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Boolean networks are discrete dynamical systems for modeling regulation and signaling in living cells. We investigate a particular class of Boolean functions with inhibiting inputs exerting a veto (forced zero) on the output. We give analytical expressions for the sensitivity of these functions and provide evidence for their role in natural systems. In an intracellular signal transduction network [Helikar et al., Proc. Natl. Acad. Sci. USA 105, 1913 (2008)], the functions with veto are over-represented by a factor exceeding the over-representation of threshold functions and canalyzing functions in the same system. In Boolean networks for control of the yeast cell cycle [Li et al., Proc. Natl. Acad. Sci. USA 101, 4781 (2004); Davidich et al., PLoS ONE 3, e1672 (2008)], no or minimal changes to the wiring diagrams are necessary to formulate their dynamics in terms of the veto functions introduced here.
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http://dx.doi.org/10.1103/PhysRevE.90.022815 | DOI Listing |
J Theor Biol
September 2025
Department of Bioengineering, Indian Institute of Science, Bengaluru 560012, India. Electronic address:
Several computational models are available for representing the gene expression process, with each having their advantages and disadvantages. Phenomenological models are widely used as they make appropriate simplifications that aim to find a middle ground between accuracy and complexity. The existing phenomenological models compete in terms of how the transcription initiation process is approximated, to achieve high accuracy while having the lowest complexity possible.
View Article and Find Full Text PDFPLoS One
September 2025
Department of Biological Sciences, University of Limerick, Limerick, Ireland.
This study investigates the interaction between circadian rhythms and lipid metabolism disruptions in the context of obesity. Obesity is known to interfere with daily rhythmicity, a crucial process for maintaining brain homeostasis. To better understand this relationship, we analyzed transcriptional data from mice fed with normal or high-fat diet, focusing on the mechanisms linking genes involved with those regulating circadian rhythms.
View Article and Find Full Text PDFPLoS One
September 2025
Information Technologies and Programming Faculty, ITMO University, Saint Petersburg, Russia.
In the paper we consider the well-known Influence Maximization (IM) and Target Set Selection (TSS) problems for Boolean networks under Deterministic Linear Threshold Model (DLTM). The main novelty of our paper is that we state these problems in the context of pseudo-Boolean optimization and solve them using evolutionary algorithms in combination with the known greedy heuristic. We also propose a new variant of (1 + 1)-Evolutionary Algorithm, which is designed to optimize a fitness function on the subset of the Boolean hypercube comprised of vectors of a fixed Hamming weight.
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September 2025
Faculty of Education, Universidad de La Sabana, Chía, Cundinamarca, Colombia.
This study examines how democratic values have been promoted through natural sciences education over the last 50 years, providing a comprehensive analysis based on a systematic review of relevant literature. The central problem addressed is understanding the role of natural science education in fostering democratic values such as equity, participation, critical thinking, and ethical responsibility. This research aims to identify and analyze strategies, methodologies, and transformative experiences that contribute to the promotion of democratic values.
View Article and Find Full Text PDFBiosystems
August 2025
Escuela de Ingeniería, Facultad de Ciencias, Ingeniería y Tecnología, Universidad Mayor, Santiago, Chile. Electronic address:
Threshold Boolean networks are widely used to model gene regulatory systems and social dynamics such as consensus formation. In these networks, each node takes a binary value (0 or 1), leading to an exponential growth in the number of possible configurations with the number of nodes (2). Inferring such networks involves learning a weight matrix and threshold vector from configuration data.
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