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Repairing Boolean logical models from time-series data using Answer Set Programming.

Alexandre Lemos1, Inês Lynce1, Pedro T Monteiro1

  • 1INESC-ID/Instituto Superior Técnico, Universidade de Lisboa, Rua Alves Redol 9, 1000-029 Lisbon, Portugal.

Algorithms for Molecular Biology : AMB
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Summary
This summary is machine-generated.

This study introduces an automated method for repairing inconsistent Boolean models of biological networks. The tool uses Answer Set Programming to find minimal repairs, ensuring model dynamics align with new time-series data.

Keywords:
(A)synchronous dynamicsAnswer Set ProgrammingBiological regulatory networksBoolean functionsModel repair

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Bioinformatics

Background:

  • Boolean models are crucial for understanding complex biological signaling and regulatory networks.
  • Model inconsistencies arise with new data, necessitating repair, a process currently manual and error-prone.
  • Existing research primarily focuses on model inference, leaving model repair underdeveloped.

Purpose of the Study:

  • To develop an automated method and tool for repairing inconsistent Boolean models.
  • To address the limitations of manual model repair in systems biology.

Main Methods:

  • Utilized Answer Set Programming (ASP) to formulate model repair as a combinatorial optimization problem.
  • Developed a tool to suggest minimal repair operations for Boolean models.
  • Considered both synchronous and asynchronous updating schemes for model dynamics.

Main Results:

  • The proposed method successfully identifies minimal repairs to reconcile Boolean models with time-series data.
  • The tool generates repairs that ensure model dynamics are coherent with experimental data.
  • Demonstrated the method's efficacy on both established and synthetic biological models.

Conclusions:

  • The developed method provides an efficient and automated approach to Boolean model repair.
  • The study discusses limitations concerning updating schemes and minimization algorithms.
  • This work advances the field of computational systems biology by automating model maintenance.