Comparing metabolic network models based on genomic and automatically inferred enzyme information from Plasmodium and its human host to define drug targets in silico

Comparing metabolic network models based on genomic and automatically inferred enzyme information from Plasmodium and its human host to define drug targets in silico

Plasmodium falciparum causes the most severe malaria pathogen and has developed resistance to existing drugs making it indispensable to discover new drugs. In order to predict drug targets in silico, a useful model for the metabolism is needed. However, automatically reconstructed network models typically cover more non-confirmed enzymes than confirmed enzymes of known gene products....
 
 
 
visit (http://www.ncbi.nlm.nih.gov/pubmed/20804860)
 
 
Fatumo S, Plaimas K, Adebiyi E, König R.
JESUS
 
Other publications by this author (view profile)
 
 
Computational Biology and Bioinformatics in Nigeria
 
A Step Further on the Combinatorial Number Theoretic Aspect of Ayo Game
 
Critical Analysis of Decision Making Experience with a Machine Learning Approach in Playing Ayo Game.
 
Aligning Multiple Sequences with Genetic Algorithm.
 
Clustering Plasmodium falciparum Genes to their Functional Roles Using k-means.
 
In-silico evaluation of malaria drug targets.
 
Simulating the Efflux Models of Chloroquine in the Plasmodium Falciparum Resistance (PfCRT) Mechanism
 
Computational Discovery of Drugs Resistance Mechanism(s) of the Malaria Parasite to Tetracyclines and Chloroquines
 
A Functional Workbench for Anopheles gambiae Micro Array Analysis
 
Computational and experimental analysis identified 6-diazo-5-oxonorleucine as a potential agent for treating infection by Plasmodium falciparum
 
ANOSPEX: a stochastic, spatially explicit model for studying Anopheles metapopulation dynamics
 
In silico models for drug resistance.
 
Reducing the time requirement of k-means algorithm.
 
Ethnobotanical survey for potential anti-malarial plants in south-western Nigeria.
 
Comparing metabolic network models based on genomic and automatically inferred enzyme information from Plasmodium and its human host to define drug targets in silico
 
Computational identification of signalling pathways in Plasmodium falciparum.
 
Ten simple rules for organizing a virtual conference--anywhere
 
Estimating novel potential drug targets of Plasmodium falciparum by analysing the metabolic network of knock-out strains in silico.
 
New insights into the genetic regulation of Plasmodium falciparum obtained by Bayesian modeling.
 
An efficient algorithm for finding short approximate non-tandem repeats
 
SYNAPSES – Bridging the gap between Biologists and Bioinformaticians. 2nd international Workshop on Pattern Discovery in Biology
 
Detection of Recombination in Variable Number Tandem Repeat Sequences
 
Extracting Common Motifs under the Levenshtein Measure: Theory and Experimentation