|Title||Using in silico models to simulate dual perturbation experiments: procedure development and interpretation of outcomes.|
|Publication Type||Journal Article|
|Year of Publication||2009|
|Authors||Jamshidi N, Palsson BØ|
|Journal||BMC systems biology|
|Keywords||Biological Transport, Computational Biology, Energy Transfer, Erythrocytes, Glucosephosphate Dehydrogenase, Humans, Kinetics, Models, Biological, Oxidation-Reduction, Pyruvate Kinase|
BACKGROUND: A growing number of realistic in silico models of metabolic functions are being formulated and can serve as 'dry lab' platforms to prototype and simulate experiments before they are performed. For example, dual perturbation experiments that vary both genetic and environmental parameters can readily be simulated in silico. Genetic and environmental perturbations were applied to a cell-scale model of the human erythrocyte and subsequently investigated.
RESULTS: The resulting steady state fluxes and concentrations, as well as dynamic responses to the perturbations were analyzed, yielding two important conclusions: 1) that transporters are informative about the internal states (fluxes and concentrations) of a cell and, 2) that genetic variations can disrupt the natural sequence of dynamic interactions between network components. The former arises from adjustments in energy and redox states, while the latter is a result of shifting time scales in aggregate pool formation of metabolites. These two concepts are illustrated for glucose-6 phosphate dehydrogenase (G6PD) and pyruvate kinase (PK) in the human red blood cell.
CONCLUSION: Dual perturbation experiments in silico are much more informative for the characterization of functional states than single perturbations. Predictions from an experimentally validated cellular model of metabolism indicate that the measurement of cofactor precursor transport rates can inform the internal state of the cell when the external demands are altered or a causal genetic variation is introduced. Finally, genetic mutations that alter the clinical phenotype may also disrupt the 'natural' time scale hierarchy of interactions in the network.
|Alternate Journal||BMC Syst Biol|