Chatchawit Aporntewan

A hardware implementation of the Compact Genetic Algorithm

We propose a hardware implementation of the Compact Genetic Algorithm (GA). The design is realized using the Verilog hardware description language (HDL) and then fabricated on an FPGA. Our design, though simple, runs about 1000 times faster than the software executing on a workstation. An alternative hardware for linkage learning is also proposed in order to enhance the capability of the Compact GA to solve highly deceptive problems.

Hypomethylation of Intragenic LINE-1 Represses Transcription in Cancer Cells through AGO2

In human cancers, the methylation of long interspersed nuclear element -1 (LINE-1 or L1) retrotransposons is reduced. This occurs within the context of genome wide hypomethylation, and although it is common, its role is poorly understood. L1s are widely distributed both inside and outside of genes, intragenic and intergenic, respectively. Interestingly, the insertion of active full-length L1 sequences into host gene introns disrupts gene expression. Here, we evaluated if intragenic L1 hypomethylation influences their host gene expression in cancer. First, we extracted data from L1base (http://l1base.molgen.mpg.de), a database containing putatively active L1 insertions, and compared intragenic and intergenic L1 characters. We found that intragenic L1 sequences have been conserved across evolutionary time with respect to transcriptional activity and CpG dinucleotide sites for mammalian DNA methylation. Then, we compared regulated mRNA levels of cells from two different experiments available from Gene Expression Omnibus (GEO), a database repository of high throughput gene expression data, (http://www.ncbi.nlm.nih.gov/geo) by chi-square. The odds ratio of down-regulated genes between demethylated normal bronchial epithelium and lung cancer was high (p<1E−27; OR = 3.14; 95% CI = 2.54–3.88), suggesting cancer genome wide hypomethylation down-regulating gene expression. Comprehensive analysis between L1 locations and gene expression showed that expression of genes containing L1s had a significantly higher likelihood to be repressed in cancer and hypomethylated normal cells. In contrast, many mRNAs derived from genes containing L1s are elevated in Argonaute 2 (AGO2 or EIF2C2)-depleted cells. Hypomethylated L1s increase L1 mRNA levels. Finally, we found that AGO2 targets intronic L1 pre-mRNA complexes and represses cancer genes. These findings represent one of the mechanisms of cancer genome wide hypomethylation altering gene expression. Hypomethylated intragenic L1s are a nuclear siRNA mediated cis-regulatory element that can repress genes. This epigenetic regulation of retrotransposons likely influences many aspects of genomic biology.

Synthesis of Synchronous Sequential Logic Circuits from Partial Input/Output Sequences

This work takes a different approach to synthesize a synchronous sequential logic circuit. The input of the synthesizer is a partial input/output sequence. This type of specification is not suitable for conventional synthesis methods. Genetic Algorithm (GA) was applied to synthesize the desired circuit that performs according to the input/output sequences. GA searches for circuits that represent the desired state transition function. Additional combination circuits that map states to the corresponding outputs are synthesized by conventional methods. The target of our synthesis is a type of registered Programmable Array Logic which is commercially available as GAL. We are able to synthesize various types of synchronous sequential logic circuit such as counter, serial adder, frequency divider, modulo-5 detector and parity checker.

Improving correctness of finite-state machine synthesis from multiple partial input/output sequences

Our previous work focused on the synthesis of sequential circuits based on a partial input/output sequence. As the behavioural description of the target circuit is not known the correctness of the result can not be verified. This paper proposes a method which increases the correctness percentage of the finite-state machine (FSM) synthesis using multiple partial input/output sequences. The synthesizer is based on genetic algorithm. The experimental results show that the correctness percentage can be increased to 100% by increasing the number of input/output sequences.

Building-block Identification by Simultaneity Matrix

AbstractThis paper presents a study of building blocks (BBs) in the context of genetic algorithms (GAs). In GAs literature, the BBs are common structures of high-quality solutions. The aim is to identify and maintain the BBs while performing solution recombination. To identify the BBs, we construct an

Chi-Square matrix: an approach for building-block identification

\ell \times \ell

A quantitative approach for validating the building-block hypothesis

simultaneity matrix according to a set of