Training courses: Tutorial on Reverse Engineering of Gene Regulatory Networks
Course Description
The reverse engineering of biological networks from omics data is one of the central problems in systems biology and bioinformatics. In particular, the inference of gene regulatory networks from transcriptomic data involves reconstructing plausible regulatory relationships between genes from indirect measures of gene expression, obtained using technologies such as RNA-seq.
This problem is particularly relevant but also complex: transcriptomic datasets are often noisy, high-dimensional, and characterised by a number of genes far exceeding the number of available samples. Furthermore, the correct interpretation of the results requires a combination of biological, statistical, and computational expertise.
This tutorial is designed to guide participants through a complete reverse-engineering workflow, from data preparation to network evaluation, with an introductory and practical approach accessible even to those without prior experience in biological network inference.
Important Dates
- Tutorial date: 1 September 2026
Venue
Sapienza University of Rome
Piazzale Aldo Moro 5, 00185 Rome, Italy
Fee
No registration fee is required. Registration for the tutorial is separate from registration for CIBB 2026 and is not required to attend the main conference.
Selection procedure
Seats are limited.
Instructors
- Dora Tortarolo, Università degli Studi di Torino
- Grete Francesca Privitera, Università degli Studi di Catania
- Roberto Pagliarini, Università degli Studi di Udine
Organizers
- Young InfoLife
- CIBB 2026
Target Audience
- PhD students
- Master’s Students
- Post-Doc research scientists
Learning Outcomes
By the end of the tutorial, participants will be able to:
- Understand the structure of a transcriptomic dataset used for network inference
- Perform preprocessing and exploratory data analysis
- Apply computational methods for regulatory network inference, including correlation-based, mutual information-based, and regression-based approaches
- Evaluate the quality of inferred networks using metrics such as precision, recall, AUROC, and AUPRC
- Critically interpret the results and recognise the main limitations of reverse engineering from omics data
Materials
The organizers will provide:
- Slides introducing the biological background, the transcriptomic data, and the network inference methods
- Example transcriptomic datasets
- Scripts or notebooks for data preprocessing, network inference, and evaluation
- Step-by-step instructions for the hands-on activities
- A gold standard or reference network for comparison, where available
- Recordings of preparatory webinars, made available on the Young InfoLife YouTube channel
Materials may be shared before the event to allow participants to review background concepts, set up their working environment, and become familiar with the task.
Prerequisites
No prior experience with gene regulatory networks is required. Familiarity with basic data analysis and scripting is recommended.
Registration form
Programme
Tuesday, September 1, 2026 |
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| Time | Learning Experience | Topic |
| TBA | Introduction | Biological and computational background: nodes, edges, and the main challenges of network inference |
| TBA | Data exploration and preprocessing | Data inspection, normalization, transformation, gene filtering, and exploratory analysis |
| TBA | Network inference | Application of reverse engineering methods |
| TBA | Network evaluation | Precision, recall, AUROC, AUPRC, and the limitations of accuracy in sparse biological networks |
| TBA | Discussion | Comparison of methodological choices and interpretation of inferred regulatory interactions |