In silico model-driven assessment of the effects of single nucleotide polymorphisms (SNPs) on human red blood cell metabolism.
The completion of the human genome project and the construction of single nucleotide polymorphism (SNP) maps have lead to significant efforts to find SNPs that can be linked to pathophysiology. In silico models of complete biochemical reaction networks relate a cell's individual reactions to the function of the entire network. Sequence variations can in turn be related to kinetic properties of individual enzymes, thus allowing an in silico model-driven assessment of the effects of defined SNPs on overall cellular functions. This process is applied to defined SNPs in two key enzymes of human red blood cell metabolism: glucose-6-phosphate dehydrogenase and pyruvate kinase. The results demonstrate the utility of in silico models in providing insight into differences between red cell function in patients with chronic and nonchronic anemia. In silico models of complex cellular processes are thus likely to aid in defining and understanding key SNPs in human pathophysiology.
Jamshidi
Neema
N
Department of Bioengineering, University of California-San Diego, La Jolla, California 92093-0412, USA.
Wiback
Sharon J
SJ
Palsson B
Bernhard Ø
BØ
D019295:Computational Biology / Q000379:methods; D004912:Erythrocytes / Q000737:chemistry* / Q000201:enzymology / Q000378:metabolism*; D005955:Glucosephosphate Dehydrogenase Deficiency / Q000235:genetics; D006801:Humans; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D011770:Pyruvate Kinase / Q000172:deficiency / Q000235:genetics
D011770:Pyruvate Kinase