523 research outputs found
Inductive Pattern Formation
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
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Shape theory and mathematical design of a general geometric kernel through regular stratified objects
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This dissertation focuses on the mathematical design of a unified shape kernel for geometric computing, with possible applications to computer aided design (CAM) and manufacturing (CAM), solid geometric modelling, free-form modelling of curves and surfaces, feature-based modelling, finite element meshing, computer animation, etc.
The generality of such a unified shape kernel grounds on a shape theory for objects in some Euclidean space. Shape does not mean herein only geometry as usual in geometric modelling, but has been extended to other contexts, e. g. topology, homotopy, convexity theory, etc. This shape theory has enabled to make a shape analysis of the current geometric kernels. Significant deficiencies have been then identified in how these geometric kernels represent shapes from different applications.
This thesis concludes that it is possible to construct a general shape kernel capable of representing and manipulating general specifications of shape for objects even in higher-dimensional Euclidean spaces, regardless whether such objects are implicitly or parametrically defined, they have ‘incomplete boundaries’ or not, they are structured with more or less detail or subcomplexes, which design sequence has been followed in a modelling session, etc. For this end, the basic constituents of such a general geometric kernel, say a combinatorial data structure and respective Euler operators for n-dimensional regular stratified objects, have been introduced and discussed
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Automated analysis and validation of open chemical data
Methods to automatically extract Open Data from the chemical literature,
validate it, and use it to validate theory are examined.
Chemical identifiers which assist the automatic location of chemical structures
using commercial Web search engines are investigated. The IUPAC
International Chemical Idenfitifer (InChI) gives almost 100% recall and precision,
though is shown to be too long for present search engines. A combination
of InChI and InChIKey, a shorter, fixed-length hash of the InChI
string, is concluded to be the best current method of identifying structures.
The proportion of published, Open Crystallographic Information Files
(CIFs) that are valid with respect to the specification is shown to be improving,
and is around 99% in 2007. The error rate in the conversion of valid
CIFs to Chemical Markup Language (CML) is less than 0.2%. The machine
generation of connection tables from CIFs requires many heuristics, and in
some cases it is impossible to deduce the exact connection table.
CrystalEye, a fully-automated system for the reformulation of the fragmented
crystallographic Web into a structured XML-based repository is described.
Published, Open CIFs can be located and aggregated programmatically
with almost 100% recall. It is shown that, by converting CIF data
to CML, software can be created to use the latest Web standards and technologies
to enhance the ability of Web users to browse, find, keep updated,
download and reuse the latest published crystallography.
A workflow for the high-throughput calculation of solid-state geometry
using a semi-empirical method is described. A wide-range of organic and
inorganic systems provided by CrystalEye are used to test both the data and
the method. Several errors in the method are discovered, many of which can
be attributed to the parameterization process.
An Open NMR experiment to perform high-throughput prediction of 13C
chemical shifts using a GIAO protocol is described. The data and analysis
were provided on publicly-available webpages to enable crowdsourcing, which
assisted in discovering an error rate of 6.1% in the starting data. The protocol
was refined during the work and shown to have an average unsigned error
of 2.24ppm for 13C nuclei of small, rigid molecules; comparable to the errors
observed elsewhere for general structures using HOSE and Neural Network
methods
Exploiting bacterial DNA gyrase as a drug target: current state and perspectives
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
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
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
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In crystallo-screening for discovery of human norovirus 3C-like protease inhibitors.
Outbreaks of human epidemic nonbacterial gastroenteritis are mainly caused by noroviruses. Viral replication requires a 3C-like cysteine protease (3CLpro) which processes the 200 kDa viral polyprotein into six functional proteins. The 3CLpro has attracted much interest due to its potential as a target for antiviral drugs. A system for growing high-quality crystals of native Southampton norovirus 3CLpro (SV3CP) has been established, allowing the ligand-free crystal structure to be determined to 1.3 Å in a tetrameric state. This also allowed crystal-based fragment screening to be performed with various compound libraries, ultimately to guide drug discovery for SV3CP. A total of 19 fragments were found to bind to the protease out of the 844 which were screened. Two of the hits were located at the active site of SV3CP and showed good inhibitory activity in kinetic assays. Another 5 were found at the enzyme's putative RNA-binding site and a further 11 were located in the symmetric central cavity of the tetramer
Application of Graph Neural Networks and graph descriptors for graph classification
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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