523 research outputs found

    Inductive Pattern Formation

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    With the extended computational limits of algorithmic recursion, scientific investigation is transitioning away from computationally decidable problems and beginning to address computationally undecidable complexity. The analysis of deductive inference in structure-property models are yielding to the synthesis of inductive inference in process-structure simulations. Process-structure modeling has examined external order parameters of inductive pattern formation, but investigation of the internal order parameters of self-organization have been hampered by the lack of a mathematical formalism with the ability to quantitatively define a specific configuration of points. This investigation addressed this issue of quantitative synthesis. Local space was developed by the Poincare inflation of a set of points to construct neighborhood intersections, defining topological distance and introducing situated Boolean topology as a local replacement for point-set topology. Parallel development of the local semi-metric topological space, the local semi-metric probability space, and the local metric space of a set of points provides a triangulation of connectivity measures to define the quantitative architectural identity of a configuration and structure independent axes of a structural configuration space. The recursive sequence of intersections constructs a probabilistic discrete spacetime model of interacting fields to define the internal order parameters of self-organization, with order parameters external to the configuration modeled by adjusting the morphological parameters of individual neighborhoods and the interplay of excitatory and inhibitory point sets. The evolutionary trajectory of a configuration maps the development of specific hierarchical structure that is emergent from a specific set of initial conditions, with nested boundaries signaling the nonlinear properties of local causative configurations. This exploration of architectural configuration space concluded with initial process-structure-property models of deductive and inductive inference spaces. In the computationally undecidable problem of human niche construction, an adaptive-inductive pattern formation model with predictive control organized the bipartite recursion between an information structure and its physical expression as hierarchical ensembles of artificial neural network-like structures. The union of architectural identity and bipartite recursion generates a predictive structural model of an evolutionary design process, offering an alternative to the limitations of cognitive descriptive modeling. The low computational complexity of these models enable them to be embedded in physical constructions to create the artificial life forms of a real-time autonomously adaptive human habitat

    Exploiting bacterial DNA gyrase as a drug target: current state and perspectives

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    DNA gyrase is a type II topoisomerase that can introduce negative supercoils into DNA at the expense of ATP hydrolysis. It is essential in all bacteria but absent from higher eukaryotes, making it an attractive target for antibacterials. The fluoroquinolones are examples of very successful gyrase-targeted drugs, but the rise in bacterial resistance to these agents means that we not only need to seek new compounds, but also new modes of inhibition of this enzyme. We review known gyrase-specific drugs and toxins and assess the prospects for developing new antibacterials targeted to this enzyme

    Analysing directed network data

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    The topology of undirected biological networks, such as protein-protein interaction networks, or genetic interaction networks, has been extensively explored in search of new biological knowledge. Graphlets, small connected non-isomorphic induced sub-graphs of an undirected network, have been particularly useful in computational network biology. Having in mind that a significant portion of biological networks, such as metabolic networks or transcriptional regulatory networks, are directed by nature, we define all up to four node directed graphlets and orbits and implement the directed graphlet and graphlet orbits counting algorithm. We generalise all existing graphlet based measures to the directed case, defining: relative directed graphlet frequency distance, directed graphlet degree distribution similarity, directed graphlet degree vector similarity, and directed graphlet correlation distance. We apply new topological measures to metabolic networks and show that the topology of directed biological networks is correlated with biological function. Finally, we look for topology–function relationships in metabolic networks that are conserved across different species.Open Acces

    Elucidating the Interactions between Gut Bacterial β-Glucuronidases and Approved Drugs

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    The human gut microbiome contains a plethora of enzymes that metabolize a myriad ofdiet-derived, host-derived, and therapeutic agents. Metabolism of therapeutic agents by gutmicrobiota can result in altered drug efficacy and toxicity. An important bacterial enzymeinvolved in the metabolism of drugs in the gastrointestinal tract is the gut bacterial β-glucuronidase (GUS). One notable example of drug metabolism by GUS enzymes is thereactivation of the active metabolite of the chemotherapeutic irinotecan, SN-38, which causessevere, dose limiting toxicity in patients. Recent analysis of the Human Microbiome Projectrevealed 279 unique GUS isoforms encoded by gut microbiota. Elucidating the exact GUSisoform reactivating drugs like SN-38 can lead to precise dosing of drugs on an individual basisand reduce gut microbiota-mediated toxic side effects. In this dissertation, a novel activity-basedproteomics strategy is used to identify the exact GUS isoform responsible for the reactivation ofSN-38. In addition to the metabolism of drugs by GUS enzymes, another important interaction isthe inhibition of GUS enzymes by FDA-approved drugs. Inhibition of GUS activity can result inaltered homeostasis because GUS enzymes also reactivate host-derived endobiotics and processdiet-derived glucuronic acid-containing polysaccharides. We present data showing that approveddrugs with a particular chemical scaffold can inhibit the activity of GUS enzymes. Altering theabundance or activity of GUS enzymes can potentially reduce GUS-mediated metabolism oftherapeutics. We next present data on how dietary fiber influences the abundance of GUSivencoding gut bacterial species. These data provide information on how diet can potentially beused to modulate GUS activity in the gut. In addition to diet, another approach to reduce themetabolism of drugs by GUS and other gut microbial enzymes is using small moleculeadjuvants. The final chapter outlines a chemoproteomics platform that could be used to discoverselective small molecule inhibitors that reduce the metabolism of any drug of interest. Together,the work presented here expands on our knowledge of GUS-drug interactions, discusses methodsto alter GUS activity, and outlines a chemoproteomics platform to identify small moleculeinhibitors of gut enzymes.Doctor of Philosoph

    Application of Graph Neural Networks and graph descriptors for graph classification

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    Graph classification is an important area in both modern research and industry. Multiple applications, especially in chemistry and novel drug discovery, encourage rapid development of machine learning models in this area. To keep up with the pace of new research, proper experimental design, fair evaluation, and independent benchmarks are essential. Design of strong baselines is an indispensable element of such works. In this thesis, we explore multiple approaches to graph classification. We focus on Graph Neural Networks (GNNs), which emerged as a de facto standard deep learning technique for graph representation learning. Classical approaches, such as graph descriptors and molecular fingerprints, are also addressed. We design fair evaluation experimental protocol and choose proper datasets collection. This allows us to perform numerous experiments and rigorously analyze modern approaches. We arrive to many conclusions, which shed new light on performance and quality of novel algorithms. We investigate application of Jumping Knowledge GNN architecture to graph classification, which proves to be an efficient tool for improving base graph neural network architectures. Multiple improvements to baseline models are also proposed and experimentally verified, which constitutes an important contribution to the field of fair model comparison.Comment: Master's thesis submitted at AGH University of Science and Technolog
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