Genetic algorithms with applications in wireless communications

Genetic algorithms (GAs) are known to locate the global optimal solution provided sufficient population and/or generation is used. Practically, a near-optimal satisfactory result can be found by Gas with a limited number of generations. In wireless communications, the exhaustive searching approach is widely applied to many techniques, such as maximum likelihood decoding (MLD) and distance spectrum (DS) techniques. The complexity of the exhaustive searching approach in the MLD or the DS technique is exponential in the number of transmit antennas and the size of the signal constellation for the multiple-input multiple-output (MIMO) communication systems. If a large number of antennas and a large size of signal constellations, e.g. PSK and QAM, are employed in the MIMO systems, the exhaustive searching approach becomes impractical and time consuming. In this paper, the GAs are applied to the MLD and DS techniques to provide a near-optimal performance with a reduced computational complexity for the MIMO systems. Two different GA-based efficient searching approaches are proposed for the MLD and DS techniques, respectively. The first proposed approach is based on a GA with sharing function method, which is employed to locate the multiple solutions of the distance spectrum for the Space-time Trellis Coded Orthogonal Frequency Division Multiplexing (STTC-OFDM) systems. The second approach is the GA-based MLD that attempts to find the closest point to the transmitted signal. The proposed approach can return a satisfactory result with a good initial signal vector provided to the GA. Through simulation results, it is shown that the proposed GA-based efficient searching approaches can achieve near-optimal performance, but with a lower searching complexity comparing with the original MLD and DS techniques for the MIMO systems.

A genetic estimation algorithm for parameters of stochastic ordinary differential equations

A generic method for the estimation of parameters for Stochastic Ordinary Differential Equations (SODEs) is introduced and developed. This algorithm, called the GePERs method, utilises a genetic optimisation algorithm to minimise a stochastic objective function based on the Kolmogorov-Smirnov statistic. Numerical simulations are utilised to form the KS statistic. Further, the examination of some of the factors that improve the precision of the estimates is conducted. This method is used to estimate parameters of diffusion equations and jump-diffusion equations. It is also applied to the problem of model selection for the Queensland electricity market. (C) 2003 Elsevier B.V. All rights reserved.

Roles of transmembrane domain 2 and the first intracellular loop in human noradrenaline transporter function: pharmacological and SCAM analysis

The aim was to investigate the roles of transmembrane domain 2 and the adjacent region of the first intracellular loop in determining human noradrenaline transporter (hNET) function by pharmacological and substituted-cysteine accessibility method (SCAM) analyses. It was first necessary to establish a suitable background NET for SCAM. Alanine mutants of endogenous hNET cysteines, hC86A, hC131A and hC339A, were examined and showed no marked effects on expression or function. hNET and the mutants were also resistant to methanethiosulfonate (MTS), ethylammonium (MTSEA) and MTStrimethylammonium (MTSET). Hence, wild-type hNET is an appropriate background for production of cysteine mutants for SCAM. Pharmacological investigation showed that all mutants except hT99C and hL109C showed reduced cell-surface expression, while all except hM107C showed a reduction in functional activity. The mutations did not markedly affect the apparent affinities of substrates, but apparent affinities of cocaine were decreased 7-fold for hP97C and 10-fold for hF101C and increased 12-fold for hY98C. [H-3]Nisoxetine binding affinities were decreased 13-fold for hP97C and 5-fold for hF101C. SCAM analysis revealed that only hL102C was sensitive to 1.25 mM MTSEA, and this sensitivity was protected by noradrenaline, nisoxetine and cocaine. The results suggest that this region of hNET is important for interactions with antidepressants and cocaine, but it is probably not involved in substrate translocation mechanisms.

