Evolutionary Computation

This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. The intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field

Predicting and characterising protein-protein complexes

Macromolecular interactions play a key role in all life processes. The construction and annotation of protein interaction networks is pivotal for the understanding of these processes, and how their perturbation leads to disease. However the extent of the human interactome and the limitations of the experimental techniques which can be brought to bear upon it necessitate theoretical approaches. Presented here are computational investigations into the interactions between biological macromolecules, focusing on the structural prediction of interactions, docking, and their kinetic and thermodynamic characterisation via empirical functions. Firstly, the use of normal modes in docking is investigated. Vibrational analysis of proteins are shown to indicate the motions which proteins are intrinsically disposed to undertake, and the use of this information to model flexible deformations upon protein-protein binding is evaluated. Subsequently SwarmDock, a docking algorithm which models flexibility as a linear combination of normal modes, is presented and benchmarked on a wide variety of test cases. This algorithm utilises state of the art energy functions and metaheuristics to navigate the free energy landscape. Information derived from Langevin dynamics simulations of encounter complex formation in the crowded cytosolic environment can be incorporated into SwarmDock and enhances its performance. Finally, a benchmark of binding free energies derived from the literature is presented. For this benchmark, a large number of molecular descriptors are derived. Machine learning methods are then applied to these in order to derive empirical binding free energy, association rate and dissociation rate functions which take account of the conformational changes which occur upon complexation

A theoretical study of metal-organic frameworks

Among the options for carbon sequestration, the development of CO2 capture materials has gained momentum over the past two decades. The design and construction of chemical and physical absorbents for the capture of CO2 and clean energy storage are a crucial technology for a sustainable low-carbon future. Metal-organic frameworks (MOFs) provide a new vision for the adsorption of molecules on solid surfaces. The interest in MOFs is owed to their ultrahigh porosity, high surface areas and tuneable pore sizes and shapes. The main objective of this thesis was to adopt a rational predictive capacity used in MOF design to control properties such as framework porosity and flexibility on a molecular scale. The in-silico studies were carried out by using ab initio quantum mechanical approaches such as density functional theory and perturbation theory. In addition, semi-classical methods like the Grand Canonical Monte Carlo (GCMC) approach was also used. A structural motif called vicinal fluorination was adopted to study MOF linkers in isolation and in a framework. An extensive conformational study, in various solvents, was carried out to investigate the effect of vicinal fluorination on the isolated MOF linkers and therefore elucidate their conformational stability. The effect of fluorination on adsorption isotherms was also investigated. Moreover, various fluorination patterns were explored. Adsorption isotherms of a non-fluorinated copperbased MOF based on experimental work, and its various fluorinated analogues were predicted using the GCMC method. It was found that vicinal fluorination is not dominant in controlling conformations of some MOF linkers. Rather, an interplay of interactions, including solute and steric interactions, influence the conformational stability on rotational profiles. However, vicinal fluorination was shown to control the flexibility of the linkers used in MOFs as it controls the force constants around the minima of rotational profiles of isolated MOF linkers. The study also highlighted the importance of the solvent on the relative energies of the linker conformations – this has a potential impact on the synthesis of MOFs. With the help of computational methods and validation from experimental data, the structural and sorption properties of the framework, upon fluorination, were shown to have consequences on the adsorption properties of the MOF. Vicinally fluorinated frameworks were shown to have higher uptakes at a low temperature and low pressures

A Perspective on Conventional High-Temperature Superconductors at High Pressure: Methods and Materials

