In Silico Medicine and Simulation

1. Knowledge and understanding

At the end of the course, students will be able to describe the fundamental principles of in silico medicine

and computational simulation applied to biomedical systems; distinguish the main classes of predictive

models, including ordinary and partial differential equation models, agent-based models, multiscale models,

and hybrid approaches; explain the concepts of Virtual Patients, Digital Twins, and In Silico Trials; and

illustrate the main stages of computational model development and evaluation, including conceptual

modelling, parameterization, calibration, sensitivity analysis, uncertainty quantification, verification,

validation, and credibility assessment. Students will also understand the role of computational models in

biomedical research, drug and vaccine development, personalized medicine, and regulatory assessment.

2. Applying knowledge and understanding

Students will be able to analyse a biomedical problem and translate it into an appropriate modelling

problem; define a conceptual model; identify the most suitable modelling paradigm and the data required for

its implementation; critically interpret simulation results; identify the main sources of uncertainty; and

propose appropriate verification, validation, and credibility assessment strategies. Through practical

exercises, case-study analysis, and the development of a project, students will apply the acquired

knowledge to representative problems in in silico medicine.

3. Making judgements

Through the analysis of case studies, scientific articles, and computational models and through the

development of the project, students will be able to critically analyse the assumptions, strengths, limitations,

and sources of uncertainty of a modelling approach; compare alternative modelling strategies; evaluate the

adequacy of verification and validation evidence in relation to the model Context of Use; and formulate and

justify a reasoned judgement on the credibility and applicability of a model in scientific, clinical, industrial, or

regulatory settings.

4. Communication skills

Students will be able to clearly and rigorously present and discuss the problem addressed, methodological

rationale, structure, results, and limitations of their project using appropriate scientific and technical

terminology. The development and presentation of the project will contribute to their ability to communicate

interdisciplinary problems to audiences with computational, mathematical, biomedical, and regulatory

backgrounds.

5. Learning skills

Students will be able to independently retrieve and analyse scientific literature and technical documentation

related to in silico medicine, select the sources required for the development of the project, explore

emerging modelling paradigms, and update their knowledge concerning the evolution of computational

methodologies, Digital Twins, and model credibility assessment approaches.

The course includes lectures devoted to the theoretical and methodological principles of in silico medicine

and computational modelling, complemented by guided exercises and the analysis and discussion of case

studies, scientific articles, and representative applications in biomedical, clinical, industrial, and regulatory

settings.

A central component of the course is the development, individually or in groups, of a project addressing a

problem in the field of in silico medicine. Course activities will progressively guide students through problem

identification, conceptual model definition, selection of the methodological approach, identification of the

required data, and critical evaluation of verification, validation, and credibility assessment strategies.

Lectures primarily contribute to the development of knowledge and understanding, whereas practical

exercises, case studies, and project activities are aimed at developing the ability to apply knowledge, make

independent judgements, solve problems, learn autonomously, and communicate scientific information, in

accordance with the intended learning outcomes.

Pursuant to Article 12 of the University Academic Regulations concerning University Educational Credits

(CFU), within the standard workload of 25 total hours of student commitment corresponding to one credit,

seven hours may be devoted to lectures or equivalent instructional activities, with the remaining hours

reserved for individual study; alternatively, between twelve and fifteen hours may be devoted to classroom

exercises or equivalent supervised activities, with the remaining hours reserved for individual study and

elaboration.

If the course is delivered in blended or remote mode, appropriate adjustments may be made to the above,

in order to ensure consistency with the syllabus.

Basic knowledge of mathematics and computer science, consistent with that normally acquired in a first-

cycle degree programme in Computer Science or a related field, is essential. Introductory knowledge of

programming, mathematical modelling, probability and statistics, as well as basic concepts of human

biology and physiology, is useful but not essential. The specialised concepts required to understand the

topics of in silico medicine will be introduced or reviewed during the course.

Attendance is mandatory in accordance with the Academic Regulations of the Master’s Degree Programme

in Computer Science as reported here: https://web.dmi.unict.it/corsi/lm-18/regolamento-didattico.

1. Introduction to In Silico Medicine 

• Evolution of computational medicine.
• Definition and principles of in silico medicine.
• The role of computer simulation in biomedical research, drug development, and personalized medicine.
• Digital Health, Precision Medicine, and Digital Twins.

2. Fundamentals of Computational Modelling 

• Conceptual and computational models.
• Taxonomy of predictive models.
• Deterministic, stochastic, mechanistic, and data-driven models.
• Multiscale modelling.

3. Mathematical and Computational Modelling Methods 

• Ordinary Differential Equation (ODE) models.
• Partial Differential Equation (PDE) models.
• Agent-Based Modelling (ABM).
• Hybrid models and integration with Artificial Intelligence techniques.

4. Model Development and Evaluation 

• Definition of the biological problem.
• Conceptual modelling.
• Model parameterization and calibration.
• Sensitivity analysis.
• Uncertainty quantification.
• Verification, Validation, and Credibility Assessment.

