Medical Biotechnology

Activity type

Attività formative affini o integrative

SSD

MED/46

Year

N/D

Semester

N/D

CFU

3

Hours distribution

24 classroom hours

Lecturers

DOMENICO RAIMONDO

Activity type

Attività formative affini o integrative

SSD

ING-IND/06

Year

N/D

Semester

N/D

CFU

3

Hours distribution

24 classroom hours

Lecturers

ALBERTO GIACOMELLO

Activity type

Discipline biotecnologiche comuni

SSD

BIO/10

Year

N/D

Semester

N/D

CFU

1

Hours distribution

8 classroom hours

Lecturers

ALLEGRA VIA

Activity type

Discipline biotecnologiche comuni

SSD

MED/04

Year

N/D

Semester

N/D

CFU

2

Hours distribution

16 classroom hours

Lecturers

TERESA COLOMBO

The course aims to provide students with theoretical and practical knowledge related to the application of computational methodologies to the study of complex biological systems,
with particular reference to the analysis of omics big data, the use of bioinformatics tools, and the use of molecular dynamics and machine learning techniques.

Module 1 - Big Data and Omics Science
Knowledge and Understanding
Know the basic Unix/Linux shell commands for filesystem management.
Become familiar with the basic concepts of genomics and transcriptomics and the main sequencing technologies (first, second and third generation).
Understand the organization and content of major biological databases.
Ability to apply knowledge and understanding
Use shell commands to manipulate files, folders, data streams, and filters (e.g., grep) in big data environments.
Apply bioinformatics tools for gene expression analysis, functional annotation, and genomic visualization (e.g., UCSC Genome Browser).
Leverage web tools for differential analysis and functional enrichment.
Autonomy of judgment
Critically evaluate bioinformatics tools, methods, and resources used for omics data analysis.
Select the most appropriate strategies for querying, integrating, and analyzing large biological datasets.
Communication Skills
Effectively present and discuss the results of bioinformatics analyses, using correct scientific terminology and digital communication tools.
Learning skills
Develop an autonomous and proactive approach to continuous learning in bioinformatics and omics sciences, with emphasis on updating digital resources and databases.

Module 3 - Computational Biology and Molecular Dynamics
Knowledge and Understanding
Gain up-to-date knowledge of computational methodologies for structural analysis of biomolecules, including molecular docking, protein modeling and molecular dynamics.
Understand the relationships between protein structure, dynamics and function.
Ability to apply knowledge and understanding
Use tools for scientific computational sessions and structural analysis of proteins.
Model the three-dimensional structure of proteins and simulate the molecular dynamics of soluble and membrane proteins, as well as ligand/protein interactions.
Access databases to complete, validate and analyze structural models.
Critically interpret simulation results and estimate their biophysical relevance.
Autonomy of judgment
Independently assess the quality of computational and experimental data.
Make informed judgments about the reliability of biological models obtained from simulations or predictions.
Communication Skills
Communicate methods, results, and conclusions effectively to specialist and non-specialist interlocutors, including in interdisciplinary settings.
Learning skills
Conduct autonomous computational investigations, including in advanced research settings, while maintaining up-to-date technical and scientific skills.

Module: Computational biology and molecular dynamics I
N/D
Module: Computational biology and molecular dynamics II
The course aims to provide students with theoretical and practical knowledge on the application of computational methodologies to the study of complex biological systems, with particular reference to the analysis of omics big data, the use of bioinformatics tools and the use of molecular dynamics and machine learning techniques.

Module 3 - Computational Biology and Molecular Dynamics
Knowledge and understanding
- Acquire up-to-date knowledge of computational methodologies for the structural analysis of biomolecules, including molecular docking, protein modelling and molecular dynamics.
- Understand the relationships between protein structure, dynamics, and function.
Applying knowledge and understanding
- Use tools for scientific computational sessions and structural analysis of proteins.
- Modelling the three-dimensional structure of proteins and simulating the molecular dynamics of soluble and membrane proteins as well as ligand/protein interactions.
- Access databases to complete, validate and analyse structural models.
- Critically interpret simulation results and estimate their biophysical relevance.
Making judgements
- Independently assess the quality of computational and experimental data.
- Make informed judgements about the reliability of biological models obtained from simulations or predictions.
Communication skills
- Effectively communicate methods, results and conclusions to specialists and non-specialists, including in interdisciplinary fields.
Learning skills
- To conduct autonomous computational investigations, also in advanced research contexts, keeping one's technical and scientific skills up-to-date.