Two hydrogen-rich materials, H$_3$S and LaH$_{10}$, synthesized at megabar pressures, have revolutionized the field of condensed matter physics providing the first glimpse to the solution of the hundred-year-old problem of room temperature superconductivity. The mechanism underlying superconductivity in these exceptional compounds is the conventional electron-phonon coupling. Here we describe recent advances in experimental techniques, superconductivity theory and first-principles computational methods which have made possible these discoveries. This work aims to provide an up-to-date compendium of the available results on superconducting hydrides and explain how the synergy of different methodologies led to extraordinary discoveries in the field. Besides, in an attempt to evidence empirical rules governing superconductivity in binary hydrides under pressure, we discuss general trends in the electronic structure and chemical bonding. The last part of the Review introduces possible strategies to optimize pressure and transition temperatures in conventional superconducting materials as well as future directions in theoretical, computational and experimental research.Comment: 68 pages, 30 figures, Preprint submitted to Physics Report

Industrial machine structural components’ optimization and redesign

Tese de doutoramento em Líderes para as Indústrias TecnológicasO corte por laser é um processo altamente flexível com numerosas vantagens sobre tecnologias concorrentes. O crescimento do mercado é revelador do seu potencial, totalizando 4300 milhões de dólares americanos em 2020. O processo é utilizado em muitas indústrias e as tendências atuais passam por melhorias ao nível do tempo de ciclo, qualidade, custos e exatidão. Os materiais compósitos (nomeadamente polímeros reforçados por fibras) apresentam propriedades mecânicas atrativas para várias aplicações, incluindo a que se relaciona com o presente trabalho: componentes de máquinas industriais. A utilização de compósitos resulta tipicamente em máquinas mais eficientes, exatidão dimensional acrescida, melhor qualidade superficial, melhor eficiência energética e menor impacto ambiental. O principal objetivo deste trabalho é aumentar a produtividade de uma máquina de corte laser, através do redesign de um componente crítico (o pórtico), grande influenciador da exatidão da máquina. Pretende-se com isto criar uma metodologia genérica capaz de auxiliar no processo de redesign de componentes industriais. Dado que o problema lida com dois objetivos concorrentes (redução de peso e aumento de rigidez) e com um elevado número de variáveis, a implementação de uma rotina de otimização é um aspeto central. É crucial demonstrar que o processo de otimização proposto resulta em soluções efetivas. Estas foram validadas através de análise de elementos finitos e de validação experimental, com recurso a um protótipo à escala. O algoritmo de otimização usado é uma metaheurística, inspirado no comportamento de grupos de animais. Algoritmos Particle Swarm são sugeridos com sucesso para problemas de otimização semelhantes. A otimização focou-se na espessura de cada laminado, para diferentes orientações. A rotina de otimização resultou na definição de uma solução quase-ótima para os laminados analisados e permitiu a redução do peso da peça em 43% relativamente à solução atual, bem como um aumento de 25% na aceleração máxima permitida, o que se reflete na produtividade da máquina, enquanto a mesma exatidão é garantida. A comparação entre os resultados numéricos e experimentais para os protótipos mostra uma boa concordância, com divergências pontuais, mas que ainda assim resultam na validação do modelo de elementos finitos no qual se baseia a otimização.Laser cutting is a highly flexible process with numerous advantages over competing technologies. These have ensured the growth of its market, totalling 4300 million United States dollars in 2020. Being used in many industries, the current trends are focused on reduced lead time, increased quality standards and competitive costs, while ensuring accuracy. Composite materials (namely fibre reinforced polymers) present attractive mechanical properties that poses them as advantageous for several applications, including the matter of this thesis: industrial machine components. The use of these materials leads to machines with higher efficiency, dimensional accuracy, surface quality, energy efficiency, and environmental impact. The main goal of this work is to increase the productivity of a laser cutting machine through the redesign of a critical component (gantry), also key for the overall machine accuracy. Beyond that, it is intended that this work lays out a methodology capable of assisting in the redesign of other machine critical components. As the problem leads with two opposing objectives (reducing weight and increasing stiffness), and with many variables, the implementation of an optimization routine is a central aspect of the present work. It is of major importance that the proposed optimization method leads to reliable results, demonstrated in this work by a finite element analysis and through experimental validation, by means of a scale prototype. The optimization algorithm selected is a metaheuristic inspired by the behaviour of swarms of animals. Particle swarm algorithms are proven to provide good and fast results in similar optimization problems. The optimization was performed focusing on the thickness of each laminate and on the orientations present in these. The optimization routine resulted in a definition of a near-optimal solution for the laminates analysed and allowed a weight reduction of 43% regarding the current solution, as well as an increase of 25% in the maximum allowed acceleration, which reflects on the productivity of the machine, while ensuring the same accuracy. The comparison between numeric and experimental testing of the prototypes shows a good agreement, with punctual divergences, but that still validates the Finite elements upon which the optimization process is supported.Portuguese Foundation for Science and Technology - SFRH/BD/51106/2010

