22 papers · ranked by Valyu relevance
Karthik Chandrasekhar, Claus Kadelka, Reinhard Laubenbacher, David Murrugarra
'David Murrugarra'] Stability is an important characteristic of network models that has implications for other desirable aspects such as controllability. The stability of a Boolean network (BN) depends on various factors, such as the topology of its wiring diagram and the type of the functions describing its dynamics.…
David Murrugarra, Alan Veliz-Cuba, Elena Dimitrova, Claus Kadelka + 2 more
'Matthew Wheeler' 'Reinhard Laubenbacher'] The concept of control is crucial for effectively understanding and applying biological network models. Key structural features relate to control functions through gene regulation, signaling, or metabolic mechanisms, and computational models need to encode these. Applications…
David Murrugarra, Alan Veliz-Cuba, Elena Dimitrova, Claus Kadelka + 2 more
'Matthew Wheeler' 'Reinhard Laubenbacher'] The concept of control is crucial for effectively understanding and applying biological network models. Key structural features relate to control functions through gene regulation, signaling, or metabolic mechanisms, and computational models need to encode these. Applications…
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…
Alhazov, Artiom, Freund, Rudolf + 2 more
Membrane computing and P systems are a paradigm of massively parallel natural computing introduced by Gheorghe Paun in 1999, inspired by the structure of the living cell and by its biochemical ˘ reactions. In spite of this explicit biological motivation, P systems have not been extensively used in modelling real-world…
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…
Loïc Paulevé, Cédric Gaucherel
Being able to infer the interactions between a set of species from observations of the system is of paramount importance to obtain explaining and predictive models in ecology. We tackled this challenge by employing qualitative modeling frameworks and logic methods for the synthesis of mathematical models that can…
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…
RAÚL SEBASTIÁN ASTETE ELGUIN
This paper proposes a new parameter for studying Boolean networks: the independence number. We establish that a Boolean network is k-independent if, for any set of k variables and any combination of binary values assigned to them, there exists at least one fixed point in the network that takes those values at the given…
José E.R. Cury, Patrícia Tenera Roxo, Vasco Manquinho, Claudine Chaouiya + 1 more
'Claudine Chaouiya' 'Pedro T. Monteiro'] Boolean networks constitute relevant mathematical models to study the behaviours of genetic and signalling networks. These networks define regulatory influences between molecular nodes, each being associated to a Boolean variable and a regulatory (local) function specifying its…
Santosh Manicka, Manuel Marques-Pita, Luis M. Rocha
Living systems comprise interacting biochemical components in very large networks. Given their high connectivity, biochemical dynamics are surprisingly not chaotic but quite robust to perturbations-a feature C.H. Waddington named canalization. Because organisms are also flexible enough to evolve, they arguably operate…
Artiom Alhazov, Vincent Ferrari-Dominguez, Rudolf Freund, Nicolas Glade + 1 more
'Nicolas Glade' 'Sergiu Ivanov'] Abstract. A Boolean network is a discrete dynamical system operating on vectors of Boolean variables. The action of a Boolean network can be conveniently expressed as a system of Boolean update functions, computing the new values for each component of the Boolean vector as a function of…
Laura Cifuentes-Fontanals, Elisa Tonello, Heike Siebert
Background The study of control mechanisms of biological systems allows for interesting applications in bioengineering and medicine, for instance in cell reprogramming or drug target identification. A control strategy often consists of a set of interventions that, by fixing the values of some components, ensure that…
Qing Ye, Nancy Lan Guo, Huihui Fan, Fulong Yu + 1 more
There are insufficient accurate biomarkers and effective therapeutic targets in current cancer treatment. Multi-omics regulatory networks in patient bulk tumors and single cells can shed light on molecular disease mechanisms. Integration of multi-omics data with large-scale patient electronic medical records (EMRs) can…
Matthew T. Wheeler, Claus Kadelka, Alan Veliz‐Cuba, David Murrugarra + 1 more
'Reinhard Laubenbacher'] Boolean networks have been used in a variety of settings, as models for general complex systems as well as models of specific systems in diverse fields, such as biology, engineering, and computer science. Traditionally, their properties as dynamical systems have been studied through simulation…
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…
Thomas Parmer, Luis M. Rocha, Filippo Radicchi
The optimization problem aiming at the identification of minimal sets of nodes able to drive the dynamics of Boolean networks toward desired long-term behaviors is central for some applications, as for example the detection of key therapeutic targets to control pathways in models of biological signaling and regulatory…
Kevin M. Stoltz, Cliff Joslyn
Boolean networks are a valuable class of discrete dynamical systems models, but they remain fundamentally limited by their inability to capture multi-way interactions in their components. To remedy this limitation, we propose a model of Boolean hypernetworks, which generalize standard Boolean networks. Utilizing the…
Aravind Karanam, Wouter-Jan Rappel
2.1.### Truth tables As we mentioned in Section [sec1], the nodes in a Boolean network can only take on values 0 (OFF) and 1 (ON). The ON state of a variable corresponds to high activity or concentration and the OFF state corresponds to low activity or concentration. The interactions between the nodes are given by 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…