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<div id="truePage" class="realPage"> | <div id="truePage" class="realPage"> | ||
<div id="firstnav" class="ui fixed collapsed borderless ten menu"> | <div id="firstnav" class="ui fixed collapsed borderless ten menu"> | ||
| − | + | <a id="sdin-link" href="#"> | |
<img id="sdin-icon" src="https://static.igem.org/mediawiki/2017/1/17/T--SYSU-Software--project_sdin-small.png" alt="logo"> | <img id="sdin-icon" src="https://static.igem.org/mediawiki/2017/1/17/T--SYSU-Software--project_sdin-small.png" alt="logo"> | ||
</a> | </a> | ||
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<div class="ui simple dropdown item page-nav" id="active-page"> | <div class="ui simple dropdown item page-nav" id="active-page"> | ||
<a href="https://2017.igem.org/Team:SYSU-Software/Project" class="page-link">Project</a> | <a href="https://2017.igem.org/Team:SYSU-Software/Project" class="page-link">Project</a> | ||
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<img class="centered" id="laptop" src="https://static.igem.org/mediawiki/2017/5/58/T--SYSU-Software--project_laotop.png" alt=""> | <img class="centered" id="laptop" src="https://static.igem.org/mediawiki/2017/5/58/T--SYSU-Software--project_laotop.png" alt=""> | ||
</div> | </div> | ||
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<section class="full" id="description"> | <section class="full" id="description"> | ||
<img src="https://static.igem.org/mediawiki/2017/5/51/T--SYSU-Software--project_combo_chart.png" alt="" class="icon"> | <img src="https://static.igem.org/mediawiki/2017/5/51/T--SYSU-Software--project_combo_chart.png" alt="" class="icon"> | ||
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<img src="https://static.igem.org/mediawiki/2017/0/0b/T--SYSU-Software--project_detection.png" alt="not provide" class="centered blue-shadowed screen-crop"> | <img src="https://static.igem.org/mediawiki/2017/0/0b/T--SYSU-Software--project_detection.png" alt="not provide" class="centered blue-shadowed screen-crop"> | ||
<p> | <p> | ||
| − | <span class="bold">2. Browse on S-Din:</span> When surfing on S-Din, we found that UV detection is a | + | <span class="bold">2. Browse on S-Din:</span> When surfing on S-Din, we found that UV detection is a not a hot area. |
</p> | </p> | ||
<img src="https://static.igem.org/mediawiki/2017/5/5a/T--SYSU-Software--project_uv-search.png" alt="not provide" class="centered blue-shadowed screen-crop"> | <img src="https://static.igem.org/mediawiki/2017/5/5a/T--SYSU-Software--project_uv-search.png" alt="not provide" class="centered blue-shadowed screen-crop"> | ||
<p> | <p> | ||
| − | <span class="bold">3. Search the previous project:</span> Standing on the shoulders of giants, we wondered predecessors will give us some ideas. We searched for ‘UV detection’ . With data analysis and | + | <span class="bold">3. Search the previous project:</span> Standing on the shoulders of giants, we wondered predecessors will give us some ideas. We searched for ‘UV detection’ . With data analysis and 29.8 score, we found ETH_Zurich 2012, and marked it. |
</p> | </p> | ||
<img src="https://static.igem.org/mediawiki/2017/d/d1/T--SYSU-Software--project_uv-search2.png" alt="not provide" class="centered blue-shadowed screen-crop"> | <img src="https://static.igem.org/mediawiki/2017/d/d1/T--SYSU-Software--project_uv-search2.png" alt="not provide" class="centered blue-shadowed screen-crop"> | ||
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<p> | <p> | ||
| − | <span class="bold">6. The plasmid design:</span> After finished our design, we click on the bottom | + | <span class="bold">6. The plasmid design:</span> After finished our design, we click on the bottom , then the design drawing of our plasmid came out. So we had a system when detect UV will produce AmilCP, a kind of blue protein. |
</p> | </p> | ||
<!-- <img src="../images/project/T--SYSU-Software--jietu4.png" alt="not provide" class="centered blue-shadowed screen-crop"> --> | <!-- <img src="../images/project/T--SYSU-Software--jietu4.png" alt="not provide" class="centered blue-shadowed screen-crop"> --> | ||
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<div class="page-content"> | <div class="page-content"> | ||
<h5>Simulation</h5> | <h5>Simulation</h5> | ||
