A New Approach to Solve N-Queen Problem with Parallel Genetic Algorithm

Over the past few decades great efforts were made to solve uncertain hybrid optimization problems. The n-Queen problem is one of such problems that many solutions have been proposed for. The traditional methods to solve this problem are exponential in terms of runtime and are not acceptable in terms of space and memory complexity. In this study, parallel genetic algorithms are proposed to solve the n-Queen problem. Parallelizing island genetic algorithm and Cellular genetic algorithm was implemented and run. The results show that these algorithms have the ability to find related solutions to this problem. The algorithms are not only faster but also they lead to better performance even without the use of parallel hardware and just running on one core processor. Good comparisons were made between the proposed method and serial genetic algorithms in order to measure the performance of the proposed method. The experimental results show that the algorithm has high efficiency for large-size problems in comparison with genetic algorithms, and in some cases it can achieve super linear speedup. The proposed method in the present study can be easily developed to solve other optimization problems

The synergistic role of light and heat in liquid-based nanoparticle manipulation

Light and heat are synergistic tools used in the manipulation of nanoparticles and biomolecules. When optical effects dominate over thermal effects, the motion of nanoparticles can be controlled by optical forces. Here, we study the motion of 100 nm gold particles within a 1D optical potential, created by interfering counterpropagating beams. Tracking of particle trajectories revealed a large and asymmetric reduction in the nanoparticle diffusion constant in the presence of the traps, in agreement with theoretical predictions. When thermal effects dominate, laser light can induce local temperature gradients. Here, this was achieved by absorption of near-infrared (NIR) laser light in a Chromium microdisc. This resulted in thermophoretic separation of sodium azide ions, causing a local electric field that was used to manipulate 26 nm polystyrene beads. The nanoparticles were observed to follow the NIR heating spot, enabling light-controlled nanoparticle swarming. The induced 3D temperature profiles were characterised by time-correlated single-photon counting microscopy, with a temperature-sensitive dye. Through analysis of the particle velocities, the thermoelectric field strength, as well as the previously unknown Soret coefficients of azide ions were quantified. Transmission of laser beams through nanoparticle suspensions can lead to strong nonlinear lensing and soliton-like propagation effects. Literature has attributed these to redistribution of particles by optical gradient forces, and the effect is commonly described as an effective Kerr nonlinearity. To test this hypothesis, beam propagation experiments through a suspension of 40 nm plasmonic gold nanoparticles were carried out, and were found to be in agreement with previously reported results. To verify the nature of the effect, a new time-resolved z-scan technique was developed to measure the timescale and magnitude of the refractive index change. Surprisingly, the data demonstrates that the timescales can only be explained by thermal-absorption, -diffusion, and thermo-optic effects. As a result, the nonlinear effects are non-local and z-scan measurements will underestimate their magnitude

Stochastic analysis of nonlinear dynamics and feedback control for gene regulatory networks with applications to synthetic biology

The focus of the thesis is the investigation of the generalized repressilator model (repressing genes ordered in a ring structure). Using nonlinear bifurcation analysis stable and quasi-stable periodic orbits in this genetic network are characterized and a design for a switchable and controllable genetic oscillator is proposed. The oscillator operates around a quasi-stable periodic orbit using the classical engineering idea of read-out based control. Previous genetic oscillators have been designed around stable periodic orbits, however we explore the possibility of quasi-stable periodic orbit expecting better controllability. The ring topology of the generalized repressilator model has spatio-temporal symmetries that can be understood as propagating perturbations in discrete lattices. Network topology is a universal cross-discipline transferable concept and based on it analytical conditions for the emergence of stable and quasi-stable periodic orbits are derived. Also the length and distribution of quasi-stable oscillations are obtained. The findings suggest that long-lived transient dynamics due to feedback loops can dominate gene network dynamics. Taking the stochastic nature of gene expression into account a master equation for the generalized repressilator is derived. The stochasticity is shown to influence the onset of bifurcations and quality of oscillations. Internal noise is shown to have an overall stabilizing effect on the oscillating transients emerging from the quasi-stable periodic orbits. The insights from the read-out based control scheme for the genetic oscillator lead us to the idea to implement an algorithmic controller, which would direct any genetic circuit to a desired state. The algorithm operates model-free, i.e. in principle it is applicable to any genetic network and the input information is a data matrix of measured time series from the network dynamics. The application areas for readout-based control in genetic networks range from classical tissue engineering to stem cells specification, whenever a quantitatively and temporarily targeted intervention is required

