In Silico Medicine and Simulation

Upon successful completion of the course, students will have acquired a comprehensive understanding of the principles, methodologies, and applications of in silico medicine and computational simulation in the biomedical field. In particular, they will understand the role of predictive models in the study of biological and pathological systems, drug and vaccine development, the design of in silico studies, and the implementation of Digital Twins for personalized medicine.

Students will become familiar with the main classes of computational models employed in in silico medicine, including ordinary differential equation (ODE) models, partial differential equation (PDE) models, agent-based models (ABMs), multiscale models, and hybrid modeling approaches integrating biological knowledge, experimental data, and artificial intelligence techniques. They will also be able to distinguish among the concepts of modeling, simulation, prediction, verification, validation, and computational model credibility assessment.

By the end of the course, students will be able to analyze a biomedical problem, formalize it through a conceptual model, identify the data required for its implementation, critically interpret simulation results, and discuss their limitations, uncertainties, and potential applications. They will also gain familiarity with the principles of scientific reproducibility, uncertainty quantification, and modern credibility assessment frameworks, with particular emphasis on their application to the development of computational tools intended for regulatory and clinical use.

Finally, students will be able to critically discuss representative case studies in in silico medicine, including Digital Twins, disease progression modeling, In Silico Trials, drug and vaccine development, personalized medicine, and clinical decision support systems. They will also be able to communicate the methodologies, results, and limitations of computational models effectively to audiences with multidisciplinary backgrounds.

Through lessons and practical sessions (when planned).

If the lessons are given in a mixed or remote way, the necessary changes with respect to what was

previously stated may be introduced, in order to meet the program envisaged and reported in the

syllabus.

Pursuant to the RDA, Article 12 – University Educational Credits (CFU), within the standard

workload of 25 total hours of student commitment corresponding to one credit, the following shall

apply:

(a) seven (7) hours shall be devoted to lectures or equivalent instructional activities, with the

remaining hours reserved for individual study;

(b) a minimum of twelve (12) and a maximum of fifteen (15) hours shall be devoted to classroom

exercises or equivalent supervised activities (laboratories), with the remaining hours reserved for

personal study and elaboration.

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

Programme in Computer Science, available at: 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 is based on two complementary components:

Individual or group project, aimed at the analysis of a biomedical problem using in silico

medicine approaches. 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 discussion

of a case study from the scientific literature, or the presentation and critical appraisal of a

scientific article of particular relevance. Students are expected to demonstrate their ability to

identify the biomedical problem, justify the adopted methodological approach, critically

interpret the results, and discuss the strengths, limitations, credibility, and potential

developments of the proposed model.

Oral examination, aimed at assessing the achievement of the intended learning outcomes

and the student’s understanding of the theoretical concepts covered during the course. The

examination will evaluate the ability to integrate and discuss the different topics of the

discipline, the appropriate use of scientific terminology, the understanding of the principles of

in silico medicine and computational modelling, and the ability to critically discuss case

studies and applications in biomedical, industrial, and regulatory contexts.

The final grade will take into account both the quality of the project and the performance in the oral

examination. In addition to theoretical knowledge, the assessment will consider the student’s ability

to apply the acquired concepts to real-world problems, scientific rigor, critical thinking, independent

judgment, and communication skills.

1. Describe the main differences between mechanistic, data-driven, and hybrid models,

discussing their advantages, limitations, and potential applications in in silico medicine.

2. Given the description of a biomedical problem (e.g., an infectious, oncological, or

autoimmune disease), propose an appropriate modelling strategy, identifying the most

suitable computational modelling paradigm, the conceptual model, the required data, and

the appropriate verification and validation approaches.

3. Describe the main stages of the computational model development lifecycle, from the

definition of the biological problem and conceptual modelling to parameterization, calibration,

verification, validation, and credibility assessment.

4. Discuss the concept of a Digital Twin and its role in personalized medicine, highlighting its

main applications in biomedical research, drug development, and clinical practice, as well as

its current limitations and future perspectives.

5. You are provided with a scientific publication in the field of in silico medicine. After critically

reviewing the paper, discuss the biomedical problem addressed, the proposed conceptual

model, the adopted modelling paradigm, the Context of Use, the Verification, Validation, and

Credibility Assessment strategies, the main findings, and the potential scientific, clinical, and

regulatory impact of the proposed modelling approach.