| − | <p>We got the models in our software | + | <p>We got the models in our software. Picture.1 and 2 show the result.</p> |
<img src="https://static.igem.org/mediawiki/2017/e/ed/T--SYSU-Software--project_simulation-result.png" alt="not provide" class="ui image middle centered blue-shadowed"> | <img src="https://static.igem.org/mediawiki/2017/e/ed/T--SYSU-Software--project_simulation-result.png" alt="not provide" class="ui image middle centered blue-shadowed"> | ||
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<h5>Experimental data</h5> | <h5>Experimental data</h5> | ||
| − | <p>After the sequencing confirmation, Escherichia coli strain BL21 (DE3) was transformed with plasmids. We didn’t have Liquid culture systems with UV light, so we used plate cultivation. We placed the dish on a 10-15W weak intensity UV lamp at a distance of about 30 cm, and got samples every few hours. 22 hours later, we got the blue bacteria moss (Photo.3 ). The bacteria was been broken by ultrasonic crusher, and then we use the OD588 to measure the concentration of the blue protein (Photo. 4) | + | <p>After the sequencing confirmation, Escherichia coli strain BL21 (DE3) was transformed with plasmids. We didn’t have Liquid culture systems with UV light, so we used plate cultivation. We placed the dish on a 10-15W weak intensity UV lamp at a distance of about 30 cm, and got samples every few hours. 22 hours later, we got the blue bacteria moss (Photo.3 ). The bacteria was been broken by ultrasonic crusher, and then we use the OD588 to measure the concentration of the blue protein (Photo. 4).</p> |
<div class="double-pic"> | <div class="double-pic"> | ||
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</section> | </section> | ||
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<section class="full" id="demonstrate"> | <section class="full" id="demonstrate"> | ||
<img src="https://static.igem.org/mediawiki/2017/8/84/T--SYSU-Software--project_edit_image.png" class="icon"> | <img src="https://static.igem.org/mediawiki/2017/8/84/T--SYSU-Software--project_edit_image.png" class="icon"> | ||
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<div class="interview"> | <div class="interview"> | ||
| − | <h5> | + | <h5>Haoquan Zhao</h5> |
| − | <p class="intro"> | + | <p class="intro">Ph.D. Student of bioinformatics , Columbia University</p> |
<div class="quote paragraph"> | <div class="quote paragraph"> | ||
<p>"The engagement of search and design is designed in an elegant way, I have to say I like this design"</p> | <p>"The engagement of search and design is designed in an elegant way, I have to say I like this design"</p> | ||
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</div> | </div> | ||
<div class="interview last"> | <div class="interview last"> | ||
| − | <h5> | + | <h5>Jiajin Li</h5> |
| − | <p class="intro">PhD student | + | <p class="intro">PhD student, University of California, Los Angeles</p> |
<div class="quote paragraph"> | <div class="quote paragraph"> | ||
<p>"You make a great attempt at solving problems like avoiding simply repeating the work that's already been done."</p> | <p>"You make a great attempt at solving problems like avoiding simply repeating the work that's already been done."</p> | ||
<!DOCTYPE html>
S-Din integrated Search Engine and Design Platform developed specifically for Synthetic Biology. With the power of our Algorithm, we provide a one-stop solution to start Your project’
To be better, We don’t have to create more. We choose S-Din Make better project with S-Din.
The science research is now facing an increasingly severe problem that most researches were ignored and not evaluated properly. Numbers of research articles are published every year. How to utilize the previous work to help solve maybe part of the current problem should be considered by everyone.
This year, SYSU-Software try to change this situation in Synthetic Biology, exploring the treasures buried in the ocean of projects.
To make full use of the works done by predecessors in Synthetic Biology. We divided it into two problems.
Follow the standardization in Synthetic Biology, we standardize the previous projects, no matter came from articles or iGEM projects, into a New data format to display you a better result when using our search engine.
Network analysis: designed to uncover the potential connection between projects, help user located exactly what they need.
