Modeling scientists are increasingly turning to Phenotype MicroArrays to validate and optimize in silico models using wet lab data. Phenotype MicroArray tests cover most of core metabolism allowing many comparisons to be made to metabolic models. Discrepancies between models and PM data often facilitate model optimization.
Genome Scale Metabolic Reconstruction and Constraints-Based Modeling of the Antarctic Bacterium Pseudoalteromonas haloplanktis TAC125
The authors created a metabolic model of a cold-adapted bacterium from Antarctic sea water. The model was used to investigate metabolic adjustments at low temperature. After model refinement using PM data, the agreement rate was found to be 84%. Through integration of various omics datasets, the affects of temperature on the model were studied.
iOD907, the First Genome-Scale Metabolic Model for the Milk Yeast Kluyveromyces lactis
This compartmentalized (extracellular milieu, cytoplasm, mitochondrion and endoplasmic reticulum) metabolic model performed well when comparing to Phenotype MicroArray test results or public data. The model also compared favorably to chemostat data, and in silico vs. in vivo knockout experiments. The model can predict K. lactis response to environmental and genetic perturbations.
Phenotype MicroArray Determination of Metabolic Pathway Activities: Helicobacter pylori as an example
Carbon source utilization predicted by genome annotation was compared to carbon sources utilized in a Phenotype MicroArray test. Phenotype MicroArrays not only confirmed predictions but also found additional carbon sources and preferred carbon sources. Interestingly, additional carbon sources were utilized in the presence of 0.05% bovine serum albumin.
Phenotype MicroArray Technology
Biolog’s Phentoype MicroArray technology enables researchers to evaluate nearly 2000 phenotypes of a microbial cell in a single experiment. This integrated system of cellular assays, instrumentation and bioinformatics software provides cellular knowledge that complements molecular information, helping you interpret and find the relevant aspects in massive amounts of gene expression or proteomics data. Through comprehensive and precise quantitation of phenotypes, researchers are able to obtain an unbiased perspective of the effect on cells of genetic differences, environmental change, exposure to chemicals or drugs, and more.