AFRANCI : multi-layer architecture for cognitive agents

Tese de doutoramento. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 201

Functional architectures of polyketide synthases

Microbial polyketide synthases (PKS) are biological factories for the production of potent natural products, which include clinically relevant antibiotics, anti-cancer drugs, statins and more. The exceptional chemical diversity generated by PKSs is encoded in a modular architecture for precursor extension. The domains required for one step of precursor elongation and modification are combined into a functional polypeptide module, which is segregated into a mandatory condensing region for elongation and an optional and variable part for intermediate modification. PKS modules contain integral acyl carrier protein (ACP) domains, flanked by flexible peptide regions. ACPs are used to load substrates and to tether intermediates throughout ongoing synthesis, by linking them as thioesters to a covalently attached phosphopantetheine cofactor. PKS modules can either act iteratively (iPKS) or in a linearly organized assembly line of multiple modules (modPKS), where the nascent polyketide is handed over from one to the next module. The collinearity between synthesis and protein sequence in modPKS holds promise for rational re-engineering in order to produce novel bioactive compounds. Despite their cyclic mode of action, iPKS may employ specific reaction programs, which introduces different substitutions in each iteration by selective use of individual catalytic domains. At the beginning of the thesis, the architecture of PKS modules as a basis for their modular organization and programmed biosynthesis was unknown. This thesis was focused on structural studies of the architecture of PKS modules, intramodular crosstalk and functional programming. Chapter one provides a comprehensive introduction into the molecular biology of PKS function. Chapter two provides a hybrid crystallographic model of an iPKS module and demonstrates its relevance also for modPKS. Overlapping crystal structures of a condensing and a complete modifying region provided the first atomic model of a PKS module with a total of 10 catalytic domains. Multiple crystallogrpahically independent copies observed in the 3.75 Å structure of the dimeric modifying region provided snapshots of a variable linker-based architecture with implications for PKS evolution and conformational coupling of reaction steps in the dimeric synthase. Comparative small angle X-ray scattering demonstrates that the iPKS architecture is also representative for tested modPKSs. Chapter three reports the crystal structure of a programming C-methyltransferase (CMeT) domain at 1.65 Å resolution. The structure reveals a novel N-terminal fold and a substrate binding cavity that accommodates intermediates of various length during iterative biosynthesis. Structural and phylogenetic analysis demonstrates conservation of CMeT domains in PKS as well as homology to an inactive pseudo-CMeT (ΨCMeT) remnant in mammalian fatty acid synthase (mFAS). The data suggest an involvement of the core elongating ketosynthase (KS) domain in PKS programming. Chapter four provides a visualization of substrate loading in iPKS. A 2.8 Å resolution crystal structure provided detailed insights into an intertwined linker-mediated integration of substrate-loading starter-unit acyltransferase (SAT) domains into an iPKS condensing region. The post-loading state was trapped by mechanism-based crosslinking. Visualization by cryo electron microscopy at 7.1 Å resolution revealed asymmetry of ACP-KS interactions and depicts conformational coupling across the dimeric PKS for coordinated synthesis. Chapter five integrates the results into the current structural and biological context and discusses current opinions and future perspectives in the field. The results of this thesis reflect the relevance of linker-based connections rather than stable domain-domain interfaces for PKS architecture. This work also highlights mechanisms for conformational coupling for synthesis and substrate channeling in dimeric, but asymmetric, PKS. These insights will support re-engineering iPKS and modPKS assembly lines for the production of novel bioactive compounds, in particular for drug discovery

Graphene and related materials in hierarchical fiber composites: Production techniques and key industrial benefits