Module: Computational biology and molecular dynamics III
Module 2 - Introduction to Machine Learning for Biological Big Data.
By the end of the module, students will be able to:
- Explain the fundamental concepts of ML, with emphasis on the main types of learning (supervised, unsupervised and reinforcement learning), and key notions such as feature, class label, feature selection, training, validation and testing of a model.
- Describe and discuss the main steps of an ML process, including: data pre-processing, partitioning into training and test datasets, training and evaluation of the model, and performance improvement by adjusting hyperparameters.


Module: Computational biology and molecular dynamics IV
N/D

Module: Computational biology and molecular dynamics I
N/D
Module: Computational biology and molecular dynamics II
No prerequisites needed


Module: Computational biology and molecular dynamics III
A biological or biomedical background. Knowledge of a variety of biological data and questions.
For the Programming module, no previous experience is necessary. Familiarity with at least one of the main OS (Linux, Mac OSX, Windows 10) is required.
Familiarity with the Google Suite is not a prerequisite but it is advised.



Module: Computational biology and molecular dynamics IV
N/D

Module: Computational biology and molecular dynamics I
The course aims to provide students with theoretical and practical knowledge related to the application of computational methodologies to the study of complex biological systems,
with particular reference to the analysis of omics big data, the use of bioinformatics tools, and the use of molecular dynamics and machine learning techniques.

Module 1 - Big Data and Omics Science
Knowledge and Understanding
Know the basic Unix/Linux shell commands for filesystem management.
Become familiar with the basic concepts of genomics and transcriptomics and the main sequencing technologies (first, second and third generation).
Understand the organization and content of major biological databases.
Ability to apply knowledge and understanding
Use shell commands to manipulate files, folders, data streams, and filters (e.g., grep) in big data environments.
Apply bioinformatics tools for gene expression analysis, functional annotation, and genomic visualization (e.g., UCSC Genome Browser).
Leverage web tools for differential analysis and functional enrichment.
Autonomy of judgment
Critically evaluate bioinformatics tools, methods, and resources used for omics data analysis.
Select the most appropriate strategies for querying, integrating, and analyzing large biological datasets.
Communication Skills
Effectively present and discuss the results of bioinformatics analyses, using correct scientific terminology and digital communication tools.
Learning skills
Develop an autonomous and proactive approach to continuous learning in bioinformatics and omics sciences, with emphasis on updating digital resources and databases.

Module 3 - Computational Biology and Molecular Dynamics
Knowledge and Understanding
Gain up-to-date knowledge of computational methodologies for structural analysis of biomolecules, including molecular docking, protein modeling and molecular dynamics.
Understand the relationships between protein structure, dynamics and function.
Ability to apply knowledge and understanding
Use tools for scientific computational sessions and structural analysis of proteins.
Model the three-dimensional structure of proteins and simulate the molecular dynamics of soluble and membrane proteins, as well as ligand/protein interactions.
Access databases to complete, validate and analyze structural models.
Critically interpret simulation results and estimate their biophysical relevance.
Autonomy of judgment
Independently assess the quality of computational and experimental data.
Make informed judgments about the reliability of biological models obtained from simulations or predictions.
Communication Skills
Communicate methods, results, and conclusions effectively to specialist and non-specialist interlocutors, including in interdisciplinary settings.
Learning skills
Conduct autonomous computational investigations, including in advanced research settings, while maintaining up-to-date technical and scientific skills.



Module: Computational biology and molecular dynamics II
- Basic elements of molecular simulations. Introduction to a molecular dynamics engine and molecule visualisation programmes.
- Interaction potentials between atoms. Preparation and visualisation of a simulation: membrane proteins.
- Ensembles. Exercise on the minimisation and equilibration of a membrane protein.
- Analysis of molecular dynamics simulations. Analysis of trajectories, calculation of relevant observables, averages and fluctuations.
- Non-equilibrium molecular dynamics. Exercise on conduction in an ion channel
- Free energy and collective variables. Exercise on calculating free energy by umbrella sampling: conduction barriers


Module: Computational biology and molecular dynamics III
Different types of Machine Learning.
Key concepts: supervised and unsupervised learning, classification, regression and clustering problems, classes and labels, training, validation and testing.
Building good training datasets - Data preprocessing
Training and test datasets
KNN
Creating a model
Model validation (k-fold cross validation)
Hyperparameter tuning
Performance evaluation



Module: Computational biology and molecular dynamics IV
N/D

Module: Computational biology and molecular dynamics I
materials (including slides, tutorials, videos, notes, and extracts from text books, examples, scripts) will be provided before and during the course by the teacher.


