Project Inspiration and Description

Overview

Our software constructs an optimal synthetic pathway in E. coli or yeast based on the desired product provided by the user. In such a synthetic pathway, we will comprehensively consider the requirements and provide information about the enzymes needed for each step of the reaction. Finally, along with the appropriate promoter, the sequences of all the required enzymes are joined together to form a backbone of a biobrick for the user. At the same time, the relevant research literature , as well as a post-experiment feedback community, will be provided.

Why this project —— Meet the needs

A computational tool for pathway design and reconstruction is needed when synthetic biologists want to optimize genetic processes within cells, model for yield prediction, make flux balance analysis and generate value-added products. However, when actually establishing a metabolic pathway, it is a cumbersome problem to separately purchase different enzymes from different suppliers and transfer them into chassis. We consider that all the enzymes in a pathway can be constructed in the same plasmid to transfer at one time. And then, enzymes expression regulation under different conditions will ensure the realization of the pathway. In this process, synthetic DNA may be an indispensable part. Although the cost of synthetic DNA is not low at present, it continues to decline. We believe that synthetic DNA will be popular in the future, and by that time, our tools will be more practical.

How we start —— Inspiration inside iGEM

We appreciate three previous iGEM projects that provide part of our inspiration:

1. Team: Tongji-Software 2018——Their useful tool AlphaAnt shows us the framework to design a pathway.
2. Team: HokkaidoU_Japan 2012——Their experiments give us confidence to construct multiple enzymes on the same plasmid.
3. Team: IIT-Madras 2017——Their statistics on codon preferences give us inspiration for sequence optimization.

What we are doing

• On the main body, based on the project of Tongji-Software in 2018, we optimize the algorithm by pruning, and expand the database of the reaction, adding novel reactions. [1]
• With reference to the frequency of use of various biological chassis, there are two chassis options available for users: E. coli and yeast. [2] We will produce different results depending on the strain selected by the user.
• We select enzymes with higher catalytic efficiency by the nature of the parameters of the enzyme itself. [3] To ensure that the enzyme is expressed normally, we use taxonomic knowledge and sequence alignment analysis to select strains that are close to the selected chassis as the sequence source for the enzyme. Subsequently, the codons are optimized. In regulating the expression of synthetic sequences, we integrate the relevant signaling pathways to make the biobrick skeleton in the results more practical. At the same time, the comprehensive physical and chemical properties of the enzyme are also part of the results, so that users can apply it in actual experimental operations.
• In addition, we consider the association recommendations for the literature on products or enzymes. In this way, users may be able to explore more research directions.
• After all, the results of the design software are ideal. We need to establish a community where synthetic biologists can exchange ideas and apply feedback after the actual experiment. This community not only provides users with a reference to the results, but also provides a direction for our developers to improve the software.

Reference

[1] Hadadi N, MohammadiPeyhani H, Miskovic L, Seijo M, Hatzimanikatis V. Enzyme annotation for orphan and novel reactions using knowledge of substrate reactive sites. Proc Natl Acad Sci U S A. 2019;116(15):7298–7307.

[2] Juhyun Kim, Manuel Salvador, Elizabeth Saunders, Jaime González, Claudio Avignone-Rossa, and Jose Ignacio Jiménez. Properties of alternative microbial hosts used in synthetic biology: towards the design of a modular chassis. Essays Biochem. 2016 Nov 30; 60(4): 303–313.

[3] Pablo Carbonell, Jerry Wong, Neil Swainston, Eriko Takano, Nicholas J Turner, Nigel S Scrutton, Douglas B Kell, Rainer Breitling, Jean-Loup Faulon, Selenzyme: enzyme selection tool for pathway design, Bioinformatics, Volume 34, Issue 12, 15 June 2018, Pages 2153–2154.