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Biochemical and genetic regulatory networks are often modeled by Petri nets. We study the algebraic structure of the computations carried out by Petri nets from the viewpoint of algebraic automata theory. Petri nets comprise a formalized graphical modeling language, often used to describe computation occurring within biochemical and genetic regulatory networks, but the semantics may be interpreted in different ways in the realm of automata. Therefore, there are several different ways to turn a Petri net into a state-transition automaton. Here, we systematically investigate different conversion methods and describe cases where they may yield radically different algebraic structures. We focus on the existence of group components of the corresponding transformation semigroups, as these reflect symmetries of the computation occurring within the biological system under study. Results are illustrated by applications to the Petri net modelling of intermediary metabolism. Petri nets with inhibition are shown to be computationally rich, regardless of the particular interpretation method. Along these lines we provide a mathematical argument suggesting a reason for the apparent all-pervasiveness of inhibitory connections in living systems.
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http://dx.doi.org/10.1016/j.biosystems.2008.05.019 | DOI Listing |
Bioinformation
May 2025
Department of Biotechnology, Swami Vivekanand Subharti University, Subhartipuram, NH-58, Delhi-Haridwar Bypass Road, Meerut-250005, India.
Thiamine diphosphate (TPP) is essential cofactor in H37Rv metabolism, making its biosynthesis pathway a key target for therapy. Therefore, it is of interest to describe a Petri net-based model of the TPP biosynthesis super-pathway, developed using curated MetaCyc data and simulated with Snoopy software. The model integrates three biosynthetic branches and maps key enzymes (ThiC, ThiD, ThiE, ThiF, ThiG, ThiS) along with their gene identifiers.
View Article and Find Full Text PDFIEEE Trans Comput Biol Bioinform
January 2025
Graphlet Degree Distribution Agreement (GDDA) is a comparison metric created for graphlets. Unfortunately, it has been reported and confirmed that it has an issue with the stability of result values in low-density graphs. Recently, graphlets have been modified for comparison of Petri net-based models of biological systems.
View Article and Find Full Text PDFBiosystems
October 2025
Molecular Bioinformatics Group, Institute of Computer Science, Goethe University Frankfurt, Robert-Mayer-Str. 11-15, 60325 Frankfurt am Main, Germany.
The cytokines interleukin 6 (IL-6) and interleukin 22 (IL-22) are involved in multiple signaling pathways in a variety of cells, e.g. the activation of the acute-phase response, cell homeostasis and tissue repair.
View Article and Find Full Text PDFStochastic Petri Nets (SPNs) are an increasingly popular tool of choice for modeling discrete-event dynamics in areas such as epidemiology and systems biology, yet their parameter estimation remains challenging in general and in particular when transition rates depend on external covariates and explicit likelihoods are unavailable. We introduce a neural-surrogate (neural-network-based approximation of the posterior distribution) framework that predicts the coefficients of known covariate-dependent rate functions directly from noisy, partially observed token trajectories. Our model employs a lightweight 1D Convolutional Residual Network trained end-to-end on Gillespie-simulated SPN realizations, learning to invert system dynamics under realistic conditions of event dropout.
View Article and Find Full Text PDFJ Integr Bioinform
March 2025
Bioinformatics Unit, Pasteur Institute of Montevideo, Montevideo, Uruguay.
Stem cells are capable of self-renewal and differentiation into various cell types, showing significant potential for cellular therapies and regenerative medicine, particularly in cardiovascular diseases. The differentiation to cardiomyocytes replicates the embryonic heart development, potentially supporting cardiac regeneration. Cardiomyogenesis is controlled by complex post-transcriptional regulation that affects the construction of gene regulatory networks (GRNs), such as: alternative polyadenylation (APA), length changes in untranslated regulatory regions (3'UTRs), and microRNA (miRNA) regulation.
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