Structures, Structural Transfromations and Properties of Selected Elemental and Extended Solids

The current boom in computer power has created avenue to study materials’ properties under extreme thermodynamic conditions where experimental characterization is very challenging. This thesis is an aggregation of several objectives ranging from the study of elemental as well as extended materials for technological, high energy density (HED), and geophysical applications; all at high pressure. The density functional theory (DFT), ab initio metadynamics and ab initio molecular dynamics (AIMD) methods have been employed to analyze structural phase transitions, electronic, vibrational, and mechanical properties of selected materials at high pressure. Where available, high-pressure-high-temperature (HPHT) experiments were combined with the various theoretical methods for complete elucidation of the system. The first set of projects in this thesis involve study of structural phase transition in two elements: carbon (C) and nitrogen (N). The first part presents the results of structural phase transition in a two-dimensional polymeric C60 after being subjected to uniaxial compression at high temperature in a metadynamics simulation. The new structure exhibits a mixed sp2/sp3 hybridization. The structure is stable at ambient condition and exhibits superior mechanical performance than most of widely used hard ceramics. The second part presents theoretical results on the identification, and characterization of single bonded nitrogen in crystal structure isostructural to black phosphorus (BP-N) at 146 GPa and 2200 K. The crystal structure exhibits a unique puckered two-dimensional layer exhibiting exciting physical and chemical phenomena including prospect for high energy density (HED) applications. Synchrotron x-ray diffraction and Raman spectroscopy were used for experimental characterization of the BP-N. First-principles methods were employed in the theoretical characterization. The second set of projects involve the theoretical studies of transition metal (TM) -TM alloys/compounds. The first part of the chapter investigates structural phase transition leading to shape memory loss in the shape memory alloy NiTi. The second part investigates the formation of Au-Fe compounds at high pressure. A detailed analysis of the transition kinetics and dynamical pathway in NiTi using the metadynamics method reveals the possibility of the B19′ phase of NiTi losing its shape memory when subjected to high stress conditions and heated above a critical temperature (Tc) of 700 K. Using the particle swarm-intelligence optimization algorithm interfaced with first principles methods, we predicted the formation of bulk intermetallic compounds of two bulk-immiscible components, Fe and Au. the systems are stabilized by pressure and notable electron transfer. Next, the results of theoretical studies of the formation of noble gas element - TM compound were presented. The identification of a thermodynamically stable compound of Argon (Ar) and nickel (Ni) under thermodynamic conditions representative of the Earth’s core using density functional calculations were presented. The study present evidence of the reactability of Ar with one of the Earth’s core’s main constituents, Ni. The compound of Ar and Ni was identified as ArNi with a L11 Laves structure. It was found that ArNi compound is stabilized by notable electron transfer from Ni to Ar. The final project is an extensive theoretical study of the formation of alkali metal-transition metal intermetallic compounds at high pressure and temperature relevant to the upper mantle and the core of the Earth. These studies were carried out using particle swarm-intelligence optimization and genetic algorithms interfaced with first principles methods. The first part investigates the formation of K-Fe compounds at thermodynamics conditions relevant to the Earth’s interior while the second part investigates the formation of K-Ni compounds in the Earth’s interior. It was found that K and Fe can form intermetallic compounds that are stabilized by high pressure and energy reordering of atomic orbital. Phase transitions were also reported and the instabilities that induce them were also investigated. Furthermore, the study on K-Ni systems identify the crystal structure for the long-sought structure of the only experimentally known K-Ni compound to date. The identified K2Ni exhibits a semiconducting ground state with an indirect bandgap. The results of both studies indicate that the chemical properties of elements can change dramatically under extreme conditions and could have significant implications for understanding the Earth’s interior

