Mdabc

Abstract

Motif discovery is one of the most popular problems in molecular biology. There are many solutions provided by researchers. In this paper we have adopted the features of the Artificial beecolony algorithm (ABC) and designed a sequential algorithm to solve motif problem and named it MDABC. Artificial bee colony algorithm is a population based heuristic search technique used for optimization problem. We have performed experiments with the nucleotide sequences of homo sapiens (human) and mouse viz. CDRT4, MACF1, Zfa, TNFRSF19 and TICAM2. The ABC algorithm applied to the CDRT4, MACF1, Zfa, TNFRSF19 and TICAM2 DNA sequence for determining the motif of length 10, 20 and 30 using the maximum number of cycle (MCN) or the maximum number of generation is equal to 250, 500 and 1000. Our result shows a promising superior performance of the algorithm. As we will see, our results surpass the results obtained by other approaches proposed in the literature.

Heavy metal remediation in copper (Cu) contaminated soil of Andosol and Latosol using spinach (Amaranthus sp.) as bioaccumulator

“…The level of heavy metal pollution that is exposed to the environment causes serious problems in nature because these metals cannot be decomposed naturally. According to Singh et al [2] and Sankhla et al [3] there are at least 20 metals classified as toxic waste which pose a major risk to human health. Heavy metals that are commonly found in the environment such as Cd, Cu, Pb, Co, Zn and Cr have phytotoxic properties at both low and very high concentrations which are detected in waste [4].…”