Insecticide resistance is a genetic characteristic involving changes in one or more insect genes. It is also a major public health challenge combating world efforts on malaria control and strategies. The Malaria vector, Anopheles gambiae (A. gambiae) has formed resistance to the existing classes of insecticides, especially pyrethroid, the only class approved for Indoor Residual Spray (IRS) and Long-Lasting Insecticide Treated Net (LLITNs). Identification of novel insecticidal targets for the development of more effective insecticides is therefore urgent. However, deciding which gene products are ideal insecticidal targets remains a difficult task in the search. To this end, it has been shown that the dissection and comprehensive studies of biochemical metabolic networks has great potential to effectively and specifically identify and extract essential enzymes as potential insecticidal targets. Using the PathoLogic programme, AnoCyc, a pathway/genome database (PGDB) for A. gambiae AgamP3 was constructed, using its annotated genomic sequence and other annotated information from ANOBASE, VECTORBASE, UNIPROT and KEGG databases. Furthermore, additional annotations to proteins annotated as “hypothetical” was gathered using specifically two annotation tools from the DKFZ HUSAR open servers, namely GOPET and DomainSweep and present a more comprehensive annotated PGDB for A. gambiae AgamP3. The resulting PGDB for A. gambiae AgamP3 has been deployed under the www.bioCyc.org databases. Next, a graph based model that analyzed the topology of the metabolic network of Anopheles gambiae was developed to determine the essential enzymatic reactions in the networks. A refined list of 61 new potential insecticidal candidate targets was obtained, which include one clinically validated insecticidal target and host of others with biological evidence in the literature. Finally, the biochemical network of A. gambiae was overlaid with two gene expression data obtained from the treatment of A. gambiae with pyrethroid (permethrin) to elucidate some tightly linked resistance genes and deduce computationally, for the first time, its resistance mechanism(s) toward this insecticid
