PROJECT

PROJECT - 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.
①Team: Tongji-Software 2018——Their useful tool AlphaAnt shows us the framework to design a pathway.
②Team: HokkaidoU_Japan 2012——Their experiments give us confidence to construct multiple enzymes on the same plasmid.
③Team: 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.

REFERRENCE

[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.

PROJECT - DESIGN

DATA

The data of 2018 Tongji-Software team are used and updated, and the physicochemical properties of enzymes are collated in BRENDA database, including the ratio of Kcat to Km, Km value, optimal pH and optimal temperature.

SEARCHING ALGORITHM

RANKING CRITERIA

When scoring the pathway, we consider thermodynamic feasibility, competition of heterologous reactions, frequency of reaction and toxicity of compound, which are used in last year’s project. In the function of Enzyme Selection, we searched for the presence of the required enzyme in the close source bacteria of the engineering bacteria according to the affinity of the bacteria. If the same enzyme exists in multiple near-source bacteria, we will arrange the sequence according to the physicochemical properties of the enzyme, including the ratio of Kcat to Km, Km value, optimal pH and optimal temperature. In order to measure the adaptability of physical and chemical properties, we build a model.

LITERATURE RECOMMENDATION

Considering that users will perform some personalized operations in the practical application of the result pathway, we provide the relevant literature keywords of compounds in Pubmed required for each step of the reaction in the form of word clouds. This may inspire users with possible target compounds or a follow-up research direction.

CONDON OPTIMIZATION

We searched the codon preference databases of E. coli and yeast from the Internet, and modified the infrequently used codon with the information in the database to avoid the difficulties caused by translation and gene expression, and improve the success of expressing foreign genes.

PROJECT - CONTRIBUTION

Building a complete pathway requires three steps: searching for a pathway, selecting related enzymes, and designing parts. These steps are quite difficult for a worker to realize by himself, so we aim at making the whole process into one software to reduce workers pressure on complicated and boring work.

On this basis, we developed our software called Pathlab, whose core idea is modular design. We made each step into certain module, and for the users, they can choose to use one module or the combination of any modules.

In brief, Pathlab makes people who work in synthetic biology have a platform to search a certain pathway that can be applied.

PROJECT - VALIDATION

In order to verify whether pathlab can achieve the expected function, we use software to search several paths and compare them with the actual paths in the literature.

EXAMPLE 1

EXAMPLE 2

EXAMPLE 3 (中国农业大学的通路，链接至collaboration)

EXAMPLE 4（四川大学的通路，链接至collaboration）

PROJECT - DEMONSTRATION

PROJECT - IMPROVE

Our software was built on the project of last year's Tongji_Software team. The main improvement is to change the searching algorithm and add software functions, including the enzyme selection and parts design

SEARCHING ALGORITHM

In theory, the greedy algorithm may fail to get a global optimum while improving the speed. However, we use both DFS algorithm and greedy algorithm to find specific pathways, and then compare the results. We found that based on the existing database, we made tests to check the accuracy of Greedy in limited reactions compared with DFS, and the accuracy of two algorithms is similar, but the speed of greedy algorithm is significantly improved, so we think this is a good improvement.

ADDITIONAL FUNCTIONS

In choosing the enzymes needed for each reaction, we establish our own judgment model. At the same time, the key words related to compounds needed in the pathway were sorted out, and these key words would be presented as a word cloud. When providing the final result of enzyme selection to the user, the optimized sequence is provided considering the codon preference for the engineering bacteria.

In parts design, we cleared up the data from iGEM part database, and we made a search engine which enables users to search parts with their name or a certain function.

These functions can be used as a whole, meanwhile they can be used separately.

What’s more, users can apply for their own account on our website, and can leave a message on the webpage. We will always pay attention to your message and constantly optimize the Pathlab, and users can also make comments about optimized enzymes or different parts. Moreover, your message will be seen by other users, and users can communicate through the message board and user can read others comments about the enzyme or parts they are going to use.

PROJECT - COLLRBORATION

The paths found by our software are based on databases and algorithms, which need to be verified by practical experiments. At the same time, the results obtained by our software can provide support for the path design of the experimental team.

Through CCiC, we had a deep communication with three other experimental teams related to pathways construction. We know the substrates they own and the products they want to get，then try to design parts through Pathlab search paths and verify with the pathways they implement.

COLLABORATION 1：TONGJI-CHINA

Because we are from the same school, Tongji_China and us have more integrated collaboration from the very beginning. We had conferences together for several times, and their project is about manufacturing, meanwhile, ours about pathway search, so we get feedback from them after they used our software, and our results also inspire them sometimes.

One of their suggestions which had a great influence to us is that we should avoid that some of the result is that putting some of the groups on a compound and then take it down, which is pretty useless. So we added codes to avoid this kind of situation take place. And we had searched the pathway they used, but we didn't get a realistic and practical result, so we realized that the data we used had limitation.

For Tongji_China, they tried to improve the synthesis of indigo, and the method of finding new pathways could be found through reading literature, experimental attempts through the combination of existing pathways, or simulated synthesis through software design and retrieval. Therefore, we provided help in software retrieval. But in the existing database we did not find useful results, because the data in the synthetic indigo pathways are already published literature or need the experimental materials are too expensive, not suitable for synthesis, but we found the reaction of the upstream and downstream information about indigo provide certain reference and support for their experiments, they also try to give us your attempt to retrieve database does not exist in the reaction, to join the design new reaction, to design efficient and useful pathways.

COLLABORATION 2：中国农业大学

COLLABORATION 3：电子科技大学

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COLLABORATION 4：WASHIFTON IGEM

Washington iGEM invited us to participate in the manufacturing of their audiobook which is a popular science of biology, and we mainly do some translation and recording word for them， thus making chinese students can read and know what biology is.

COLLABORATION 5：SASTRA IGEM

SASTRA iGEM invited us to participate in their manufacturing of their magazine, and our collaboration forms including but not limited to write articles about synthesis biology and experiment, provide interviews with professionals, and make the theme of synthesis biology or to shoot related photography.

COLLABORATION 6：UCD IGEM

We participated in UCD’s research about the use of mammals in this iGEM competition.