Inhibition of microRNA-383 promotes apoptosis of human colon cancer cells by upregulation of caspase-2 gene expression

Purpose: To investigate microRNA-383 (miR-383) as a therapeutic target for the management of colon cancer.Methods: Total RNA was isolated using RNeasy RNA isolation kit according to the manufacturer’s instructions. cDNA was synthesized using RevertAid cDNA synthesis kit. Expression analysis was carried out by quantitative real-time polymerase chain reaction (RT-PCR). Cell proliferation was examined using CellTiter 96 AQueous One Solution Cell Proliferation Assay system, while apoptosis was detected by 4',6-diamidino-2-phenylindole (DAPI) and annexin V/PI double staining followed by flow cytometry. The miR-383 target was delimited using TargetScan software. Protein expression analysis was carried out by western blotting.Results: The results indicate that miR-383 was highly expressed in colon cancer cells. Down-regulation of miR-383 inhibited cancer cell proliferation, and promoted apoptosis and cell cycle arrest. Furthermore, in silico analysis revealed caspase-2 gene to be the downstream target of miR-383, a finding that was further confirmed by western blotting.Conclusion: The results reveal that miR-383 may be an important target to tackle the increasing incidence of colon cancer. Thus, drugs that target miR-383 and inhibit its expression can potentially be developed for the treatment of colon cancer.Keywords: MicroRNA, Colon cancer, Cell proliferation, Apoptosis, Protein expressio

Вихретоковый анизотропный термоэлектрический первичный преобразователь лучистого потока

Представлена оригинальная конструкция первичного преобразователя лучистого потока, который может служить основой для создания приемника неселективного излучения с повышенной чувствительностью

Intelligence in Bioinformatics and Computational Biology (CIBCB 2007) Inference of Gene Regulatory Networks using S-System: A Unified Approach

Abstract — In this paper, a unified approach to infer gene regulatory networks using the S-system model is proposed. In order to discover the structure of large-scale gene regulatory networks, a simplified S-system model is proposed that enables fast parameter estimation to determine the major gene interactions. If a detailed S-system model is desirable for a subset of genes, a two-step method is proposed where the range of the parameters will be determined first using Genetic Programming and Recursive Least Square estimation. Then the exact values of the parameters will be calculated using a multi-dimensional optimization algorithm. Both downhill simplex algorithm and modified Powell algorithm are tested for multi-dimensional optimization. Simulation results using both synthetic data and real microarray measurements demonstrate the effectiveness of the proposed methods. I

Inference of Gene Regulatory Networks using Genetic Programming and Kalman Filter”, Gensips

In this paper, gene regulatory networks are infered through evolutionary modeling and time-series microarray measurements. A nonlinear differential equation model is adopted and an iterative algorithm is proposed to identify the model, where genetic programming is applied to identify the structure of the model and Kalman filtering is employed to estimate the parameters in each iteration. Simulation results using synthetic data and microarray measurements show the effectiveness of the proposed scheme. I