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

Development of methods for combinational approaches to cis-regulatory module interactions

The complexity and size of the higher animal genome and relative scarcity of DNA-binding factors with which to regulate it imply a complex and pleiotropic regulatory system. Cisregulatory modules (CRMs) are vitally important regulators of gene expression in higher animal cells, integrating external and internal information to determine an appropriate response in terms of gene expression by means of direct and indirect interactions with the transcriptional machinery. The interaction space available within systems of multiple CRMs, each containing several sites where one or more factors could be bound is huge. Current methods of investigation involve the removal of individual sites or factors and measuring the resulting effect on gene expression. The effects of investigations of this type may be masked by the functional redundancy present in some of these regulatory systems as a result of their evolutionary development. The investigation of CRM function is limited by a lack of technology to generate and analyse combinatorial mutation libraries of CRMs, where putative transcription factor binding sites are mutated in various combinations to achieve a holistic view of how the factors binding to those sites cooperate to bring about CRM function. The principle work of this thesis is the generation of such a library. This thesis presents the development of microstereolithography as a method for making microfluidic devices, both directly and indirectly. A microfluidic device was fabricated that was used to generate oligonucleotide mixtures necessary to synthesise combinatorial mutants of a CRM sequence from the muscle regulatory factor MyoD. In addition, this thesis presents the development of the optimisation algorithms and assembly processes necessary for successful sequence assembly. Furthermore, it was found that the CRM, in combination with other CRMs, is able to synergistically regulate gene expression in a position and orientation independent manner in three separate contexts. Finally, by testing a small portion of the available combinatorial mutant library it was shown that mutation of individual binding sites within of the CRM is not sufficient to show a significant change in the level of reporter gene expression

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Curvature-based sparse rule base generation for fuzzy rule interpolation

Fuzzy logic has been successfully widely utilised in many real-world applications. The most common application of fuzzy logic is the rule-based fuzzy inference system, which is composed of mainly two parts including an inference engine and a fuzzy rule base. Conventional fuzzy inference systems always require a rule base that fully covers the entire problem domain (i.e., a dense rule base). Fuzzy rule interpolation (FRI) makes inference possible with sparse rule bases which may not cover some parts of the problem domain (i.e., a sparse rule base). In addition to extending the applicability of fuzzy inference systems, fuzzy interpolation can also be used to reduce system complexity for over-complex fuzzy inference systems. There are typically two methods to generate fuzzy rule bases, i.e., the knowledge driven and data-driven approaches. Almost all of these approaches only target dense rule bases for conventional fuzzy inference systems. The knowledge-driven methods may be negatively affected by the limited availability of expert knowledge and expert knowledge may be subjective, whilst redundancy often exists in fuzzy rule-based models that are acquired from numerical data. Note that various rule base reduction approaches have been proposed, but they are all based on certain similarity measures and are likely to cause performance deterioration along with the size reduction. This project, for the first time, innovatively applies curvature values to distinguish important features and instances in a dataset, to support the construction of a neat and concise sparse rule base for fuzzy rule interpolation. In addition to working in a three-dimensional problem space, the work also extends the natural three-dimensional curvature calculation to problems with high dimensions, which greatly broadens the applicability of the proposed approach. As a result, the proposed approach alleviates the ‘curse of dimensionality’ and helps to reduce the computational cost for fuzzy inference systems. The proposed approach has been validated and evaluated by three real-world applications. The experimental results demonstrate that the proposed approach is able to generate sparse rule bases with less rules but resulting in better performance, which confirms the power of the proposed system. In addition to fuzzy rule interpolation, the proposed curvature-based approach can also be readily used as a general feature selection tool to work with other machine learning approaches, such as classifiers

A connectome and analysis of the adult Drosophila central brain

The neural circuits responsible for animal behavior remain largely unknown. We summarize new methods and present the circuitry of a large fraction of the brain of the fruit fly Drosophila melanogaster. Improved methods include new procedures to prepare, image, align, segment, find synapses in, and proofread such large data sets. We define cell types, refine computational compartments, and provide an exhaustive atlas of cell examples and types, many of them novel. We provide detailed circuits consisting of neurons and their chemical synapses for most of the central brain. We make the data public and simplify access, reducing the effort needed to answer circuit questions, and provide procedures linking the neurons defined by our analysis with genetic reagents. Biologically, we examine distributions of connection strengths, neural motifs on different scales, electrical consequences of compartmentalization, and evidence that maximizing packing density is an important criterion in the evolution of the fly’s brain

