Griffin: A Tool for Symbolic Inference of Synchronous Boolean Molecular Networks

Muñoz, Stalin; Carrillo, Miguel; Azpeitia, Eugenio; Rosenblueth, David A.

doi:10.3389/fgene.2018.00039

Cited by 13 publications

(17 citation statements)

References 72 publications

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“…We again use a sketch with completely undefined update logic and determine DPs based on two expected attractor state (see the supplementary material for details). Since a case study regarding this model was also performed by the authors of the inference tool Griffin ( Muñoz et al 2018 ), we compare the performance of our method to theirs. We show that when we consider the exact same literature-based knowledge and data, both methods reach the same results.…”

Section: Evaluation and Resultsmentioning

confidence: 99%

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Boolean network sketches: a unifying framework for logical model inference

et al. 2023

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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. Availability All software and data are freely available as a reproducible artefact at https://doi.org/10.5281/zenodo.7688740. Supplementary information Supplementary data available online through Bioinformatics.

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Section: Evaluation and Resultsmentioning

confidence: 99%

“…Note that Muñoz et al (2018) employ specific information on prior knowledge formalized in terms of R-graphs and other structures capturing data. In contrast, our approach is significantly more general and includes universal specifications of many aspects, e.g.…”

Section: Introductionmentioning

confidence: 99%

Boolean network sketches: a unifying framework for logical model inference

et al. 2023

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“…Boolean networks were proposed by Stuart Kauffman as gene regulatory network models [ 25 , 26 , 27 ]. They have been extensively used in many areas including artificial life, robotics, and systems biology [ 28 , 29 , 30 , 31 , 32 , 33 , 34 ]. They consist of nodes and links, where nodes represent genes, and the links represent interactions between genes.…”

Section: Methodsmentioning

confidence: 99%

Antifragility Predicts the Robustness and Evolvability of Biological Networks through Multi-Class Classification with a Convolutional Neural Network

Kim

Muñoz

Osuna³

et al. 2020

Entropy

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Robustness and evolvability are essential properties to the evolution of biological networks. To determine if a biological network is robust and/or evolvable, it is required to compare its functions before and after mutations. However, this sometimes takes a high computational cost as the network size grows. Here, we develop a predictive method to estimate the robustness and evolvability of biological networks without an explicit comparison of functions. We measure antifragility in Boolean network models of biological systems and use this as the predictor. Antifragility occurs when a system benefits from external perturbations. By means of the differences of antifragility between the original and mutated biological networks, we train a convolutional neural network (CNN) and test it to classify the properties of robustness and evolvability. We found that our CNN model successfully classified the properties. Thus, we conclude that our antifragility measure can be used as a predictor of the robustness and evolvability of biological networks.

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“…We defined positive and negative regulations following previous authors (Comet et al, 2013;Muñoz et al, 2018). In Boolean terms, a positive (resp.…”

Section: Types Of Regulations On Boolean Termsmentioning

confidence: 99%

A gene regulatory network model that recovers the abaxial-adaxial polarity in Arabidopsis thaliana leaf primordium

Yuste,

Piñeyro-Nelson,

Azpeitia

2024

Front. Ecol. Evol.

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Megaphylls, present in the majority of vascular plants, show in many plant lineages an abaxial-adaxial polarity in their dorsoventral axis. This polarity commonly translates into different tissues developing on each side of the leaf blade. This is important because it promotes better photosynthetic efficiency as related to light absorption and gas exchange. Many researchers have studied the molecular bases of the emergence of leaf abaxial-adaxial polarity, showing that it is produced by the interaction and differential expression of particular genes and other molecules. However, until now, it is still unclear if the molecular components documented thus far are sufficient to explain the emergence of leaf polarity. In this work, we integrated the available experimental data to construct a graph of the Gene Regulatory Network (GRN) involved in the formation of abaxial-adaxial polarity in the leaf primordium of Arabidopsis thaliana. This graph consisted of 21 nodes and 47 regulations. We extracted the main components of the graph to obtain a Minimum Network consisting of six genes and 22 possible regulations. Then, we used the Boolean network (BN) formalism to describe the dynamics of this Minimum Network. We identified 1905 distinct BNs that comprised the regulations of the Minimum Network and exclusively generated the two attractors representing the abaxial and adaxial cell types. This highlights the fact that most graphs, including our network, can describe experimentally observed behaviors with many BN dynamics. By performing mutant simulations and robustness analysis, we found that two of the 1905 BNs better reproduce experimentally available information. To produce the expected attractors, both BNs predict the same missing regulations, which we propose should be experimentally analyzed to confirm their existence. Interestingly, these two BNs have low robustness to perturbations compared with previously analyzed GRNs. This was an unexpected result since abaxial-adaxial polarity is a robust biological trait, which suggests more components or regulations of the network are missing.

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Griffin: A Tool for Symbolic Inference of Synchronous Boolean Molecular Networks

Cited by 13 publications

References 72 publications

Boolean network sketches: a unifying framework for logical model inference

Boolean network sketches: a unifying framework for logical model inference

Antifragility Predicts the Robustness and Evolvability of Biological Networks through Multi-Class Classification with a Convolutional Neural Network

A gene regulatory network model that recovers the abaxial-adaxial polarity in Arabidopsis thaliana leaf primordium

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