17 papers · ranked by Valyu relevance
Samuel Pastva, Kyu Hyong Park, Jordan C. Rozum, Van-Giang Trinh + 1 more
Connecting the dynamics of biomolecular networks to experimentally measurable cell phenotypes remains a central challenge in systems biology. Here we introduce a model-based definition of phenotype as a partial steady state that is committed to a certain dynamical outcome while otherwise being minimally constrained. We…
Saran Pankaew, Vincent Noel, Loic Paulevé, Denis Thieffry + 2 more
Boolean networks (BNs) have emerged as versatile tools for modeling cellular regulatory mechanisms due to their ability to capture key biological features despite their simplicity. Multiple BN synthesis methods, which aim to infer BNs with dynamics that corresponding to the experimental data, have emerged in a recent…
Marco Fariñas, Eirini Tsirvouli, John Zobolas, Tero Aittokallio + 2 more
Boolean models are a powerful resource for studying dynamic processes of biological systems. However, their inherent discrete nature limits their ability to capture continuous aspects of signal transduction, such as signal strength or protein activation levels. Although existing tools provide some path exploration…
Carissa Bleker, Maja Zagorščak, Andrej Blejec, Kristina Gruden + 1 more
Boolean and logic-based modeling approaches are well suited for the analysis of complex biological systems, particularly when detailed biochemical and kinetic information is unavailable. In such settings, biological pathways are represented as networks capturing system components and their interactions, providing a…
Ismail Belgacem, Franck Delaplace
Binarization of gene expression data is a critical prerequisite for the synthesis of Boolean gene regulatory network (GRN) models from omics datasets. Because Boolean networks encode gene activity as binary variables, the accuracy of binarization directly conditions whether the inferred models can faithfully reproduce…
Breschine Cummins, Marcio Gameiro, Tomáš Gedeon, Konstantin Mischaikow + 1 more
Boolean models are widely used to characterize the dynamics of gene regulatory networks. However, their coarse state discretization limits their ability to capture complex continuous dynamics and continuous parameter dependencies. In this paper, we present a rigorous mathematical framework that embeds monotone Boolean…
Laure de Chancel, Brigitte Mossé, Aurélien Naldi, Élisabeth Remy
Boolean networks are widely used to model biological regulatory networks and study their dynamics. Classical semantics, such as the asynchronous semantics, do not always accurately capture transient or asymptotic behaviors observed in quantitative models. To address this limitation, the Most Permissive semantics was…
Luna Xingyu Li, Yue Zhang, Boris Aguilar, Tazein Shah + 2 more
3.1### The KGBN toolkit We developed KGBN, an open-source Python library for Boolean network and probabilistic Boolean network modeling, extension, optimization, and analysis, available at [https://github.com/IlyaLab/KGBN](). It supports the entire logical model augmentation workflow described in [F1] and provides a…
Aruldoss Immanuel, Dharshini Priya Selvaganesan, Danthuluri Sree Lakshmi, Venkatasubramanian Ulaganathan + 1 more
Levan is a fructose polymer with applications in the creation of hydrogels for drug delivery and wound healing. In industrial biotechnology. Bacillus subtilis is the key organism for producing levan. However, the metabolic models of Bacillus subtilis available do not include the biosynthesis of levan. To understand…
Luna Xingyu Li, Yue Zhang, Boris Aguilar, Tazein Shah + 2 more
Logical gene regulatory network (GRN) models provide interpretable, mechanistic representations of cellular regulation and are widely used in systems biology. However, most existing models remain incomplete, context-specific, and difficult to extend to comprehensive GRNs, limiting their broader applicability to tasks…
Aamer Iqbal Bhatti
Cancer arises from the distortion of the genetic information that regulates cellular pathways. This corruption dysregulates many signalling pathways at once and produces the cancer hallmarks: resistance, proliferation, and evasion of apoptosis. Because the pathways are coupled, a drug aimed at a single target is often…
Célia Biane, Kyungduk Moon, Kangbok Lee, Loïc Paulevé
Boolean networks are discrete dynamical models that use Boolean states and logical functions to represent the dynamics of biological systems. A primary application of Boolean networks is to identify controls (e.g., genetic mutations or knockouts) that drive the system toward a desired phenotype. However, existing…
Ricco Zeegelaar, Georgios Argyris, Janine N. Post, Federica Limana
Understanding the mechanisms underlying cardiomyocyte (CM) differentiation is essential for the accurate generation of the different types of heart cells in vitro. This study advances current models of CM differentiation by introducing a gene regulatory network (GRN) model that integrates early heart field formation…
Yuxiang Yao, Dong Liu, Zheting Zhang, Chengchen Zhao + 1 more
Complex biosystems exhibit ordered, functional, self-organized features, yet a universal framework for exploring their logical paradigms and dynamic behaviors remains lacking. Here we present BioLogical, a user-friendly R package designed to analyze logical properties of gene regulatory systems. Through a standard…
Daniel Ramirez, David A. Kessler, Mingyang Lu, Herbert Levine
The Epithelial-mesenchymal transition (EMT) is a cellular state transition fundamental to development, wound healing, and cancer metastasis. The gene regulatory mechanisms underlying EMT have been extensively documented, revealing gene regulatory networks (GRNs) involving groups of mutually inhibiting transcription…
Gonzalo A. Ruz
Gene regulatory network modeling often requires balancing predictive accuracy and mechanistic interpretability. In this work, we compare continuous surrogate models and a discrete mechanistic model on the same \textit{Arabidopsis thaliana} induced systemic resistance (ISR) dataset, using both the raw continuous…
Adolphus Wagala, Samur Mehmet, Giovanni Parmigiani
Boolean matrix factorization provides an interpretable framework for discovering latent binary patterns in high-dimensional data, yet existing methods typically analyze a single binary matrix or factorize multiple matrices independently, failing to exploit shared latent structure across related datasets. We propose…