Graphics Processing Unit–Enhanced Genetic Algorithms for Solving the Temporal Dynamics of Gene Regulatory Networks

Understanding the regulation of gene expression is one of the key problems in current biology. A promising method for that purpose is the determination of the temporal dynamics between known initial and ending network states, by using simple acting rules. The huge amount of rule combinations and the nonlinear inherent nature of the problem make genetic algorithms an excellent candidate for finding optimal solutions. As this is a computationally intensive problem that needs long runtimes in conventional architectures for realistic network sizes, it is fundamental to accelerate this task. In this article, we study how to develop efficient parallel implementations of this method for the fine-grained parallel architecture of graphics processing units (GPUs) using the compute unified device architecture (CUDA) platform. An exhaustive and methodical study of various parallel genetic algorithm schemes—master-slave, island, cellular, and hybrid models, and various individual selection methods (roulette, elitist)—is carried out for this problem. Several procedures that optimize the use of the GPU’s resources are presented. We conclude that the implementation that produces better results (both from the performance and the genetic algorithm fitness perspectives) is simulating a few thousands of individuals grouped in a few islands using elitist selection. This model comprises 2 mighty factors for discovering the best solutions: finding good individuals in a short number of generations, and introducing genetic diversity via a relatively frequent and numerous migration. As a result, we have even found the optimal solution for the analyzed gene regulatory network (GRN). In addition, a comparative study of the performance obtained by the different parallel implementations on GPU versus a sequential application on CPU is carried out. In our tests, a multifold speedup was obtained for our optimized parallel implementation of the method on medium class GPU over an equivalent sequential single-core implementation running on a recent Intel i7 CPU. This work can provide useful guidance to researchers in biology, medicine, or bioinformatics in how to take advantage of the parallelization on massively parallel devices and GPUs to apply novel metaheuristic algorithms powered by nature for real-world applications (like the method to solve the temporal dynamics of GRNs)

Green Parallel Metaheuristics: Design, Implementation, and Evaluation

Fecha de lectura de Tesis Doctoral 14 mayo 2020Green parallel metaheuristics (GPM) is a new concept we want to introduce in this thesis. It is an idea inspired by two facts: (i) parallel metaheuristics could help as unique tools to solve optimization problems in energy savings applications and sustainability, and (ii) these algorithms themselves run on multiprocessors, clusters, and grids of computers and then consume energy, so they need an energy analysis study for their different implementations over multiprocessors. The context for this thesis is to make a modern and competitive effort to extend the capability of present intelligent search optimization techniques. Analyzing the different sequential and parallel metaheuristics considering its energy consumption requires a deep investigation of the numerical performance, the execution time for efficient future designing to these algorithms. We present a study of the speed-up of the different parallel implementations over a different number of computing units. Moreover, we analyze and compare the energy consumption and numerical performance of the sequential/parallel algorithms and their components: a jump in the efficiency of the algorithms that would probably have a wide impact on the domains involved.El Instituto Egipcio en Madrid, dependiente del Gobierno de Egipto

