Motivation: Boolean networks are simple but efficient mathematical formalism for modelling complex biological systems. However, having only two levels of activation is sometimes not enough to fully capture the dynamics of real-world biological systems. Hence, the need for multi-valued networks (MVNs), a generalization of Boolean networks. Despite the importance of MVNs for modelling biological systems, only limited progress has been made on developing theories, analysis methods, and tools that can support them. In particular, the recent use of trap spaces in Boolean networks made a great impact on the field of systems biology, but there has been no similar concept defined and studied for MVNs to date. Results: In this work, we generalize the concept of trap spaces in Boolean networks to that in MVNs. We then develop the theory and the analysis methods for trap spaces in MVNs. In particular, we implement all proposed methods in a Python package called trapmvn. Not only showing the applicability of our approach via a realistic case study, we also evaluate the time efficiency of the method on a large collection of real-world models. The experimental results confirm the time efficiency, which we believe enables more accurate analysis on larger and more complex multi-valued models.
This work was supported by L’Institut Carnot STAR, Marseille, France, and by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. .
Trinh VG, Benhamou B, Henzinger TA, Pastva S. Trap spaces of multi-valued networks: Definition, computation, and applications. Bioinformatics. 2023;39(Supplement_1):i513-i522. doi:10.1093/bioinformatics/btad262
Trinh, V. G., Benhamou, B., Henzinger, T. A., & Pastva, S. (2023). Trap spaces of multi-valued networks: Definition, computation, and applications. Bioinformatics. Oxford Academic. https://doi.org/10.1093/bioinformatics/btad262
Trinh, Van Giang, Belaid Benhamou, Thomas A Henzinger, and Samuel Pastva. “Trap Spaces of Multi-Valued Networks: Definition, Computation, and Applications.” Bioinformatics. Oxford Academic, 2023. https://doi.org/10.1093/bioinformatics/btad262.
V. G. Trinh, B. Benhamou, T. A. Henzinger, and S. Pastva, “Trap spaces of multi-valued networks: Definition, computation, and applications,” Bioinformatics, vol. 39, no. Supplement_1. Oxford Academic, pp. i513–i522, 2023.
Trinh VG, Benhamou B, Henzinger TA, Pastva S. 2023. Trap spaces of multi-valued networks: Definition, computation, and applications. Bioinformatics. 39(Supplement_1), i513–i522.
Trinh, Van Giang, et al. “Trap Spaces of Multi-Valued Networks: Definition, Computation, and Applications.” Bioinformatics, vol. 39, no. Supplement_1, Oxford Academic, 2023, pp. i513–22, doi:10.1093/bioinformatics/btad262.
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