereafter the program for each module.

Introduction to Computational Modelling and Simulations in Life Sciences (INF/01 - 5CFU - 40h)

1. Introduction to Computational Modeling in Life Sciences
2. Overview of Modeling Approaches
3. Deterministic Models;
4. Stochastic Models;
5. Agent-based Models;
6. Computational tools and languages for modelling.

Epidemiology (MED/ 42 - 1CFU - 12h)

1. Epidemiological relationship (association vs causation)
2. Confounding and Effect modification
3. Measure of occurence and measure of association
4. Ecological studies
5. Prevelence studies
6. Case-control studies
7. Cohort studies
8. RCTs

Cancer as a model system for population dynamics  (BIO/17 - 2 CFU 24h)

1. The biological hallmarks of cancer, oncogenes and oncosuppressors;
2. Clonal evolution, genetic diversity and darwinian selection;
3. Phenotypic heterogeneity and reversible phenotypic plasticity;
4. The role of cancer-stroma interactions in cancer development and progression;
5. Immune surveillance, immuno editing and immuno therapies;
6. Therapies and resistance (genetic resistance and phenotypic drug tolerance);
7. Mathematical approaches and experiments to measure and model cancer evolution.

Course delivery

The course consists of  88 hours of lectures and laboratories. Laboratories include exclusively practical activities.

The slides presented during lectures are available to students as online materials.

Attendance of lessons is not mandatory, but highly recommended due to the necessity of learning and employing specific computer science instruments.

Learning assessment methods

Introduction to Computational Modelling and Simulations in Life Sciences (INF/01 - 5CFU - 40h)

The exam consists of an oral test  and requires a practice exercise

Epidemiology (MED/ 42 - 1CFU - 12h)

The exam consists of a written test based on a scientific article analysis

Introduction to Computational Modelling and Simulations in Life Sciences (INF/01 - 5CFU - 40h)

The exam consists of a written test followed by an oral discussion

Teaching Modules

• ONC0262A Introduction to Computational Modelling and Simulations in Life Sciences (ONC0262A)
• ONC0262B Cancer as a model system for population dynamics (ONC0262B)
• ONC0262C Supervised learning applications to healthcare (ONC0262C)
• ONC0262D Epidemiology (ONC0262D)

• Teaching material
• Exams and finals
• Class schedule
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