Fiber-reinforced composites (FRC) are nowadays one of the most widely used class of high-tech materials. In particular, sporting goods, cars and the wings and fuselages of airplanes are made of carbon fiber reinforced composites (CFRC). CFRC are mature commercial products, but are still challenging materials. Their mechanical and electrical properties are very good along the fiber axis, but can be very poor perpendicular to it; interfacial interactions have to be tailored for specific applications to avoid crack propagation– and delamination; fiber production includes high-temperature treatments of adverse environmental impact, leading to high costs. Recent research work shows that the performance of CFRC can be improved by addition of graphene or related 2-dimensional materials (GRM). Graphene is a promising additive for CFRC because: 1) Its all-carbon aromatic structure is similar to the one of carbon fiber (CF). 2) Its 2-dimensional shape, high aspect ratio, high flexibility and mechanical strength allow it to be used as a coating on the surface of fiber, or as a mechanical/electrical connection between different fiber layers. 3) Its tunable surface chemistry allows its interaction to be enhanced with either the fiber or the polymer matrix used in the composite and 4) in contrast to carbon fibers or nanotubes, it is easily produced on a large scale at room temperature, without metal catalysts. Here, we summarize the key strategic advantages that could be obtained in this way, and some of the recent results that have been obtained in this field within the Graphene Flagship project and worldwide

Machine learning in hybrid hierarchical and partial-order planners for manufacturing domains

The application of AI planning techniques to manufacturing Systems is being widely deployed for all the tasks involved in the process, from product design to production planning and control. One of these problems is the automatic generation of control sequences for the entire manufacturing system in such a way that final plans can be directly use das the sequential control programs which drive the operation of manufacturing systems. Hybis is a hierarchical and nonlinear planner whose goal is to obtain partially ordered plans at such a level of detail that they can be use das sequential control programs for manufacturing systems. Currently, those sequential control programs are being generated by hand using modelling tools. This document describes a work whose aim is to improve the efficiency of solving problems with Hybis by using machine learning techniques. It implements a deductive learning method that is able to automatically acquire control knowledge (heuristics) by generating bounded explanations of the problem solving episodes. The learning approach builds on Hamlet, a system that learns control knowledge in the form of control rules.This work was partially supported by a grant from the Ministerio de Ciencia y Tecnología through projects TAP1999-0535-C02-02, TIC2001-4936-E, and TIC2002-04146-C05-05.Publicad

Monitoring the Complexity of IT Architectures: Design Principles and an IT Artifact

Monitoring the complexity of a firm’s IT architecture is imperative to ensure a stable and flexible platform foundation for competing in the era of digital business strategy. However, IT architects lack IT support for dealing with this important problem. We engaged with five companies in a significant design science research (DSR) program and drew on the heuristic theorizing framework both to solve this problem through evolving IT artifacts and to accumulate nascent design knowledge. We base the design knowledge development on a conceptual framework involving three essential concepts for understanding and solving this problem: structural complexity, dynamic complexity, and problem-solving complexity. Drawing on this foundation, we address the research question: How can IT support be provided for reducing the problem-solving complexity of monitoring the structural and dynamic complexity of IT architectures in the context of a digital business strategy? To answer this question, we present a set of design principles that we derived from our iterative process of IT artifact construction and evaluation activities with five companies. Our nascent design knowledge contributes to the research on IT architecture management in the context of digital business strategy. In addition, we also contribute to the understanding of how, through the use and illustration of the heuristic theorizing framework, design knowledge can be accumulated systematically on the basis of generalization from IT artifact construction and evaluation outcomes generated across multiple contexts and companies

Knowledge formalization in experience feedback processes : an ontology-based approach

Because of the current trend of integration and interoperability of industrial systems, their size and complexity continue to grow making it more difficult to analyze, to understand and to solve the problems that happen in their organizations. Continuous improvement methodologies are powerful tools in order to understand and to solve problems, to control the effects of changes and finally to capitalize knowledge about changes and improvements. These tools involve suitably represent knowledge relating to the concerned system. Consequently, knowledge management (KM) is an increasingly important source of competitive advantage for organizations. Particularly, the capitalization and sharing of knowledge resulting from experience feedback are elements which play an essential role in the continuous improvement of industrial activities. In this paper, the contribution deals with semantic interoperability and relates to the structuring and the formalization of an experience feedback (EF) process aiming at transforming information or understanding gained by experience into explicit knowledge. The reuse of such knowledge has proved to have significant impact on achieving themissions of companies. However, the means of describing the knowledge objects of an experience generally remain informal. Based on an experience feedback process model and conceptual graphs, this paper takes domain ontology as a framework for the clarification of explicit knowledge and know-how, the aim of which is to get lessons learned descriptions that are significant, correct and applicable