Introduction
Our project is based on several complex subprojects, which together form GLP.exe. The following content will solely focus on our selected drylab parts of our multi-component system.
Metabolic Modeling
Introducing microbiome scale metabolic models to the iGEM competition makes this part of our project a novel deliverable. We did not only extend our characterization of the probiotic E. coli Nissle 1917 strain, we also investigated its interactions and reactions with the gut microbiome and drew conclusions which made our wetlab work easier.
Since all three modelled compositions for the gut microbiome came from recent respectively well established publications, the interactions and co-dependencies which we investigated are on a state of the art scale with current scientific knowledge.
On top of our efforts toward the interpretation of all the scores, we calculated for several interactions, we offer all models we created this iGEM season as download under Parts/Downloads. This way we hope to help future iGEMers to use metabolic modeling as a crucial part of their project, just the way we did.
For more in depth explanations and detailed results please visit our model page.
RNA-Seq
Our extensive E. coli Nissle 1917 characterization was a great success. Due to our very creative and novel experimental design, we can offer unprecedented insights into the reaction of E. coli Nissle 1917 to Diabetes drugs such as Metformin, to bile acid, to mGAM as growth medium and to interactions with bacterial supernatants.
By virtue of our comprehensive determination of the tipping points of the stress factors for E. coli Nissle 1917, as well as our EMBL samples being anaerobic (to represent the gut environment), the characterization was conducted under fully realistic environments. For more in depth explanations and detailed results please visit our Nissle page.
CPP
During the course of this iGEM year, we developed C3Pred, a machine learning based tool, which allows for the comparison of efficacy of different cell penetrating peptides. Due to our regression approach yielding numerical values instead of classical classification approaches returning yes or no answers, researchers are for the first time equipped with a tool aiding with the design and evaluation of novel and unknown cell penetrating peptides.
We verified our tool on several experimentally characterized cell penetrating peptides by other iGEM teams in the iGEM registry. Hence, our tool works under realistic conditions and surely will be of great help to scientists working with cell penetrating peptides.
For more in depth explanations and results please visit our Software page.