5. In Silico Medicine and Digital Twins 

• Virtual Patients.
• Digital Twins.
• Disease progression modelling.
• Personalized medicine.

6. In Silico Trials 

• In silico clinical trials.
• Virtual populations.
• Simulation of treatment response.
• Applications to drug and vaccine development.

7. Applications of In Silico Medicine 

• Oncology.
• Immunology.
• Infectious diseases.
• Neurological and autoimmune diseases.
• Clinical decision support.

8. Regulatory Aspects and Future Perspectives 

• Assessment of model reliability.
• National and international regulatory framework.
• Role of in silico medicine in the development and evaluation of healthcare technologies.
• Current challenges and future perspectives.

1. Teaching materials, slides, and lecture notes provided by the teacher and made available through the

Studium platform.

2. Scientific articles and additional reading materials selected by the teacher and made available during the

course.

Learning assessment consists of the development and presentation of an individual or group project

addressing a problem in the field of in silico medicine.

The project may consist of the design of a computational model, the development of a conceptual model,

the critical analysis of an existing model, the analysis of a case study from the scientific literature, or the

critical appraisal of a particularly relevant scientific article.

Students will be expected to demonstrate their ability to correctly identify and describe the biomedical

problem, select and justify the most appropriate modelling approach, identify the required data and

information, correctly apply the methodological principles covered during the course, and critically analyse

assumptions, results, limitations, and sources of uncertainty. Students will also be expected to discuss

appropriate verification, validation, and credibility assessment strategies in relation to the model Context of

Use and to consider its potential applications in scientific, clinical, industrial, or regulatory settings.

The project will be presented and discussed in order to assess students’ understanding of the theoretical

and methodological principles of the course, independent judgement, ability to connect different topics

covered in the syllabus, ability to justify methodological choices, and appropriate use of scientific and

technical terminology.

For group projects, the assessment will also take into account each student’s individual contribution and

ability to discuss the project.

The final grade will be based on an overall assessment of:

- knowledge and understanding of the principles of in silico medicine and computational modelling;

- ability to apply the acquired knowledge to the problem addressed;

- appropriateness and scientific rigour of methodological choices;

- critical thinking and independent judgement;

- scientific quality and internal coherence of the project;

- ability to appropriately use and interpret scientific literature and other relevant sources;

- clarity of presentation and appropriate use of scientific and technical terminology.

The final grade will be assigned according to the following general criteria:

Fail: the project presents substantial deficiencies in the understanding of the problem or in the application of

methodological principles; the student demonstrates insufficient or fragmentary knowledge and is unable to

adequately justify the choices made.

18–21: the project correctly addresses the essential aspects of the problem, although with limited depth and

critical analysis; knowledge and ability to apply the acquired concepts are sufficient, and the presentation is

generally understandable.

22–25: the project demonstrates satisfactory knowledge of the course contents and an adequate ability to

apply and connect concepts; methodological choices are generally appropriate and justified, and the

presentation uses appropriate scientific terminology.

26–28: the project demonstrates good knowledge and understanding of the discipline, methodological

appropriateness, the ability to integrate different concepts, and good independence in critical analysis; the

presentation is clear and rigorous and makes appropriate use of scientific terminology.

29–30 with honours: the project demonstrates comprehensive and in-depth knowledge, excellent ability to

apply and integrate concepts, full independent judgement, and particularly rigorous critical analysis of

methodological choices, results, limitations, and implications; the presentation and discussion demonstrate

scientific maturity and excellent communication skills.

In the case of a group project, the assessment will also take into account each student’s individual

contribution and ability to discuss the project.

Learning assessment may also be carried out on-line, should the conditions require it.

To ensure equal opportunities and in compliance with current laws, interested students may request a

personal interview in order to plan any compensatory and/or dispensatory measures based on educational

objectives and specific needs. Students may also contact the CInAP referring teacher within their

Department.

1. Comparatively analyse mechanistic, data-driven, and hybrid models applicable to a specific biomedical

problem, discussing their rationale, advantages, limitations, and potential fields of application.

2. Starting from the description of a biomedical problem, such as an infectious, oncological, neurological, or

autoimmune disease, design an appropriate modelling strategy by identifying the computational paradigm,

conceptual model, required data, and verification and validation strategies.

3. Design the development and evaluation lifecycle of a computational model, from problem definition and

Context of Use to conceptual modelling, parameterization, calibration, sensitivity analysis, uncertainty

quantification, verification, validation, and credibility assessment.

4. Analyse an application of a Digital Twin in personalized medicine, discussing its structure, data

requirements, updating strategy, potential applications, limitations, and future perspectives.

5. Critically analyse a scientific article in the field of in silico medicine, discussing the biomedical problem

addressed, conceptual model, modelling paradigm, Context of Use, Verification, Validation and Credibility

Assessment strategies, main findings, and potential scientific, clinical, or regulatory impact.