KR2023Proceedings of the 20th International Conference on Principles of Knowledge Representation and ReasoningProceedings of the 20th International Conference on Principles of Knowledge Representation and Reasoning

Rhodes, Greece. September 2-8, 2023.

Edited by

ISSN: 2334-1033
ISBN: 978-1-956792-02-7

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Published by

Copyright © 2023 International Joint Conferences on Artificial Intelligence Organization

Revising Boolean Logical Models of Biological Regulatory Networks

  1. Frederico Aleixo(NOVA LINCS, NOVA University Lisbon)
  2. Matthias Knorr(NOVA LINCS, NOVA University Lisbon)
  3. João Leite(NOVA LINCS, NOVA University Lisbon)


  1. Applications of KR in bioinformatics
  2. KR related tools and systems
  3. Logic programming, answer set programming


Boolean regulatory networks are used to represent complex biological processes, modelling the interactions of biological compounds, such as proteins or genes, with each other and with other substances in a cell. Creating and maintaining computational models of these networks is crucial for comprehending corresponding cellular processes, as they allow reproducing known behaviours and testing new hypotheses and predictions in silico. In this context, model revision focuses on validating and (if necessary) repairing existing models based on new experimental data. However, model revision is commonly performed manually, which is inefficient and prone to error, and the few existing automated solutions either only apply to simpler networks or are limited in their revision process, since they may not be able to produce a solution within a reasonable time frame or miss the optimal solution. In this paper, we develop a solution for revising logical models of Boolean regulatory networks, able to find repairs that are consistent with provided, possibly incomplete experimental data, and minimal w.r.t. the differences to the original network. We show that our solution can be used to revise different real-world Boolean logical models very efficiently, surpassing a previous solution in terms of solved instances and with a considerable margin w.r.t. processing time.