Genotoxicity of inorganic lead salts and disturbance of microtubule function

Lead compounds are known genotoxicants, principally affecting the integrity of chromosomes. Lead chloride and lead acetate induced concentration-dependent increases in micronucleus frequency in V79 cells, starting at 1.1 μ M lead chloride and 0.05 μ M lead acetate. The difference between the lead salts, which was expected based on their relative abilities to form complex acetato-cations, was confirmed in an independent experiment. CREST analyses of the micronuclei verified that lead chloride and acetate were predominantly aneugenic (CREST-positive response), which was consistent with the morphology of the micronuclei (larger micronuclei, compared with micronuclei induced by a clastogenic mechanism). The effects of high concentrations of lead salts on the microtubule network of V79 cells were also examined using immunofluorescence staining. The dose effects of these responses were consistent with the cytotoxicity of lead(II), as visualized in the neutral-red uptake assay. In a cell-free system, 20-60 μ M lead salts inhibited tubulin assembly dose-dependently. The no-observed-effect concentration of lead(II) in this assay was 10 μ M. This inhibitory effect was interpreted as a shift of the assembly/disassembly steady-state toward disassembly, e.g., by reducing the concentration of assembly-competent tubulin dimers. The effects of lead salts on microtubule-associated motor-protein functions were studied using a kinesin-gliding assay that mimics intracellular transport processes in vitro by quantifying the movement of paclitaxel-stabilized microtubules across a kinesin-coated glass surface. There was a dose-dependent effect of lead nitrate on microtubule motility. Lead nitrate affected the gliding velocities of microtubules starting at concentrations above 10 μ M and reached half-maximal inhibition of motility at about 50 μ M. The processes reported here point to relevant interactions of lead with tubulin and kinesin at low dose levels. Environ. Mal. Mutagen. 45:346-353, 2005. © 2005 Wiley-Liss, Inc.

A framework for the study of genetic variation in migratory behaviour

Evolutionary change results from selection acting on genetic variation. For migration to be successful, many different aspects of an animal's physiology and behaviour need to function in a co-coordinated way. Changes in one migratory trait are therefore likely to be accompanied by changes in other migratory and life-history traits. At present, we have some knowledge of the pressures that operate at the various stages of migration, but we know very little about the extent of genetic variation in various aspects of the migratory syndrome. As a consequence, our ability to predict which species is capable of what kind of evolutionary change, and at which rate, is limited. Here, we review how our evolutionary understanding of migration may benefit from taking a quantitative-genetic approach and present a framework for studying the causes of phenotypic variation. We review past research, that has mainly studied single migratory traits in captive birds, and discuss how this work could be extended to study genetic variation in the wild and to account for genetic correlations and correlated selection. In the future, reaction-norm approaches may become very important, as they allow the study of genetic and environmental effects on phenotypic expression within a single framework, as well as of their interactions. We advocate making more use of repeated measurements on single individuals to study the causes of among-individual variation in the wild, as they are easier to obtain than data on relatives and can provide valuable information for identifying and selecting traits. This approach will be particularly informative if it involves systematic testing of individuals under different environmental conditions. We propose extending this research agenda by using optimality models to predict levels of variation and covariation among traits and constraints. This may help us to select traits in which we might expect genetic variation, and to identify the most informative environmental axes. We also recommend an expansion of the passerine model, as this model does not apply to birds, like geese, where cultural transmission of spatio-temporal information is an important determinant of migration patterns and their variation.

Effect of disrupted SOX18 transcription factor function on tumor growth, vascularization, and endothelial development

