Journal of Proteomics & Bioinformatics
Open Access
ISSN: 0974-276X
+44 1223 790975
ISSN: 0974-276X
+44 1223 790975
Ezekiel Adebiyi
Accepted Abstracts: J Proteomics Bioinform
Metabolic pathways are processes by which the parasite produces the energy and components it needs to survive. The formally popular antimalaria drug Chloroquine, inhibit multiple sites in metabolic pathways leading to neutrophil superoxide release. Currently, the popular antimalarial drug artemisinin?s biological mode of action is controversial. Reports have shown that the parasite is growing resistance to existing drugs. Therefore, there is a renew effort to decipher clearly the metabolic pathways in P. falciparum. In this work, we have adapted and extended a method (formally used by Oyelade et al., 2010 for extracting signalling pathways) to extract linear and non linear metabolic pathways from the malaria parasite, Plasmodium falciparum metabolic weighted graphs (networks). The weights are calculated using the metabolite degrees and relevant pathways are obtained using atom mapping information. Adopting the representation of the biochemical metabolic network as we have in Koenig et al., 2006, we are able to make our algorithm tenable to accept metabolic network from other source apart from KEGG. This gives us opportunity for the first time to compare the metabolic pathways extracted from different metabolic networks. We did a preliminary run of our algorithm (for four selected pathways: Pyruvate, Glutamate, Glycolysis and Mitochondrial TCA) on graph from KEGG and compare our results with the results obtained from recent algorithms: ReTrace and atommetanet. Our results compare favourably with these two algorithms. Considering the results with genes classified into these pathways from Plasmodb, resulted into a lot of false positiveness. Furthermore, we compare the runs of our algorithm on graphs from KEGG and PlasmoCyc (from BioCyc). The results are remarkably different and the results from PlasmoCyc produce less false positiveness when compared to the results from Plasmodb. We identify 2, 1, 2, 4 gene(s) in addition to belong to these pathways respectively. Some of the genes have not been classified earlier to any known metabolic pathways.
Ezekiel Femi Adebiyi obtained the Ph.D degree in 2002 in Computer Science- Algorithms and Bioinformatics from the University of Tubingen, Germany on a German Academic Exchange Programme (DAAD) Scholarship. He had his basic and research training in Mathematics at the University of Ilorin, Nigeria with the B.Sc (Second Class Upper) and the M.Sc degrees. Starting as a Graduate Assistant at the University of Ilorin, Nigeria in 1992, Ezekiel Adebiyi rose rapidly to become a Professor at Covenant University in June 2010. In between this working track, he served as Research Fellow in several institutes in the US and also in Europe. He has also worked in World-Class organizations and therefore has a rich blend of academic and professional experience. He is the President, Nigeria Society of Bioinformatics and Computational Biology, formerly Vice-President and presently Secretary, African Society of Bioinformatics and Computational Biology. Prof. Ezekiel Adebiyi is the leader of the Covenant University Bioinformatics Research. He is industrious, with a passion to turning his theories to products. His main research interests are in computational complexity and computational molecular biology, with emphases on the bioinformatics of malaria research.