Overview
As an effective drug for the treatment of type Ⅱ diabetes, the huge demand of acarbose is increasing year by year. Therefore, we need to find a cell transformation scheme to expand the production of acarbose by actinomycetes.
This project intends to develop a generalized modeling software. By adding enzyme constraints to the genome-wide metabolism model of Actinomycetes, and combining with deep learning to predict the Kcat value of the model parameter, in order to fill in the protein database of Actinomycetes.
With the help of our software, constrained flux analysis and cell modification scheme recommendation can be performed for GEM models of various microorganisms, which is helpful to identify gene manipulation targets and drug targets for metabolic engineering, and promote the identification of gene functions, as well as expand production of rare antibiotics and discover new antibiotics.
Function
Since acarbose is an important treatment for areas like type Ⅱ diabetes, our project builds a standard platform to expand the production of acarbose by actinomycetes.
Our software provides a multifaceted contribution to the iGEM community. Teams following us can introduce more computing methods and add more functions to the platform, which allows more opportunities to expand production of rare antibiotics and discover new antibiotics.
The deletion provides a feasible solution for the reconstruction of a deep learning-based enzyme-constrained genome-wide metabolic model (DL-ecGEM) for actinomycetes. The reconstructed model is used to conduct simulation experiments to find the key enzymes for the target reaction, and at the same time, combined with the protein hot spot identification tool, to find the directional modification scheme for the key enzymes.This project has developed a multi-functional software integrating modeling, prediction and simulation, which is convenient for the rapid use of biological science researchers, reduces the waste of experimental costs, and shortens the experimental exploration period.
When studying actinomycetes or other types of organisms for which complete genomic, proteomic and metabolomic data are available, experimenters can build genome-wide metabolic models and use our software to simulate their metabolic capabilities under different conditions. By combining multiple predictive tools, antibiotic manufacturers can provide optimized and high-yield strategies for existing antibiotics. The software can also promote the exploration of new antibiotics in the study of actinomycetes in extreme environments such as the ocean, and accelerate the industrialization and clinical application of rare antibiotics.
On this basis, we hope to achieve high quality DL-ecGEM model reconstruction for any species, and to analyze the importance of the metabolic pathways of the target reactions, to predict the transformation protocols and self-validation of the important enzymes involved, and to achieve more rapid cell engineering solutions.
Feedbacks
Once our modeling platform was initially built, we contacted senior students in our cooperating lab to get feedback on the trial. We are happy to have received practical suggestions for improvement.
The senior students gave feedback that For researchers studying this actinomycete, macroscopic understanding of cellular metabolic growth becomes more intuitive, daily use of basic functions becomes easier and faster, and the ability to give reference changes in modified proteins can guide wet experiments.
But when they tried our software for retrofit solution recommendation, they got a high number of possible solutions, and conducting wet experiment verification was actually a relatively large workload for them, and they wondered if improvements could be made.
When understanding the functionality of our software, a judge at CCiC pointed out that there are some genes in Actinomycetes that are silent. He wondered if we can we use this tool to regulate the expression of genes and activate the silent genes.
We consindered this posibility thouroughly, and reached the agreement that we could look into the suggested direction.
Perhaps in terms of improving the direction of the software in the future, our modeling platform could reach out of the realm of enzymes and proteins, into the new area of gene regulation, given proper modification and improvement.