Background. The growth of solid tumors depends on establishing blood supply; thus, inhibiting tumor angiogenesis has been a long-term goal in cancer therapy. The SOX18 transcription factor is a key regulator of murine and human blood vessel formation. Methods: We established allograft melanoma tumors in wild-type mice, Sox18-null mice, and mice expressing a dominant-negative form of Sox18 (Sox18RaOp) (n = 4 per group) and measured tumor growth and microvessel density by immunohistochemical analysis with antibodies to the endothelial marker CD31 and the pericyte marker NG2. We also assessed the affects of disrupted SOX18 function on MCF-7 human breast cancer and human umbilical vein endothelial cell (HUVEC) proliferation by measuring BrdU incorporation and by MTS assay, cell migration using Boyden chamber assay, and capillary tube formation in vitro. All statistical tests were two-sided. Results: Allograft tumors in Sox18-null and Sox18RaOp mice grew more slowly than those in wild-type mice (tumor volume at day 14, Sox18 null, mean = 486 mm(3), 95% confidence interval [CI] = 345 mm(3) to 627 mm(3), p = .004; Sox18RaOp, mean = 233 mm(3), 95% CI = 73 mm(3) to 119 mm(3), p < .001; versus wild-type, mean = 817 mm(3), 95% CI = 643 mm(3) to 1001 mm(3)) and had fewer CD31- and NG2-expressing vessels. Expression of dominant-negative Sox18 reduced the proliferation of MCF-7 cells (BrdU incorporation: MCF-7(Ra) = 20%, 95% CI = 15% to 25% versus MCF-7 = 41%, 95% CI = 35% to 45%; P = .013) and HUVECs (optical density at 490 nm, empty vector, mean = 0.46 versus SOX18 mean = 0.29; difference = 0.17, 95% CI = 0.14 to 0.19; P = .001) compared with control subjects. Overexpression of wild-type SOX18 promoted capillary tube formation of HUVECs in vitro, whereas expression of dominant-negative SOX18 impaired tube formation of HUVECs and the migration of MCF-7 cells via the disruption of the actin cytoskeleton. Conclusions: SOX18 is a potential target for antiangiogenic therapy of human cancers.

Genetic manipulation of blood group carbohydrates alters development and pathfinding of primary sensory axons of the olfactory systems

Primary sensory neurons in the vertebrate olfactory systems are characterised by the differential expression of distinct cell surface carbohydrates. We show here that the histo-blood group H carbohydrate is expressed by primary sensory neurons in both the main and accessory olfactory systems while the blood group A carbohydrate is expressed by a subset of vomeronasal neurons in the developing accessory olfactory system. We have used both loss-of-function and gain-of-function approaches to manipulate expression of these carbohydrates in the olfactory system. In null mutant mice lacking the alpha(1,2)fucosyltransferase FUT1, the absence of blood group H carbohydrate resulted in the delayed maturation of the glomerular layer of the main olfactory bulb. In addition, ubiquitous expression of blood group A on olfactory axons in gain-of-function transgenic mice caused mis-routing of axons in the glomerular layer of the main olfactory bulb and led to exuberant growth of vomeronasal axons in the accessory olfactory bulb. These results provide in vivo evidence for a role of specific cell surface carbohydrates during development of the olfactory nerve pathways. (c) 2006 Elsevier Inc. All rights reserved.

Stochastic delay differential equations for genetic regulatory networks

Time delay is an important aspect in the modelling of genetic regulation due to slow biochemical reactions such as gene transcription and translation, and protein diffusion between the cytosol and nucleus. In this paper we introduce a general mathematical formalism via stochastic delay differential equations for describing time delays in genetic regulatory networks. Based on recent developments with the delay stochastic simulation algorithm, the delay chemical masterequation and the delay reaction rate equation are developed for describing biological reactions with time delay, which leads to stochastic delay differential equations derived from the Langevin approach. Two simple genetic regulatory networks are used to study the impact of' intrinsic noise on the system dynamics where there are delays. (c) 2006 Elsevier B.V. All rights reserved.

Evolving genetic regulatory networks using an artificial genome

Boolean models of genetic regulatory networks (GRNs) have been shown to exhibit many of the characteristic dynamics of real GRNs, with gene expression patterns settling to point attractors or limit cycles, or displaying chaotic behaviour, depending upon the connectivity of the network and the relative proportions of excitatory and inhibitory interactions. This range of behaviours is only apparent, however, when the nodes of the GRN are updated synchronously, a biologically implausible state of affairs. In this paper we demonstrate that evolution can produce GRNs with interesting dynamics under an asynchronous update scheme. We use an Artificial Genome to generate networks which exhibit limit cycle dynamics when updated synchronously, but collapse to a point attractor when updated asynchronously. Using a hill climbing algorithm the networks are then evolved using a fitness function which rewards patterns of gene expression which revisit as many previously seen states as possible. The final networks exhibit “fuzzy limit cycle” dynamics when updated asynchronously.

