Difference between revisions of "Team:NCTU Formosa/Achievements"

     The main idea of our system was inspired by the urgent need to achieve the maximum productivity and maintain healthy soil at the same time. We participated in Asia Agri-Tech Expo to know the current fertilizer status. Also, we visited the Agricultural Improvement Station and Professor Yang, a Chung Hsing University professor, to find out the problem of unbalanced microbiota. They gave us tips on how to perform our experiments and meet the needs of the public.

     We improved the sequence of 2016 NCTU_Formosa team did before. We linked Sf1a and Lectin with GS linker to increase the folding efficiency of these two protein. Compared the result with 2016 NCTU_Formosa team, the plant protecting effect of our improvement part was more obvious, which larvae consumed less leaves areas than previous part. Moreover, larva also has abnormal movement after consuming leaf coated with the improved protein.

     We’ve built Microbiota prediction model, Peptide prediction model, Growth model, and Productivity model, NGS data analysis. After computing again and again by machine learning, this system is more and accurate and precise to predict the need volume of bio-stimulators and its effect to the microbiota.

     We build several models to integrate the intelligent agriculture system that simulates and tells us the complex relationships of bacteria in the soil. Through the system, we can know the microbiota adjusted by the bio-stimulator and predicts the effects of any bio-stimulator. Also, we build up the website to let user enter the amount of nitrogen, phosphate, potassium and let user know how much bio-stimulators need. After adding bio-stimulators our system suggested, the crops achieve maximum productivity and soil become healthier at the same time.

     For bio-stimulators, we chose six bacteriocins to construct BioBricks consisting of the T7 promoter, the ribosome binding site and our peptide sequence, then purify by the intein and chitin binding domains. We test the minimum inhibitory concentration, which can inhibit the growth of B. subtilis to the desired level. For bio-sensors, we got αS1-casein, the amino acid sequence from NCBI, and optimize its expression in E. coli through adjusting the DNA sequence. Also, we added a GS linker ahead to enhance the function of sensor. We ligated above fragment to pET30a vector and transformed the plasmid to E. coli BL21 DE3 and expressed protein.

     We worked in collaboration with NTHU_Formosa. We confirm the parts of NTHU_Formosa team. We did DNA electrophoresis to confirm the size of inserted vectors which are essential in the process of emitting the light. Also, we share the basic concept of designing websites and make them more familiar with web languages. For NTHU_Formosa team, they ran an SDS-PAGE of our bacteriocins to help us check if our constructs were correctly expressed.

     To make our project more complete, we asked for many suggestions of professors and government agricultural improvement institute to learn more about the dosage of fertilizer farmer used. Also, we explore the possibilities of our solutions and the problems we may face.

     The purpose of InterLab experiment is to standardize measurement techniques to make the data from synthetic biology laboratories around the world easier to compare with. This year, we focus on converting the fluorescence readings to absolute cell counts instead of relying on O.D. measurements since O.D. measurements usually vary from lab to lab. We calibrate and measure the value to complete the experiment.