Customization:The most exciting part of S-Din is that the search and the design are seamless. Once you find a project that might be useful, you can start editing in design platform as soon as you find it. Or you can just simply create one from scratch.
Extensible: To ensure the database can be updated, new information can be added by man or ‘Spider’.
People complain, solving obstacles in project is easy, but deciding what to do is tolerance. Catching your muse is like grab fading smoke.
We made S-Din further not only satisfying the need of starting a project but choosing a project.
Therefore, the tree of words and interaction analysis are developed to inspire users. Part of the reason we made search and design seamlessly is to enable you to start design once you catch your muse.
S-Din is mimetic, sounds exactly when you come up with a good idea. And here comes S-Din, to inspire you, to do more in Synthetic Biology.
DONT BE LIMITED, unleash your imagination.
Want to solve Energy Crisis with Carbon dioxide? Search for circuits that can take in Carbon Dioxide and try to combine a energy generated circuit together to create your own circuits.
Undoubtedly, Google is the most powerful search engine in the world. Let’s see what happen when Google meets S-Din.
Words from designer: Science research are facing a problem of how to utilising the previous researches, some of them are ignored, some of them are underestimated. We‘ve always wanted to make some difference in Synthetic Biology. With the help of our search engine, our users are able to view previous projects and works at a different angle and will be inspired by it. The gap between searching and designing are now seamless. Once you catch the muse, your design can be finished instantly.
This is a search engine built specifically for Synthetic Biology that allow you to view previous projects and works in a standardized style. While your search, we provide network analysis services to help find your results faster and inspire you. Once you find your preferred project, you are allowed to edit it simultaneously in our design platform. More parts can be added by the search tools provided on the left or you can just drag in a circuits that you collected in your favourite before.
The design can be uploaded, downloaded or shared for collaborations between different accounts. Once you finish the design, you can run simulation program to check if it works well and then export it as plasmid and we will generate the sequences for synthesis.
We collect data from many channels, most of the project data and all of the Parts data come from the iGEM Projects published on article are collected on various synthetic biology Journals, usage permissions are granted by the publication groups.
Most of the data are collected by ‘Spider’ and corrected manually. All data of circuits are converted with manually with the help of our design platform.
The users of our software are all innovative researchers on Synthetic Biology, who are interested in many different biological fields. The purpose of our system is to recommend most related genetic parts to the users based on the research interest the users offer to our system.
The information that we use to make recommendation is a database built by NLP and Random Walk which contains scores between each key word and each genetic part. Formally, it is a matrix , where is the number of key words and is the number of parts . The element at row, column is the score between the key word and genetic part , which reflects the connection between them(higher score means higher connection).
The overall strategy of our system is Collaborative Filtering , i.e. we first search the similar key words in our database of the unknown word offered by users and then recommend the genetic parts which are highly related to key words that we found to the users.
A natural question is how to find the most similar key words in our dataset of a given unknown word in an accurate and efficient way? Here is our general solution : To quantify the semantic similarities between words accurately ,we use technique in Deep Learning to convert words into numerical vectors and the cosine similarities between each vectors can represents the semantic similarities between words. To search the similar key words efficiently, we use the KD Tree Algorithm, a fast algorithm based on binary tree, to implement the K Nearest Neighbors strategy.
In scientific journal, keywords are often required in published articles. Keywords can be used for indexing or searching, and offer faster way for readers to know a project main work and find their interests.
As matter of fact, in 2015, iGEM try to collect some keywords (https://2015.igem.org/Keywords) by asking teams to submit.
This year, a new data format is created and keywords enable our network analysis. We first used Microsoft Text Analytics Service (https://azure.microsoft.com/en-us/services/cognitive-services/text-analytics/) to extract keywords from project wiki text in every. We only focus on the most frequent used keywords. By using machine learning, those keywords were converted to word vectors base on a semantic database. A network profiling relationship among keywords, projects and parts data is built and stored in the S-Din database. When searching something, related information will be provided to users base on the network analysis, which would spark a brand new idea expectedly.
When you click interaction analysis on a part on the design platform, any other parts or protein that might interact with the chosen part will be displayed which can be added on the platform. We build an interaction database ourselves, the sources of data come from iGEM Registry and STRING Database. Scores for parts come from the STRING Database.