Complexity, Emergent Systems and Complex Biological Systems:\ud Complex Systems Theory and Biodynamics. [Edited book by I.C. Baianu, with listed contributors (2011)]

An overview is presented of System dynamics, the study of the behaviour of complex systems, Dynamical system in mathematics Dynamic programming in computer science and control theory, Complex systems biology, Neurodynamics and Psychodynamics.\u

Computational Approaches To Anti-Toxin Therapies And Biomarker Identification

This work describes the fundamental study of two bacterial toxins with computational methods, the rational design of a potent inhibitor using molecular dynamics, as well as the development of two bioinformatic methods for mining genomic data. Clostridium difficile is an opportunistic bacillus which produces two large glucosylating toxins. These toxins, TcdA and TcdB cause severe intestinal damage. As Clostridium difficile harbors considerable antibiotic resistance, one treatment strategy is to prevent the tissue damage that the toxins cause. The catalytic glucosyltransferase domain of TcdA and TcdB was studied using molecular dynamics in the presence of both a protein-protein binding partner and several substrates. These experiments were combined with lead optimization techniques to create a potent irreversible inhibitor which protects 95% of cells in vitro. Dynamics studies on a TcdB cysteine protease domain were performed to an allosteric communication pathway. Comparative analysis of the static and dynamic properties of the TcdA and TcdB glucosyltransferase domains were carried out to determine the basis for the differential lethality of these toxins. Large scale biological data is readily available in the post-genomic era, but it can be difficult to effectively use that data. Two bioinformatics methods were developed to process whole-genome data. Software was developed to return all genes containing a motif in single genome. This provides a list of genes which may be within the same regulatory network or targeted by a specific DNA binding factor. A second bioinformatic method was created to link the data from genome-wide association studies (GWAS) to specific genes. GWAS studies are frequently subjected to statistical analysis, but mutations are rarely investigated structurally. HyDn-SNP-S allows a researcher to find mutations in a gene that correlate to a GWAS studied phenotype. Across human DNA polymerases, this resulted in strongly predictive haplotypes for breast and prostate cancer. Molecular dynamics applied to DNA Polymerase Lambda suggested a structural explanation for the decrease in polymerase fidelity with that mutant. When applied to Histone Deacetylases, mutations were found that alter substrate binding, and post-translational modification

Protein structure and function relationships: application of computational approaches to biological and biomedical problems

In this work we have studied several cases by means of different computational approaches for the analysis of the structure and function relationships. In chapter 2 we describe a method, based on multiple neural networks, that we developed for evaluate the accuracy of predicted threedimensional protein structures. This tool has been used in different studies described in this work, in which the prediction of the 3D structure of the protein under study, has been necessary. In chapter 3, the interaction among a new class of natural sweeteners (steviol glycosides) and the human sweet taste receptor, has been analyzed by means of an insilico docking study, which allowed to identify the preferential binding site for the steviol glycosides. In chapter 4 the relationship between the dynamical properties and the function of some psychrophilic enzyme has been studied. A comparative study (psychrophile vs mesophile) of the thermodynamic properties of two different enzymes belonging to the elastases and the uracilDNAglycosylases families has been done. This study, carried out with molecular dynamic simulations, revealed that the low temperature adaptation is related to the different flexibility of the psychrophilic compared to the mesophilic enzyme. In chapter 5, we have studied the structural and functional impact of point mutations on three different proteins which are involved in serious rare diseases which cause grave metabolic disorders