Using genome-scale models of Escherichia coli, we have implemented the OptORF algorithm (which considers gene deletions and transcriptional regulation) and compared its metabolic engineering strategies for ethanol production to those found using OptKnock (which considers reaction deletions).
We identified metabolic engineering strategies for ethanol production in E. coli using the OptORF formulation with an integrated model of metabolism and regulation, and compared the resulting strategies to ones using a previous approach based on reaction deletions (OptKnock).
The simulation results have verified the effectiveness of the proposed voltage regulation method and compared the regulation performance of the ESBC with the original ES.
For this, we employed the luciferase trans-regulation assay and compared the transcriptional activity of rx2 pCREs with mutated Sox or Gli motifs (rx2 pCRE mtSox and rx2 pCRE delGli) to the corresponding wild-type activity (Fig 6C).
An important goal of the present work was to test the suitability of liver slices as a model to study PPARα dependent gene regulation and compare it with other available model systems for human liver.
Thus, in view of the fact that Ins is a key substrate in the biosynthesis of many essential cell components, we decided to assess the consequences of its metabolic diversion versus its complete elimination through MIPS down-regulation, and compare the effects associated with each approach.
To study the role of ADAR proteins in RNA editing and gene regulation, we sequenced and compared the DNA and RNA of human B cells.
We analyzed California cases of V. vulnificus infection before and after the regulation's implementation and compared case data with data from other states.
Since we are specifically interested in the role PPARα plays in nutrient-mediated gene regulation, we used and compared in our studies 3 natural agonists normally found in the diet [oleic acid (OA, C18 1), eicosapentaenoic acid (EPA, C20 5) and docosahexaenoic acid (DHA, C22 6)].
Further, we calculated a moving average of regulation along all chromosomes and compared this to 1000 randomly shuffled genomes, but found only one significant cluster of 11 regulated genes located close together on chromosome 1 (see Fig. S2 and Discussion).