Module: Computational biology and molecular dynamics II
notes from the instructor


Module: Computational biology and molecular dynamics III
Sebastian Raschka - Introduction to Machine Learning
https://sebastianraschka.com/resources/ml-lectures-1/

Further learning materials (including slides, tutorials, videos, notes, and extracts from text books, examples, scripts) will be provided before and during the course by the teacher.



Module: Computational biology and molecular dynamics IV
N/D

Module: Computational biology and molecular dynamics I
N/D
Module: Computational biology and molecular dynamics II
Statistical Mechanics: Theory and Molecular Simulation, Tuckerman, M.E., Oxford Graduate Texts, 2023


Module: Computational biology and molecular dynamics III
N/D
Module: Computational biology and molecular dynamics IV
N/D

Module: Computational biology and molecular dynamics I
N/D
Module: Computational biology and molecular dynamics II
Classes will alternate theoretical notions and computer-based exercises


Module: Computational biology and molecular dynamics III
Learning outcomes (LOs) will guide the design of learning experiences (LEs). For the achievement of each LO, the most appropriate LE(s) will be identified and planned.



Module: Computational biology and molecular dynamics IV
N/D

Module: Computational biology and molecular dynamics I
N/D
Module: Computational biology and molecular dynamics II
compulsory


Module: Computational biology and molecular dynamics III
The course will be face-to-face and will make use of active and interactive learning approaches to facilitate learning..




Module: Computational biology and molecular dynamics IV
N/D

Module: Computational biology and molecular dynamics I
N/D
Module: Computational biology and molecular dynamics II
Students will present the results of an independently developed computational project and will answer questions about the project and the syllabus


Module: Computational biology and molecular dynamics III
Students will be requested to:
- Explain the fundamental concepts of ML, with emphasis on the main types of learning (supervised, unsupervised and reinforcement learning), and key notions such as feature, class label, feature selection, training, validation and testing of a model.
- Describe and discuss the main steps of an ML process, including: data pre-processing, partitioning into training and test datasets, training and evaluation of the model, and performance improvement by adjusting hyperparameters.



Module: Computational biology and molecular dynamics IV
N/D

Module: Computational biology and molecular dynamics I
N/D
Module: Computational biology and molecular dynamics II
- what are the basic equations of molecular dynamics?
- how is a biological system prepared for a simulation?
- basic elements of ensembles
- example of method for free energy calculation


Module: Computational biology and molecular dynamics III
- Explain the fundamental concepts of ML, with emphasis on the main types of learning (supervised, unsupervised and reinforcement learning), and key notions such as feature, class label, feature selection, training, validation and testing of a model.
- Describe and discuss the main steps of an ML process, including: data pre-processing, partitioning into training and test datasets, training and evaluation of the model, and performance improvement by adjusting hyperparameters.



Module: Computational biology and molecular dynamics IV
N/D

Module: Computational biology and molecular dynamics I



Module: Computational biology and molecular dynamics II

• - Basic elements of molecular simulations. Introduction to a molecular dynamics engine and molecule visualisation programmes.
  • Books: Statistical Mechanics: Theory and Molecular Simulation, Tuckerman, M.E., Oxford Graduate Texts, 2023


• - Interaction potentials between atoms. Preparation and visualisation of a simulation: membrane proteins.
  • Books: Statistical Mechanics: Theory and Molecular Simulation, Tuckerman, M.E., Oxford Graduate Texts, 2023


• - Ensembles. Exercise on the minimisation and equilibration of a membrane protein.
  • Books: Statistical Mechanics: Theory and Molecular Simulation, Tuckerman, M.E., Oxford Graduate Texts, 2023


• - Analysis of molecular dynamics simulations. Analysis of trajectories, calculation of relevant observables, averages and fluctuations.
  • Books: Statistical Mechanics: Theory and Molecular Simulation, Tuckerman, M.E., Oxford Graduate Texts, 2023


• - Non-equilibrium molecular dynamics. Exercise on conduction in an ion channel
  • Books: Statistical Mechanics: Theory and Molecular Simulation, Tuckerman, M.E., Oxford Graduate Texts, 2023


• - Free energy and collective variables. Exercise on calculating free energy by umbrella sampling: conduction barriers
  • Books: Statistical Mechanics: Theory and Molecular Simulation, Tuckerman, M.E., Oxford Graduate Texts, 2023