Difference between revisions of "Team:SCUT China/Overview"

Parts Overview

In science, crafting theoretical models can help understand, predict and improve experiments and their interpretation. In our project, acid stress is often encountered during industrial fermentation as a result of the accumulation of acidic metabolites. Acid stress is often encountered during industrial fermentation as a result of the accumulation of acidic metabolites. Acid stress increases the intracellular acidity and can cause DNA damage and denaturation of essential enzymes, thus leading to a decrease of growth and fermentation yields^[1]. We hope to change the acid tolerance of E. coliMG1655 by regulating the expression of genes gadB, gadC, yabS and katA.

First, we constructed a transcriptional regulatory pool with an outbound capacity of 10000. And we changed the promoter strength of four genes and recorded the final OD₆₀₀ of strains under the same initial growth conditions. Then we find the optimal promoter strength combination through mathematical modeling. To this end, we established the GA-BP model.

Genetic algorithm is a global optimization algorithm, being capable of finding the globally optimal solution in complex, multi-crest, non-differentiable vector spaces. Utilizing genetic algorithm to search for the initial weights of the BP neural network could guarantee a relatively high probability to obtain the global optima, and therefore the initial search by the genetic algorithm is a preferred means to overcome the shortcoming of BP neural network. It is proved that the BP model optimized by GA is superior to the pure BP model.

Depending on the problem we're trying to solve, the input vector is the strength of four promoters, and output vector is the final OD₆₀₀ of strains. Combined with genetic algorithm, the model has the characteristics of both local precise search and global search.

[1] Xianxing Gao, Xiaofeng Yang et al. (2018). Engineered global regulator H‑NS improves the acid tolerance of E. coli.