@inproceedings{14411,
  abstract     = {Partially specified Boolean networks (PSBNs) represent a promising framework for the qualitative modelling of biological systems in which the logic of interactions is not completely known. Phenotype control aims to stabilise the network in states exhibiting specific traits.
In this paper, we define the phenotype control problem in the context of asynchronous PSBNs and propose a novel semi-symbolic algorithm for solving this problem with permanent variable perturbations.},
  author       = {Beneš, Nikola and Brim, Luboš and Pastva, Samuel and Šafránek, David and Šmijáková, Eva},
  booktitle    = {21st International Conference on Computational Methods in Systems Biology},
  isbn         = {9783031426964},
  issn         = {1611-3349},
  location     = {Luxembourg City, Luxembourg},
  pages        = {18--35},
  publisher    = {Springer Nature},
  title        = {{Phenotype control of partially specified boolean networks}},
  doi          = {10.1007/978-3-031-42697-1_2},
  volume       = {14137},
  year         = {2023},
}

@inproceedings{14718,
  abstract     = {Binary decision diagrams (BDDs) are one of the fundamental data structures in formal methods and computer science in general. However, the performance of BDD-based algorithms greatly depends on memory latency due to the reliance on large hash tables and thus, by extension, on the speed of random memory access. This hinders the full utilisation of resources available on modern CPUs, since the absolute memory latency has not improved significantly for at least a decade. In this paper, we explore several implementation techniques that improve the performance of BDD manipulation either through enhanced memory locality or by partially eliminating random memory access. On a benchmark suite of 600+ BDDs derived from real-world applications, we demonstrate runtime that is comparable or better than parallelising the same operations on eight CPU cores. },
  author       = {Pastva, Samuel and Henzinger, Thomas A},
  booktitle    = {Proceedings of the 23rd Conference on Formal Methods in Computer-Aided Design},
  isbn         = {9783854480600},
  location     = {Ames, IA, United States},
  pages        = {122--131},
  publisher    = {TU Vienna Academic Press},
  title        = {{Binary decision diagrams on modern hardware}},
  doi          = {10.34727/2023/isbn.978-3-85448-060-0_20},
  year         = {2023},
}

@article{13263,
  abstract     = {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.},
  author       = {Trinh, Van Giang and Benhamou, Belaid and Henzinger, Thomas A and Pastva, Samuel},
  issn         = {1367-4811},
  journal      = {Bioinformatics},
  number       = {Supplement_1},
  pages        = {i513--i522},
  publisher    = {Oxford Academic},
  title        = {{Trap spaces of multi-valued networks: Definition, computation, and applications}},
  doi          = {10.1093/bioinformatics/btad262},
  volume       = {39},
  year         = {2023},
}

@article{12876,
  abstract     = {Motivation: The problem of model inference is of fundamental importance to systems biology. Logical models (e.g. Boolean networks; BNs) represent a computationally attractive approach capable of handling large biological networks. The models are typically inferred from experimental data. However, even with a substantial amount of experimental data supported by some prior knowledge, existing inference methods often focus on a small sample of admissible candidate models only.

Results: We propose Boolean network sketches as a new formal instrument for the inference of Boolean networks. A sketch integrates (typically partial) knowledge about the network’s topology and the update logic (obtained through, e.g. a biological knowledge base or a literature search), as well as further assumptions about the properties of the network’s transitions (e.g. the form of its attractor landscape), and additional restrictions on the model dynamics given by the measured experimental data. Our new BNs inference algorithm starts with an ‘initial’ sketch, which is extended by adding restrictions representing experimental data to a ‘data-informed’ sketch and subsequently computes all BNs consistent with the data-informed sketch. Our algorithm is based on a symbolic representation and coloured model-checking. Our approach is unique in its ability to cover a broad spectrum of knowledge and efficiently produce a compact representation of all inferred BNs. We evaluate the method on a non-trivial collection of real-world and simulated data.},
  author       = {Beneš, Nikola and Brim, Luboš and Huvar, Ondřej and Pastva, Samuel and Šafránek, David},
  issn         = {1367-4811},
  journal      = {Bioinformatics},
  number       = {4},
  publisher    = {Oxford Academic},
  title        = {{Boolean network sketches: A unifying framework for logical model inference}},
  doi          = {10.1093/bioinformatics/btad158},
  volume       = {39},
  year         = {2023},
}