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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.02634 |
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| _version_ | 1866914614685466624 |
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| author | Karlebach, Guy |
| author_facet | Karlebach, Guy |
| contents | The network inference problem arises in biological research when one needs to quan8ta8vely choose the best protein-interac8on model for explaining a phenotype. The diverse nature of the data and nonlinear dynamics pose significant challenges in the search for the best methodology. In addi8on to balancing fit and model size, computa8onal efficiency must be considered. Importantly, underlying the measurements, which are affected by experimental noise, there is a complex computa8onal mechanism that is inherently hard to iden8fy. To address these challenges, we present a novel approach that uses algorithmic complexity to infer a Boolean network model from experimental data. We present an algorithm that is op8mal within this framework and allows for asynchronicity in network dynamics. Furthermore, we show that using our methodology a solu8on to the pseudo-8me inference problem, which is per8nent to the analysis of single-cell data, can be intertwined with network inference. Results are described for knowledge-derived and simulated networks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_02634 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Optimal Inference of Asynchronous Boolean Network Models Karlebach, Guy Molecular Networks The network inference problem arises in biological research when one needs to quan8ta8vely choose the best protein-interac8on model for explaining a phenotype. The diverse nature of the data and nonlinear dynamics pose significant challenges in the search for the best methodology. In addi8on to balancing fit and model size, computa8onal efficiency must be considered. Importantly, underlying the measurements, which are affected by experimental noise, there is a complex computa8onal mechanism that is inherently hard to iden8fy. To address these challenges, we present a novel approach that uses algorithmic complexity to infer a Boolean network model from experimental data. We present an algorithm that is op8mal within this framework and allows for asynchronicity in network dynamics. Furthermore, we show that using our methodology a solu8on to the pseudo-8me inference problem, which is per8nent to the analysis of single-cell data, can be intertwined with network inference. Results are described for knowledge-derived and simulated networks. |
| title | Optimal Inference of Asynchronous Boolean Network Models |
| topic | Molecular Networks |
| url | https://arxiv.org/abs/2501.02634 |