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Motivation: Intervention in a gene regulatory network is used to avoid undesirable states, such as those associated with a disease. Several types of intervention have been studied in the framework of a probabilistic Boolean network (PBN), which is a collection of Boolean networks in which the gene state vector transitions according to the rules of one of the constituent networks and where network choice is governed by a selection distribution. The theory of automatic control has been applied to find optimal strategies for manipulating external control variables that affect the transition probabilities to desirably affect dynamic evolution over a finite time horizon. In this paper we treat a case in which we lack the governing probability structure for Boolean network selection, so we simply have a family of Boolean networks, but where these networks possess a common attractor structure. This corresponds to the situation in which network construction is treated as an ill-posed inverse problem in which there are many Boolean networks created from the data under the constraint that they all possess attractor structures matching the data states, which are assumed to arise from sampling the steady state of the real biological network.
Results: Given a family of Boolean networks possessing a common attractor structure composed of singleton attractors, a control algorithm is derived by minimizing a composite finite-horizon cost function that is a weighted average over all the individual networks, the idea being that we desire a control policy that on average suits the networks because these are viewed as equivalent relative to the data. The weighting for each network at any time point is taken to be proportional to the instantaneous estimated probability of that network being the underlying network governing the state transition. The results are applied to a family of Boolean networks derived from gene-expression data collected in a study of metastatic melanoma, the intent being to devise a control strategy that reduces the WNT5A gene's action in affecting biological regulation.
Availability: The software is available on request.
Supplementary Information: The supplementary Information is available at http://ee.tamu.edu/~edward/tree
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http://dx.doi.org/10.1093/bioinformatics/bti765 | DOI Listing |
Plant J
March 2025
ARC Centre of Excellence in Plants for Space, School of Molecular Sciences, The University of Western Australia, Perth, Australia.
Synthetic gene circuits offer powerful new approaches for engineering plant traits by enabling precise control over gene expression through programmable logical operations. Unlike simple 'always-on' transgenes, circuits can integrate multiple input signals to achieve sophisticated spatiotemporal regulation of target genes while minimising interference with host cellular processes. Recent advances have demonstrated several platforms for building plant gene circuits, including systems based on bacterial transcription factors, site-specific recombinases and CRISPR/Cas components.
View Article and Find Full Text PDFBioinformatics
March 2025
School of Science, Constructor University, Bremen gGmbH Campus Ring 1, 28759, Bremen, Germany.
Motivation: Inferring microbial interaction networks from microbiome data is a core task of computational ecology. An avenue of research to create reliable inference methods is based on a stylized view of microbiome data, starting from the assumption that the presences and absences of microbiomes, rather than the quantitative abundances, are informative about the underlying interaction network. With this starting point, inference algorithms can be based on the notion of attractors (asymptotic states) in Boolean networks.
View Article and Find Full Text PDFNutr Rev
March 2025
Geriatrics and Gerontology Discipline, Paulista School of Medicine, Federal University of São Paulo, São Paulo, SP 04025-002, Brazil.
The dynamic balance between pro- and anti-inflammatory networks decreases as individuals age, and intestinal dysbiosis can initiate and maintain low-grade systemic inflammation. Interactions between the microbiota and humans occur from the beginning of life and, in general, the diversity of microbiota decreases with aging. The microbiome produces different metabolites with systemic effects, including immune system regulation.
View Article and Find Full Text PDFIEEE Trans Cybern
February 2025
In this article, a network attack, named shifting attack, is considered for hidden Markov Boolean control networks (HMBCNs). Using semi-tensor product of matrices, the considered network and the network attack are presented in algebraic form. State feedback control (SFC) is then applied to stabilize the considered network to a desired state.
View Article and Find Full Text PDFEntropy (Basel)
February 2025
Department of Intermediate Art and Science, School of Fundamental Science and Engineering, Waseda University, Tokyo 169-8555, Japan.
Recurrence resonance (RR), in which external noise is utilized to enhance the behaviour of hidden attractors in a system, is a phenomenon often observed in biological systems and is expected to adjust between chaos and order to increase computational power. It is known that connections of neurons that are relatively dense make it possible to achieve RR and can be measured by global mutual information. Here, we used a Boltzmann machine to investigate how the manifestation of RR changes when the connection pattern between neurons is changed.
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