Modélisation des Réarrangements Vα-Jα du TRA/TRD chez la souris et chez l'homme

V(D)J recombination constitutes a somatic site specific DNA recombination, which originates lymphocyte antigen receptor diversity in jawed vertebrates. Concerning the T cell receptor α chains, V and J genes are used from inside the TRA locus toward distal genes during the successive rearrangements, with no allelic exclusion at the genomic level. Experimental quantifications of particular V-J associations were performed in mouse, giving the tendencies of thymic and peripheral combinatorial repertoires. A stochastic numerical model, based on successive opening windows progressing over the V and J regions during the rearrangement rounds, revealed new insights in the understanding of the dynamical rules governing V-J rearrangements and provided a simulated combinatorial repertoire with the entire V-J association frequencies. In the transition to human, thymic quantifications of certain V-J associations were performed, providing a first experimental wide-ranging sampling of the human TRA combinatorial repertoire. The modeling modeling step offered a clear understanding of the dynamical building of the human α repertoire and proposed predictions on repertoire combinatorial diversity richness. Finally, the precise progression of gene accessibility to rearrangements, according to non-constant opening speeds, together with a synchronized opening of the J regions between both alleles, were sufficient to fully explain both the experimental V-J frequencies currently available for the two species as well as the interallelic J usage.La recombinaison V(D)J constitue une recombinaison somatique et site-spécifique de l'ADN à l'origine de la diversité des récepteurs antigéniques des lymphocytes T chez les vertébrés mandibulés. Concernant la chaîne α des récepteurs T, les gènes V et J sont utilisés depuis l'intérieur du locus TRA vers les gènes distaux durant des réarrangements successifs et ce sans exclusion allélique. La quantification expérimentale de certaines associations V-J chez la souris a permis de définir les tendances des répertoires combinatoires thymiques et périphériques. Un modèle numérique stochastique, basé sur des fenêtrages d'ouverture successives progressant sur les régions V et J durant les cycles de réarrangements, a permis une meilleure compréhension des règles dynamiques gouvernant les réarrangements V-J et a apporté la connaissance d'un répertoire combinatoire simulé renseignant les fréquences de toutes les associations V-J. Lors de la transition à l'homme, la quantification des associations V-J a été réalisée au niveau du thymus, constituant un premier échantillonnage à large échelle du répertoire combinatoire TRA humain. L'étape de modélisation a offert une compréhension claire de la construction dynamique du répertoire α humain et a permis de proposer des prédictions sur la diversité du répertoire combinatoire. Finalement, la progression de l'accessibilité des gènes aux réarrangements selon des vitesses d'ouverture non-constantes associée à une ouverture synchronisée des régions J entre les deux allèles se sont révélées suffisantes pour expliquer les fréquences V-J expérimentales présentement disponibles pour les deux espèces ainsi que l'utilisation interallélique des gènes J

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition

Music in Evolution and Evolution in Music

Music in Evolution and Evolution in Music by Steven Jan is a comprehensive account of the relationships between evolutionary theory and music. Examining the ‘evolutionary algorithm’ that drives biological and musical-cultural evolution, the book provides a distinctive commentary on how musicality and music can shed light on our understanding of Darwin’s famous theory, and vice-versa. Comprised of seven chapters, with several musical examples, figures and definitions of terms, this original and accessible book is a valuable resource for anyone interested in the relationships between music and evolutionary thought. Jan guides the reader through key evolutionary ideas and the development of human musicality, before exploring cultural evolution, evolutionary ideas in musical scholarship, animal vocalisations, music generated through technology, and the nature of consciousness as an evolutionary phenomenon. A unique examination of how evolutionary thought intersects with music, Music in Evolution and Evolution in Music is essential to our understanding of how and why music arose in our species and why it is such a significant presence in our lives

Genome-wide analysis of transcriptional expression programs, regulatory networks and Cis-regulatory sequences in Saccharomyces cerevisiae

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Biology, 2005.Includes bibliographical references.Historically, knowledge of gene-specific transcription has been accumulated by the study of the individual genetic and physical interactions between transcriptional regulators and the genes they regulate, often requiring considerable time and effort. Microarray technology now enables investigation of gene expression at the level of the entire genome, allowing researchers access to rich datasets and promising new levels of depth in the understanding of transcriptional regulation. Our lab has made use of these technologies both to measure the levels of all mRNA transcripts within a population of cells, as well as to locate the regions within the genome that are bound by transcriptional regulators. Such studies not only allow for the functional annotation of both genes and regulators, but can also provide clues about the identity of the regulatory regions within DNA, the structure of global regulatory networks and the regulation of DNA-binding proteins. These and other insights are presented here based on our genome-wide studies of transcriptional regulation in the yeast Saccharomyces cerevisiae.by Christopher T. Harbison.Ph.D