Multilevel modelling for inference of genetic regulatory networks

Time-course experiments with microarrays are often used to study dynamic biological systems and genetic regulatory networks (GRNs) that model how genes influence each other in cell-level development of organisms. The inference for GRNs provides important insights into the fundamental biological processes such as growth and is useful in disease diagnosis and genomic drug design. Due to the experimental design, multilevel data hierarchies are often present in time-course gene expression data. Most existing methods, however, ignore the dependency of the expression measurements over time and the correlation among gene expression profiles. Such independence assumptions violate regulatory interactions and can result in overlooking certain important subject effects and lead to spurious inference for regulatory networks or mechanisms. In this paper, a multilevel mixed-effects model is adopted to incorporate data hierarchies in the analysis of time-course data, where temporal and subject effects are both assumed to be random. The method starts with the clustering of genes by fitting the mixture model within the multilevel random-effects model framework using the expectation-maximization (EM) algorithm. The network of regulatory interactions is then determined by searching for regulatory control elements (activators and inhibitors) shared by the clusters of co-expressed genes, based on a time-lagged correlation coefficients measurement. The method is applied to two real time-course datasets from the budding yeast (Saccharomyces cerevisiae) genome. It is shown that the proposed method provides clusters of cell-cycle regulated genes that are supported by existing gene function annotations, and hence enables inference on regulatory interactions for the genetic network.

Genetic algorithm based distance spectrum technique for performance union bound of space-time trellis coded OFDM

This paper derives the performance union bound of space-time trellis codes in orthogonal frequency division multiplexing system (STTC-OFDM) over quasi-static frequency selective fading channels based on the distance spectrum technique. The distance spectrum is the enumeration of the codeword difference measures and their multiplicities by exhausted searching through all the possible error event paths. Exhaustive search approach can be used for low memory order STTC with small frame size. However with moderate memory order STTC and moderate frame size the computational cost of exhaustive search increases exponentially, and may become impractical for high memory order STTCs. This requires advanced computational techniques such as Genetic Algorithms (GAS). In this paper, a GA with sharing function method is used to locate the multiple solutions of the distance spectrum for high memory order STTCs. Simulation evaluates the performance union bound and the complexity comparison of non-GA aided and GA aided distance spectrum techniques. It shows that the union bound give a close performance measure at high signal-to-noise ratio (SNR). It also shows that GA sharing function method based distance spectrum technique requires much less computational time as compared with exhaustive search approach but with satisfactory accuracy.

The dynamics of a genetic algorithm under stabilizing selection

A formalism recently introduced by Prugel-Bennett and Shapiro uses the methods of statistical mechanics to model the dynamics of genetic algorithms. To be of more general interest than the test cases they consider. In this paper, the technique is applied to the subset sum problem, which is a combinatorial optimization problem with a strongly non-linear energy (fitness) function and many local minima under single spin flip dynamics. It is a problem which exhibits an interesting dynamics, reminiscent of stabilizing selection in population biology. The dynamics are solved under certain simplifying assumptions and are reduced to a set of difference equations for a small number of relevant quantities. The quantities used are the population's cumulants, which describe its shape, and the mean correlation within the population, which measures the microscopic similarity of population members. Including the mean correlation allows a better description of the population than the cumulants alone would provide and represents a new and important extension of the technique. The formalism includes finite population effects and describes problems of realistic size. The theory is shown to agree closely to simulations of a real genetic algorithm and the mean best energy is accurately predicted.