A simulation approach for multilocus aelection-migration models

Diese Diplomarbeit stellt eine Implementierung eines Software-Packets zur Simulation von Migrations-Selektionsmodellen vor. Die deterministische, diskrete Simulation iteriert die zugrunde liegende Differenzengleichung, um die Gleichgewichte des dynamischen Systems zu finden. Als Anwendungsfall wird die Gleichgewichtsstruktur einer Population unter quadratisch-stabilisierender Selektion untersucht. Zuerst wird das zugrunde liegende mathematische Modell eingeführt und die getroffenen biologischen Annahmen erklärt. Das untersuchte dynamische System wird definiert, Fitnessfunktionen, Rekombination und Migrationsmodelle werden abgehandelt. Weiters definieren wir wichtige Größen, die erlauben die genetische Zusammensetzung und die Differenzierung in Gleichgewichten zu messen. Danach werden relevante Publikationen besprochen, die Simulationen vorwärts in der Zeit und quadratisch stabilisierende Selektion betreffen. Weiters wird der Grenzfall starker Migration betrachtet. Dem folgt eine detailierte Beschreibung der Implementierung. Dies umfasst das Objekt-Modell, die Datenbankarchitektur und eine Diskussion algorithmischer Belange. Schließlich werden die Resultate duch Anwendung der vorgestellten Simulation auf den Fall quadratisch stabilisierender Selektion vorgestellt. Zuerst wird der Fall einer panmiktischen Population mit zwei Allelen auf zwei Loci untersucht, wobei das Optimum der Fitnessfunktion beliebig ist. Anschließend werden zwei Deme mit symmetrisch verschobenen Optima behandelt, um Migration anhand des Deakin-Modells zu untersuchen.In this diploma thesis, the implementation of a software package is presented, which facilitates the simulation and analysis of multilocus migration-selection models. The deterministic, discrete simulation iterates the underlying difference equation to find the equilibria of the dynamical system. As an application, the equilibrium structure of a population under quadratic stabilizing selection is investigated. First, we state the biological assumptions and introduce the mathematical model. We define the investigated dynamical system and introduce fitness functions, recombination, and migration models. Furthermore, we define important quantities to measure properties of the genetic composition and of differentiation at equilibrium. Then, we review related work concerning forward-time simulations and quadratic stabilizing selection. Moreover, we discuss the limiting case of strong migration. This is followed by a discussion of the implementation of the developed software. This comprises the object model, the database architecture, and a discussion of algorithmic issues. Finally, the results obtained by the application of the program to the case of quadratic stabilizing selection are presented. First, the case of a diallelic two-locus panmictic population is investigated, allowing for arbitrary optimum position. Then, two demes are considered, displacing the optima symmetrically within the demes, and assuming the Deakin migration model

Never Too Old To Learn: On-line Evolution of Controllers in Swarm- and Modular Robotics

Eiben, A.E. [Promotor

An Adaptive Modular Redundancy Technique to Self-regulate Availability, Area, and Energy Consumption in Mission-critical Applications

As reconfigurable devices\u27 capacities and the complexity of applications that use them increase, the need for self-reliance of deployed systems becomes increasingly prominent. A Sustainable Modular Adaptive Redundancy Technique (SMART) composed of a dual-layered organic system is proposed, analyzed, implemented, and experimentally evaluated. SMART relies upon a variety of self-regulating properties to control availability, energy consumption, and area used, in dynamically-changing environments that require high degree of adaptation. The hardware layer is implemented on a Xilinx Virtex-4 Field Programmable Gate Array (FPGA) to provide self-repair using a novel approach called a Reconfigurable Adaptive Redundancy System (RARS). The software layer supervises the organic activities within the FPGA and extends the self-healing capabilities through application-independent, intrinsic, evolutionary repair techniques to leverage the benefits of dynamic Partial Reconfiguration (PR). A SMART prototype is evaluated using a Sobel edge detection application. This prototype is shown to provide sustainability for stressful occurrences of transient and permanent fault injection procedures while still reducing energy consumption and area requirements. An Organic Genetic Algorithm (OGA) technique is shown capable of consistently repairing hard faults while maintaining correct edge detector outputs, by exploiting spatial redundancy in the reconfigurable hardware. A Monte Carlo driven Continuous Markov Time Chains (CTMC) simulation is conducted to compare SMART\u27s availability to industry-standard Triple Modular Technique (TMR) techniques. Based on nine use cases, parameterized with realistic fault and repair rates acquired from publically available sources, the results indicate that availability is significantly enhanced by the adoption of fast repair techniques targeting aging-related hard-faults. Under harsh environments, SMART is shown to improve system availability from 36.02% with lengthy repair techniques to 98.84% with fast ones. This value increases to five nines (99.9998%) under relatively more favorable conditions. Lastly, SMART is compared to twenty eight standard TMR benchmarks that are generated by the widely-accepted BL-TMR tools. Results show that in seven out of nine use cases, SMART is the recommended technique, with power savings ranging from 22% to 29%, and area savings ranging from 17% to 24%, while still maintaining the same level of availability