The users of S-Din are all active and innovative researchers in Synthetic Biology , who come up with many interesting and new ideas in a wide range of fields. Our software help them achieve their creative goals by allowing them to combine various genetic parts together to form brand-new genetic circuits that may never exist before. To help the researchers have a deeper understanding of how the genetic system that they created works , we construct an ODE system to simulate the dynamic behaviors of the genetic system mathematically. The main challenge of simulation is the uncertainty of genetic circuits since the users can construct them arbitrarily. Therefore we build a rather general model capable of simulating various genetic circuits.
After your Design, you can choose to convert your circuits in to Plasmids (Customizable). We will generate the sequence for you to synthesis your own circuits.
The moment you’re inspired by one of the projects, you can edit it instantly. Combining different function parts together to fulfil your need. Or you can do a de novo design.
We developed a series of algorithms to generate network composites of project, parts and keywords for analysis aiming to help user explore the treasure buried in the ocean of articles.
The needs in Designing a Circuits were considered seriously to achieve the best user experience. Simple operation logic, beautiful UI.
Simply by clicking the words, you will see the subordinated words. After few repeats, you will finally get the projects that you want. This function is designed to help specify your need, help you understand your idea better.
This function is implanted in the design platform which will tell you the potential interaction parts with the chosen one. Use your imagination, think of how to utilize these interactions.
Even though it’s difficult to develop a model that fit all the circuits, it worth a shoot. Our modeler developed a general model to allow user to simulate any circuits.
S-Din is a web application. For back-end, we choose Django with Python 3, which is fast, strong, robust and friendly to developers; MySQL with Django models binding give us fantastic database support; algorithms including Word2Vec, K-D Tree are also coded in Python 3 with the famous SciPy. For front-end, we build the view with customized Semantic UI, and control the logic behind beautiful widgets using jQuery. Some other open-source JavaScript libraries are also used to build the site, such as Chart.js for chart, jsPlumb for links in the design page, etc.. Advanced TypeScript was used to build the interesting game BioLab Rescue, hope you will enjoy it!
In our software design, we want to help biobuilder design genetic circuit seamlessly and simply. After we finish it, we carried out a validation of this workflow. This wet lab validation tested the efficiency and reliability of S-Din, and also the experiment data can be used to validate the simulation part in our software.
Nowadays, due to the thinning of the ozone layer, the potential risk of skin cancer and cataract is growing. Our journey with S-Din begin with a curiosity of how to detect UV. But we don’t have a clear idea and don’t know where to start. Luckily, S-Din can light up one's path.
1. Set up your interested research field: After log in, we sat our interest. Because we were curious about UV detection, so we chose DETECTION
2. Browse on S-Din: When surfing on S-Din, we found that UV detection is a not a hot area.
3. Search the previous project: Standing on the shoulders of giants, we wondered predecessors will give us some ideas. We searched for ‘UV detection’ . With data analysis and 29.8 score, we found ETH_Zurich 2012, and marked it.
4. Intelligent recommendation: Based on the interaction database, S-Din recommended us to use a device of Colombia 2014. After browsed its information, we decided to take the advice of S-Din.
5. Edit the circuit: We deleted extra part of ETH_Zurich, because we just decided to use the UV sensor.The surveillance system shows that the safety level of our circuit is low risk. (Upper right corner)
6. The plasmid design: After finished our design, we click on the bottom , then the design drawing of our plasmid came out. So we had a system when detect UV will produce AmilCP, a kind of blue protein.
After we constructed two plasmids, Escherichia coli DH5αwas transformed with plasmids and positive clones were identified by colony polymerase chain reaction (PCR) and restriction endonuclease digestion. Photo.1 and 2 shows the confirmation result. Further confirmations were finished by sequencing.
Photo 1. After cultivated for about 18 hours, the positive clone turned into blue.
Photo 2. The Colony PCR showed the result of Transformation.
We got the models in our software. Picture.1 and 2 show the result.
