Can engineered bacteria help control cancer?
Explore the source record for details and available documents.
Animals↗
Biomedical subjects
N S Forbes
Publications and source records attributed to N S Forbes.
Using isotopomer path tracing to quantify metabolic fluxes in pathway models containing reversible reactions.
As a more complete picture of the genetic and enzymatic composition of cells becomes available, there is a growing need to describe how cellular regulatory elements interact with the cellular environment to affect cell physiology. One means for describing intracellular regulatory mechanisms is concurrent measurement of multiple metabolic pathways and their interactions by metabolic flux analysis. Flux of carbon through a metabolic pathway responds to all cellular regulatory systems, including changes in enzyme and substrate concentrations, enzyme activation or inhibition, and ultimately genetic control. The extent to which metabolic flux analysis can describe cellular physiology depends on the number of pathways in the model and the quality of the data. Intracellular information is obtainable from isotopic tracer experiments, the most extensive being the determination of the isotopomer distribution, or specific labeling pattern, of intracellular metabolites. We present a rapid and novel solution method that determines the flux of carbon through complex pathway models using isotopomer data. This time-consuming problem was solved with the introduction of isotopomer path tracing, which drastically reduces the number of isotopomer variables to the number of isotopomers observed experimentally. We propose a partitioned solution method that takes advantage of the nearly linear relationship between fluxes and isotopomers. Whereas the stoichiometric matrix and the isotopomer matrix are invertible, simulated annealing and the Newton-Raphson method are used for the nonlinear components. Reversible reactions are described by a new parameter, the association factor, which scales hyperbolically with the rate of metabolite exchange. Automating the solution method permits a variety of models to be compared, thus enhancing the accuracy of results. A simplified example that contains all of the complexities of a comprehensive pathway model is presented.
Algorithms↗
Optimization of cytochrome P4502D6 (CYP2D6) phenotype assignment using a genotyping algorithm based on allele frequency data.
Cytochrome P4502D6 (CYP2D6) is a highly polymorphic gene locus with > 50 variant alleles which lead to a wide range in enzymatic activity. So called poor metabolizers are carriers of any two non-functional alleles of the CYP2D6 gene. CYP2D6 genotyping is cumbersome and the question of how much genotyping is necessary for an accurate phenotype prediction is still debated. The goal of this study was to determine the optimum amount of genotyping required to accurately predict the phenotype at a reasonable cost in a white North American population. To address this issue, we designed a polymerase chain reaction (PCR)/restriction fragment length polymorphism-based genotyping strategy to detect 'key' mutations linked to extensive metabolizer or poor metabolizer associated alleles in combination with extra-long PCR (XL-PCR). All mutations with the exception of gene deletions and duplications are detectable by simple restriction digestion analysis and agarose gel electrophoresis. In addition, we utilized a genotyping algorithm based on our own and published allele frequency data and phenotype analysis to calculate the probability of a correct genotype (and thus, phenotype) assignment. As little as one XL-PCR reaction followed by a maximum of six reamplification reactions allows an accurate prediction of an individual's genotype to 99.15%. As few as four reamplification reactions identify 97.9% of poor metabolizer individuals. We evaluated our model in 208 white North Americans by testing for the presence of 'key' mutations linked to CYP2D6*2, *3, *4, *6, *7, *8, *9, *10, *11, *12, *15, *17 and *18 alleles and the *5, *13 and *16 gene deletions. For all individuals, the correct phenotype has been predicted. Discordant phenotype assignment occurred in only two individuals which subsequently was attributed to CYP2D6 inhibition by concomitant drug therapy.
Algorithms↗