BIOINFORMATICS II

Obiettivi formativi

This course offers an introduction to network medicine, a rapidly emerging field that integrates systems biology and network science. It runs counter to the prevailing scientific reductionist trend that dominates current medical research on disease etiology and treatment. Reductionism relies on single molecules or single genes to provide comprehensive and robust insights into the pathophysiology of complex diseases. Similarly, current drug development methodologies target single molecules that very frequently fail because of the unforeseen and unintended effects that result from the application of this piecemeal approach to pharmacology. In contrast, network medicine emphasizes a more holistic approach through the identification and investigation of networks of interacting molecular and cellular components. When network medicine is integrated into biomedical research, it has the potential to transform investigations of disease etiology, diagnosis, and treatment. The course will explore the concept of network medicine through: (1) a review of the role, identification, and behavior of networks in biology and disease, (2) the integration of multiple types of -omics data into networks as a paradigm for understanding disease expression and course, and (3) systems pharmacology approaches for the development and evaluation of effective therapies of complex disease. Moreover, this course will provide hands-on experience in the analysis of two specific types of biological networks—gene co-expression networks and drug-disease networks. During the course, attendees will apply the theory to real data sets. After completing the course, attendees should to be able to apply these methods in their own research. The course goals are: Understand the role of networks in biology and disease. Understand networks as a paradigm for disease expression and course. Understand the challenges of developing effective therapies for complex diseases. Understand the role of omics data in networks. Understand network medicine in terms of investigation for disease etiology, diagnosis, and treatment.

Canale 1
PAOLA PACI Scheda docente

Programmi - Frequenza - Esami

Programma
1 - Introduction to Network Medicine 2 - Algorithms based on gene expression networks (SWIM and WGCNA) 3 - Disease genes and algorithm for disease genes identification (DIAMOND) 4 - Introduction to drug repurposing and drug-disease networks 5 - Algorithms based on drug-disease networks (BiRW and SAveRUNNER) 6 - Practice
Prerequisiti
Conoscenza del linguaggio di programmazione R
Testi di riferimento
Slide fornite durante il corso
Modalità insegnamento
Il corso prevede lezioni sia teoriche che pratiche in presenza, con il proprio pc. Il docente assegnerà dei compiti settimanali da consegnare entro la fine del corso. Il docente assegnerà un punteggio ad ogni compito.
Frequenza
La frequenza alle lezione e' facoltativa.
Modalità di esame
Gli studenti sono invitati a scegliere un progetto tra quelli proposti dal docente. L'esame finale consisterà in una presentazione orale del progetto prescelto seguita da domande del docente. La valutazione finale sarà finalizzata a definire la capacità dello studente di esposizione sintetica, ragionamento e lavoro autonomo. Per la valutazione finale verrà preso in considerazione anche il punteggio riportato per ogni compito a casa.
Bibliografia
Scientific Reports (2021), 11:1, 14677 BMC Bioinformatics (2021), 22:150 PLoS Computational Biology (2021), 17(2):e1008686 npj Systems Biology and Applications (2021), 7(1):3 WIREs Systems Biology and Medicine (2020), 12:e1489 Biochimica et Biophysica Acta - Gene Regulatory Mechanisms (2020), 1863(6),194416 Scientific Reports (2020), 10:1, 3361 BMC Bioinformatics (2019), 20(1):545 BMC Bioinformatics (2018), 19(Suppl 15):436 Genes (2018), 9(9):437 Scientific Reports (2018), 8(1):7769 Scientific Reports (2017), 7:44797 Plant Cell (2014), 26(12), pp. 4617-4635
Modalità di erogazione
Il corso prevede lezioni sia teoriche che pratiche in presenza, con il proprio pc. Il docente assegnerà dei compiti settimanali da consegnare entro la fine del corso. Il docente assegnerà un punteggio ad ogni compito.
  • Codice insegnamento1049266
  • Anno accademico2024/2025
  • CorsoBioinformatics - Bioinformatica
  • CurriculumCurriculum unico
  • Anno3º anno
  • Semestre1º semestre
  • SSDING-INF/06
  • CFU6
  • Ambito disciplinareAttività formative affini o integrative