Inferring the skeleton cell cycle regulatory network of malaria parasite using comparative genomic and variational Bayesian approaches.
The development of new antimalarial drugs is urgently needed due to elevated drug resistance in the causative agents Plasmodium parasites. An intervention strategy based on the interruption of the parasite cell cycle could be undertaken using a systems-biology aided drug discovery approach. However, little is known about the components or the mechanism of parasite cell cycle control to date. In this proof of concept study, we attempted to infer the skeleton components using comparative genomic analysis and to uncover the genetic regulatory network (GRN) ab initio using a Variational Bayesian expectation maximization (VBEM) approach.
Tienda-Luna
Isabel M
IM
Department of Applied Physics, University of Granada, Granada, Spain.
Yin
Yufang
Y
Carrion
Maria C
MC
Huang
Yufei
Y
Cai
Hong
H
Sanchez
Maribel
M
Wang
Yufeng
Y
D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D002453:Cell Cycle / Q000235:genetics*; D018797:Cell Cycle Proteins / Q000235:genetics / Q000378:metabolism; D003198:Computer Simulation; D020869:Gene Expression Profiling; D053263:Gene Regulatory Networks; D018503:Genome, Protozoan / Q000235:genetics*; D006801:Humans; D008954:Models, Biological; D020411:Oligonucleotide Array Sequence Analysis; D010963:Plasmodium falciparum / Q000166:cytology* / Q000235:genetics*; D012372:ROC Curve
D018797:Cell Cycle Proteins