Ateneo - UPO

The lecture includes an overview of the main methods of mathematical and computational modeling applied in the biological and pharmaceutical fields. Special emphasis will be given to molecular modeling and Artificial Intelligence, particularly Machine Learning, where several specific examples will be presented to demonstrate the effectiveness of these approaches.

Slides of the lectures.
Textbook related to specific arguments treated in the lectures are mentioned inside the slides

Knowledge: Developing the ability to formulate mathematical and computational models in System Biology and Virtual screening, particularly in the field of modeling biological target and their interaction with biotechnological drugs at the molecular level. Acquisition of methodological knowledge for the in silico analysis of the main structural and physicochemical properties influencing molecular recognition between the pharmacological target of interest. Methodological strategies to predict and validate the mechanism of action of drugs and biotechnological products with particular reference to the rational design of studies on animal models according to the 3R principle.

Judgment skills: Ability to critically and independently analyze the accuracy and scope of application of the modeling and computational approaches employed in describing biological systems and in the development of drugs and biotechnological products.

Communication skills: Enhancement of disciplinary vocabulary in the analysis of the main structural, physicochemical, and stereo-electronic properties for molecular recognition between the pharmacological target and biotechnological drugs. Ability to communicate on the topics presented during the course using appropriate scientific language.

Learning skills: Acquisition of the ability to critically deepen and autonomously update the acquired skills through the reading of texts and scientific articles. Ability to use educational material for critical and reasoned study.

Basic knowledge in Analysis and Biology.
Some basic knowledge in programming are welcome.

Frontal lessons.
Demos of models presented in the lectures.
Exercises completed by the student.

Oral exam with the presentation of a small project on a specific topic.

Contents:
- Introduction to modern AI and Deep learning with some examples
- Mathematical models in Life Sciences. Some examples in medicine and in System biology.
- Network medicine and introduction to Multiomics.
- Molecular modelling with some biomedical and pharmaceutical applications.
- Machine learning applied to Virtual screening. Extraction of features for drugs and target proteins.
- Machine learning from a biological perspective: some examples (supervised learning and Bayes rules, biologically inspired computing with spiking neurons, Tree-like structures in machine learning and biology)

Practical examples in Python programming language will be provided for the last two topics.

KNOWLEDGE:
- main applications of mathematical models and computational methods;
- critical evaluation of the limits and advantages of the predictive methods used in system biology and in the development of drugs and biotechnological products;

COMPETENCES AND SKILLS:
- ability to autonomously and thoroughly understand the scientific methods and results of a publication containing computational studies
- ability to communicate the results of an in silico study;
- versatile knowledge of computational methods as a basis for further insights from personal studies in the field
- ability to work in academic or industrial research groups on multidisciplinary projects