DNAコンピューティングシステムの設計支援 : DNAツールボックスとその拡張

学位の種別:課程博士University of Tokyo(東京大学

Characterization of Nanoparticles Using Solid State Nanopores

Solid state nanopores are widely used in detection of highly charged biomolecules like DNA and proteins. In this study, we use a solid state nanopore based device to characterize spherical nanoparticles to estimate their size and electrical charge using the principle of resistive pulse technique. The principle of resistive pulse technique is the method of counting and sizing particles suspended in a fluid medium, which are electrophoretically driven through a channel and produce current blockage signals due to giving rise to a change in its initial current. This change in current is denoted as a current blockage or as a resistive pulse. The information from these current blockage signals in case of nanopore devices and spherical nanoparticles helps us to look at the properties of each individual nanoparticles such as size, electrical charge and electrophoretic mobility. In this thesis, two spherical nanoparticles of different sizes and different surface charge groups are used: Negatively charged 25 nm iron oxide nanoparticle with – COOH surface group and positively charged 53 nm polystyrene nanoparticle with – NH2 surface group. Nanopores used in these studies are about twice the nanoparticle size. These nanopores were fabricated by various fabrication techniques such as, Focused ion beam milling and ion beam sculpting method. The current blockage events produced by these two nanoparticles were measured as a function of applied voltage. The parameters extracted from the current blockage events, such as the current drop amplitudes and event duration are analyzed to estimate the size and electrical charge of the nanoparticles. Estimation of drift velocity of the nanoparticle and diffusion coefficient are also discussed. The estimated size is then compared to the nanoparticle size obtained from dynamic light scattering technique. Stable nanoparticles are widely used in biological and pharmacological studies and understanding the behavior of these nanoparticles in a nanopore environment would make a significant contribution to the studies at the nanoscale

Exploring new methodologies to identify disease-associated variants in African populations through the integration of patient genotype data and clinical phenotypes derived from routine health data: A case study for Type 2 Diabetes Mellitus in patients in the Western Cape Province, South Africa

Thesis Title Exploring new methodologies to identify disease-associated variants in African populations through the integration of patient genotype data and clinical phenotypes derived from routine health data: A case study for Type 2 Diabetes Mellitus patients in the Western Cape Province, South Africa. Abstract Introduction There is poor knowledge on the genetic drivers of disease in African populations and this is largely driven by the limited data for human genomes from sub-Saharan Africa. While the costs of generating human genomic data have gone down significantly, they are still a barrier to generating large scale African genomic data. This project is therefore a proof-of-concept pilot study that demonstrates the implementation of a cost-effective, scalable genotyped virtual cohort that can address population level genomic questions. Methods We optimised a tiered informed consent process that is suitable for the cohort study design and adapted it to conducting human genomic research in the African context. We used an existing dataset to explore statistical methods for modelling longitudinal routine health data into a standardised phenotype for genome wide association studies (GWAS). We then conducted a feasibility study and piloted the tiered informed consent process, DNA collection by buccal swab and DNA extraction from buccal swabs and peripheral blood samples. DNA samples were genotyped for approximately 2.2 million variants on the Infinium™ H3Africa Consortium Array V2. Genotyping quality control (QC) was done in Plink 1.9 and genome wide imputation on the Sanger Imputation Service. We demonstrated successful variant calling and provide aggregate statistics for known aetiological variants for type 2 diabetes and severe COVID-19 as well as demonstrating the feasibility of running nested case-control GWAS with these data. Results We demonstrate the use of routine health data to provide complex phenotypes to link to genotype data for both non-communicable diseases (diabetes) and infectious diseases (Tuberculosis, HIV and COVID-19). 459 participants consented to providing a DNA sample and access to their routine health data and were included in the feasibility study. A total of 343 DNA samples and 1782023 genotyped variants passed quality control and were available for further analysis. While most of the cohort population clustered with the 1000 genomes African population, principal component analysis showed extensive population admixture. For the COVID-19 analysis, we identified 63 cases of severe COVID-19 and 280 controls, and for the type 2 diabetes analysis we identified 93 cases and 250 controls using the routine health data of participants in the cohort. While the sample sizes were insufficient for a GWAS we were able to evaluate known type 2 diabetes mellitus and COVID-19 variants in the study population. Conclusion We have described how we conceptualised and implemented a genotyped virtual population cohort in a resource constrained environment, and we are confident that this design and implementation are appropriate to scale up the cohort to a size where novel health discoveries can be made through nested case-control studies. In the interim we demonstrate the analysis and validation of aetiological variants identified in other studies and populations