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BCIT Citations Collection

Design of a dynamic model of genes with multiple autonomous regulatory modules by evolutionary computations
A new approach to design a dynamic model of genes with multiple autonomous regulatory modules by evolutionary computations is proposed. The approach is based on Genetic Algorithms (GA), with new crossover operators especially designed for these purposes. The new operators use local homology between parental strings to preserve building blocks found by the algorithm. The approach exploits the subbasin-portal architecture of the fitness functions suitable for this kind of evolutionary modeling. This architecture is significant for Royal Road class fitness functions. Two real-life Systems Biology problems with such fitness functions are implemented here: evolution of the bacterial promoter rrnP1 and of the enhancer of the Drosophila even-skipped gene. The effectiveness of the approach compared to standard GA is demonstrated on several benchmark and reallife tasks., Peer-reviewed article, Published.
Evolutionary design of gene networks
The co-evolution of species with their genomic parasites (transposons) is thought to be one of the primary ways of rewiring gene regulatory networks (GRNs). We develop a framework for conducting evolutionary computations (EC) using the transposon mechanism. We find that the selective pressure of transposons can speed evolutionary searches for solutions and lead to outgrowth of GRNs (through co-option of new genes to acquire insensitivity to the attacking transposons). We test the approach by finding GRNs which can solve a fundamental problem in developmental biology: how GRNs in early embryo development can robustly read maternal signaling gradients, despite continued attacks on the genome by transposons. We observed co-evolutionary oscillations in the abundance of particular GRNs and their transposons, reminiscent of predator-prey or host-parasite dynamics., Peer-reviewed article, Published.
Evolutionary techniques for image processing a large dataset of early drosophila gene expression
Understanding how genetic networks act in embryonic development requires a detailed and statistically significant dataset integrating diverse observational results. The fruit fly (Drosophila melanogaster) is used as a model organism for studying developmental genetics. In recent years, several laboratories have systematically gathered confocal microscopy images of patterns of activity (expression) for genes governing early Drosophila development. Due to both the high variability between fruit fly embryos and diverse sources of observational errors, some new nontrivial procedures for processing and integrating the raw observations are required. Here we describe processing techniques based on genetic algorithms and discuss their efficacy in decreasing observational errors and illuminating the natural variability in gene expression patterns. The specific developmental problem studied is anteroposterior specification of the body plan., Peer-reviewed article, Published.
Making the body plan
We quantify fluctuations in protein expression for three of the segmentation genes in the fruit fly, Drosophila melanogaster. These proteins are representative members of the first three levels of a signalling hierarchy which determines the segmented body plan: maternal (Bicoid protein); gap (Hunchback protein); and pair-rule (Even-skipped protein). We quantify both inter-embryo and inter-nucleus (within a single embryo) variability in expression, especially with respect to positional specification by concentration gradient reading. Errors are quantified both early and late in cleavage cycle 14, during which the protein patterns develop, to study the dynamics of error transmission. We find that Bicoid displays very large positional errors, while expression of the downstream genes, Hunchback and Even-skipped, displays far more precise positioning. This is evidence that the pattern formation of the downstream proteins is at least partially independent of maternal signal, i. e. evidence against simple concentration gradient reading. We also find that fractional errors in concentration increase during cleavage cycle 14., Peer-reviewed article, Published. Received 30 September 2002; Accepted 12 December 2002; Published 16 December 2002.
Modeling the evolution of gene regulatory networks for spatial patterning in embryo development
A central question in evolutionary biology concerns the transition between discrete numbers of units (e.g. vertebrate digits, arthropod segments). How do particular numbers of units, robust and characteristic for one species, evolve into another number for another species? Intermediate phases with a diversity of forms have long been theorized, but these leave little fossil or genomic data. We use evolutionary computations (EC) of a gene regulatory network (GRN) model to investigate how embryonic development is altered to create new forms. The trajectories are epochal and non-smooth, in accord with both the observed stability of species and the evolvability between forms., Peer-reviewed article, Published.
Predictive algorithm for Volt/VAR optimization of distribution networks using Neural Networks
Proceedings of IEEE Canadian Conference on Electrical and Computer Engineering (CCECE2014),May 2014, Toronto, Canada. Smart Grid functions such as Advanced Metering Infrastructure, Pervasive Control and Distribution Management Systems have brought numerous control and optimization opportunities for distribution networks through more accurate and reliable techniques. This paper presents a new predictive approach for Volt/VAr Optimization (VVO) of smart distribution systems using Neural Networks (NN) and Genetic Algorithm (GA). The proposed predictive algorithm is capable of predicting the load profile of target nodes a day ahead by employing the historical metrology data of Smart Meters, It can further perform a comprehensive VVO in order to minimize distribution network loss/operating costs and run Conservation Voltage Reduction (CVR) to conserve more energy. To test the merits of the proposed algorithm, British Columbia Institute of Technology north campus distribution grid is used as research case study., Conference paper, Published.
Retroviral genetic algorithms
Proceedings of the 2011 International Conference on Evolutionary Computation Theory and Applications. Classical understandings of biological evolution inspired creation of the entire order of Evolutionary Computation (EC) heuristic optimization techniques. In turn, the development of EC has shown how living organisms use biomolecular implementations of these techniques to solve particular problems in survival and adaptation. An example of such a natural Genetic Algorithm (GA) is the way in which a higher organism's adaptive immune system selects antibodies and competes against its complement, the development of antigen variability by pathogenic organisms. In our approach, we use operators that implement the reproduction and diversification of genetic material in a manner inspired by retroviral reproduction and a genetic-engineering technique known as DNA shuffling. We call this approach Retroviral Genetic Algorithms, or retroGA (Spirov and Holloway, 2010). Here, we extend retroGA to include: (1) the utilization of tags in strings; (2) the capability of the Reproduction-Crossover operator to read these tags and interpret them as instructions; and (3), as a consequence, to use more than one reproductive strategy. We validated the efficacy of the extended retroGA technique with benchmark tests on concatenated trap functions and compared these with Royal Road and Royal Staircase functions., Conference paper, Published.