Phylogeography and diversification of Taiwanese bats

PhDGene flow is a central evolutionary force that largely determines the level of differentiation among populations of organisms and thus their potential for divergence from each other. Identifying key factors that influence gene flow among populations or closely related taxa can thus provide valuable insights into how new species arise and are maintained. I undertook a comparative study of the factors that have shaped range-wide intraspecific differentiation in four related and broadly co-distributed Taiwanese bat species of the genera Murina and Kerivoula. Bats were sampled from sites across Taiwan and sequenced at two mitochondrial genes as well as genotyped at newly developed and/or existing multi-locus microsatellite markers. To improve phylogeographic inference of existing patterns of population genetic structure, I undertook spatial distribution modeling of the focal species at both the present time and at the Last Glacial Maximum. Genetic data were analysed using traditional and new methods, including Bayesian clustering, coalescent-based estimation of gene flow, and haplotype network reconstruction. My findings revealed contrasting signatures of population subdivision and demographic expansion that appear in part to reflect differences in the altitudinal ranges of the focal taxa. Mitochondrial analyses also revealed a putative sister relationship between two of the Taiwanese endemic taxa - M. gracilis and M. recondita, which - given the fact both are restricted to Taiwan - presents an unusual case of potential non-allopatric divergence. To dissect this divergence process in more detail, I used 454-Pyrosequencing to obtain ten nuclear loci sequences of these two taxa, and a third taxon from mainland Asia, M. eleryi. Based on these loci, Bayesian isolation-migration models provided no strong evidence of post-split gene flow and, therefore, did not support speciation within Taiwan. Instead, the divergence process reconstructed from ncDNA loci was found to be incompatible with the mtDNA tree, with M. recondita showing a sister relationship with M. eleryi. This conflict is best explained by the ancient introgression of mtDNA between the two insular species following their colonization of Taiwan at different times.Overseas Research Students Awards Scheme of the UK and the Taiwanese Ministry of Education, London Central Research Fund and the National Science Council of Taiwa

Unravelling the mystery of migratory behaviour in the Bogong moth Agrotis infusa using genomics and novel automated monitoring techniques

An exceptionally impressive example of animal navigation is presented by the Bogong moth Agrotis infusa, that migrates over 1000 km from widely distributed winter breeding grounds to a relatively confined summer range in the Australian Alps, consistently arriving to the same sites as its predecessors, despite never having an opportunity to learn the migratory route, or indeed, the location of its destination. The Bogong moth then waits out the summer in a dormant state known as aestivation, lining the walls of cool cracks and crevices in high altitude granite outcrops, where it forms massive assemblages with an estimated 17000 moths per square metre. Recent and ongoing investigations into the sensory and neurological capabilities of the Bogong moth have revealed that it possesses a "compass sense" that relies on geomagnetic and stellar information. However, since the migratory direction of the Bogong moth varies across its breeding range, a compass is not sufficient on its own for the moth's navigation. How, for instance, does a Bogong moth know - given its starting location - in which direction to migrate? The objective of this thesis is to understand the basis of the Bogong moth migratory direction. Even though this thesis opens as many questions as it answers, significant progress towards achieving this objective is presented (in two parts) herein, primarily through development of the scientific infrastructure for studying Bogong moth biology more generally. Part I introduces a new method for quantitatively measuring Bogong moth activity and abundance using automated camera-based detection, which is then used to model the influence of abiotic factors on Bogong moth behaviour, and to measure the arrival, departure, and population dynamics of the moths in their summer range. In addition to its utility in addressing ethological questions, this new method enables quantitative long-term monitoring of the Bogong moth population, which may prove invaluable for conservation efforts (the Bogong moth has recently been assessed as endangered for the IUCN Red List). In part II, the annotated sequence of the Bogong moth genome is presented, opening the door to high-throughput molecular research on the moth. Extensive differential gene expression in the sensory and brain tissue of migrating and aestivating moths is observed, along with evidence of epigenomic modification. Finally, the results of re-sequencing the genomes of 77 Bogong moths collected from across their breeding and summer ranges are presented, which show that the Bogong moth population is panmictic, and harbours a vast quantity of rare genetic variants. Interestingly, a small number of variants are highly correlated with migratory direction, indicating promising avenues for further research into the genetic basis of migratory direction