15 papers · ranked by Valyu relevance
Samuel Pastva, David Šafránek, Nikola Beneš, Luboš Brim + 1 more
Recent developments in both computational analysis and data-driven synthesis enable a new era of automated reasoning with logical models (Boolean networks in particular) in systems biology. However, these advancements also motivate an increased focus on quality control and performance comparisons between tools. At the…
Van-Giang Trinh, Kyu Hyong Park, Samuel Pastva, Jordan C Rozum
Boolean networks are popular dynamical models of cellular processes in systems biology. Their attractors model phenotypes that arise from the interplay of key regulatory subcircuits. A succession diagram describes this interplay in a discrete analog of Waddington’s epigenetic attractor landscape that allows for fast…
Stéphanie Chevalier, Julia Becker, Yujuan Gui, Vincent Noël + 9 more
Boolean networks provide robust explainable and predictive models of cellular dynamics, especially for cellular differentiation and fate decision processes. Yet, the construction of such models is extremely challenging, as it requires integrating prior knowledge with experimental observation of transcriptome…
Gustavo Maganã López, Laurence Calzone, Andrei Zinovyev, Loïc Paulevé
Boolean networks are largely employed to model the qualitative dynamics of cell fate processes by describing the change of binary activation states of genes and transcription factors with time. Being able to bridge such qualitative states with quantitative measurements of gene expressions in cells, as scRNA-Seq, is a…
Kyu Hyong Park, Réka Albert
Comprehensive analysis of the dynamics of Boolean models of biological systems is hampered by the exponentially large state space. Here we introduce the succession-diagram-based Markov chain (SD Markov chain), a coarse-grained representation that uses trap spaces (unescapable state subspaces) of the Boolean model as…
Aravind Karanam, David He, Po-Kai Hsu, Sebastian Schulze + 4 more
Signaling networks are at the heart of almost all biological processes. Most of these networks contain a large number of components and often the connections between these components are either not known, or the rate equations that govern the dynamics of soluble signaling components are not quantified. This uncertainty…
Arnaud Poret, Claudio Monteiro Sousa, Jean-Pierre Boissel
Due to the scarcity of quantitative details about biological phenomena, quantitative modeling in systems biology can be compromised, especially at the subcellular scale. One way to get around this is qualitative modeling because it requires few to no quantitative information. One of the most popular qualitative…
Guy Karlebach
Gene regulatory networks (GRNs) are increasingly used for explaining biological processes with complex transcriptional regulation. A GRN links the expression levels of a set of genes via regulatory controls that gene products exert on one another. Boolean networks are a common modeling choice since they balance between…
Antonio Bensussen, J. Arturo Arciniega-González, Elena R. Álvarez-Buylla, Juan Carlos Martínez-García
Characterizing the minimum, necessary and sufficient components to generate the dynamics of a biological system has always been a priority to understand its functioning. In this sense, the canonical form of biological systems modeled by Boolean networks accurately defines the components in charge of controlling the…
Gang Yang, Jorge G. T. Zañudo, Réka Albert
Dynamical models of biomolecular networks are successfully used to understand the mechanisms underlying complex diseases and to design therapeutic strategies. Network control, and its special case of target control, is a promising avenue toward developing disease therapies. In target control it is assumed that a small…
Ajay Subbaroyan, Olivier C. Martin, Areejit Samal
The properties of random Boolean networks as models of gene regulation have been investigated extensively by the statistical physics community. In the past two decades, there has been a dramatic increase in the reconstruction and analysis of Boolean models of biological networks. In such models, neither network…
Wenpin Hou, Peiying Ruan, Wai-Ki Ching, Tatsuya Akutsu
It is known that many driver nodes are required to control complex biological networks. Previous studies imply that O(N) driver nodes are required in both linear complex network and Boolean network models with N nodes if an arbitrary state is specified as the target. In this paper, we mathematically prove under a…
Bingyu Jiang, Pierre Klemmer, Marek Ostaszewski
Boolean networks have become essential tools for modeling gene regulatory systems and understanding cellular decision-making processes, but their optimization for biological relevance remains challenging. This study presents a comprehensive benchmark comparison of three prominent Boolean network optimization…
Marco Fariñas, Eirini Tsirvouli, John Zobolas, Tero Aittokallio + 2 more
Boolean models are widely used 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 capabilities…
Kyu Hyong Park, Jordan C. Rozum, Réka Albert
Network-based dynamic modeling is useful for studying the responses of complex biomolecular systems to environmental changes and internal perturbations. In modeling signal transduction and other regulatory networks, it is common to integrate evidence from perturbation (e.g. gene knockout) - observation pairs, where the…