Picture 1. Our software’s simulation of AmilCP
After the sequencing confirmation, Escherichia coli strain BL21 (DE3) was transformed with plasmids. We didn’t have Liquid culture systems with UV light, so we used plate cultivation. We placed the dish on a 10-15W weak intensity UV lamp at a distance of about 30 cm, and got samples every few hours. 22 hours later, we got the blue bacteria moss (Photo.3 ). The bacteria was been broken by ultrasonic crusher, and then we use the OD588 to measure the concentration of the blue protein (Photo. 4).
Photo 3. One of the plates after exposed in UV for 22 hours.
Photo 4. The mixture of bacteria after broken by ultrasonic crusher.
But unfortunately, collecting bacteria from plate may cause some system error, and because of this, reproducibility of OD588 was not so good. Using it to validate our simulation is meaningless. Hence, we decided to collaborate with SCUT-China_A and used their project's data to prove the accuracy of our simulation. Picture.2 and 3 show the simulation result and the experimental result of their project.
Picture. 2 The simulation result of our software.
Picture. 3 The experiment result, data from SCUT-China_A.
Picture. 4 Our software simulation shows preferable performance.
Compared with experimental data of SCUT-China_A, our software simulation shows preferable performance (Picture.4). The result proves that our simulation works. After 7 hours, the experiment data is higher than predicted value, maybe the reason is about bacterial reproduction.
we successfully installed S-Din on Windows and Linux system.
We recommend users to follow the installation tutor on out github repository
| Basic Requirements | Highly Recommended | |
| Storage | 20G | 20G SSD |
| Memory | 4G | 6G |
| CPU | Intel I5-2500k or better |
Windows 10 Enterprise edition with 8GB RAM and Intel(R) Core(TM) i7-4720HQ CPU @ 2.60GHz, 64-bit operating system
The installation on Windows system is relatively easy. You just need to click initial.bat and runserver.bat and it will finish soon as the images
GNOME ver 3.26.1 with 15.6G RAM and Intel Core i7-4720HQ CPU @ 2.60GHZ x8, 64-bit operating system
After you follow our installation tutor and successfully install S-Din, you can open our software in browser.
In this part，we will illustrate how to operate S-Din to search Synthetic biology projects and desin a circuit.
If you want to use the design function, you should register for an account firstly, but if you just want to search something, you don't need to sign in.
Search for projects, you can type in some keywords in search bar, and we will recommend some projects according to the keywords.
When we show you the search results, you can choose one you are insterested in, and then will see details about this project. You can see what this work has done and may get inspiration from that circuit and the decription for this work.
Click ”Design“ to switch to desgin function, here you can start you design.
You can search for part and add them to the design paper, and you can see the description for the parts you search to help you make a good choice.
If you don't know what is the best match between two parts, we privoide parts interaction information to help you make a choice.
After add parts to the design pages, you can add relationship between them.
When you finish your work, we provide a simulation function to help you check your work whether it is good or not.
Finally you can download your work to your PC!
The best user experience makes the best software, even though we've done enough preparation and investigation before initiation of this project. User study is also important because help us adjust the details in our software to make it more user-friendly.
It's pity that we can't expand the scale of our user study due to the long development period and lack of coders. But we're happy that they offer their precious time to help us test out software and give us positive feedbacks.
research assistent who help us on our wet-lab validation
study in molecular mechanism of interaction between pathogen and host.
"It's considerable that you add safety surveillance system in your design platform. I wonder how many High-risk parts are there in your database? Yeah, may not many, but this is still necessary right?"
"From a researcher's point of view, I would say that the software still needs to improve to be more fit in the need of expertise. Though the current version is good enough."
Ph.D. Student of bioinformatics , Columbia University
"The engagement of search and design is designed in an elegant way, I have to say I like this design"
"The design platform is fluent and easy to use indeed but I think it can be more professional or more biology? There are lots of processes need to reflect on a circuit of pathway sketch."
"Beautiful UI, how much money your team pay for the designer?"
PhD student, University of California, Los Angeles
"You make a great attempt at solving problems like avoiding simply repeating the work that's already been done."
"There are things you need to consider like the updating of your database and how to make others use your software. Well done but keep on!"
Improvement of the efficiency of our algorithms.
Extend the scale of our database.
Better user experience for users in Synthetic Biology.
And more
