As an experimenter, have you ever confused about getting no result when all experimental conditions are well enough? Or have you ever been tired of repeating the experiment over and over again without knowing whether your pay is futile? Or have you ever dreamed of choosing an imported gene by global regulation instead of only direct interaction? If yes, take a gNAP before wearing lab gloves!

gNAP, genetic Network Analyze and Predict, is a software analyze the infection of exogenous gene to engineered bacteria’s Genetic Regulatory Network(GRN) and introduce a predicted imported gene satisfying your needs!

To reach the best user experience, we designed four succinct User Interfaces on which you just need to click some buttons and all the questions above will be solved.

Starter

“Starter” has only one button which could lead users to input all information needed for the next simulation and get ready to start. It is an easy-to-use interface guiding users to complete the operation according to the prompts. The exogenous transcription unit’s promoter and gene sequence shall be put into gNAP in this part. If not, users could not use analyze function, yet predict module can function normally. All the database files will be integrated in this part and genetic information will be stored in an external file, “all_info”, eventually. The original GRN is built in “Starter” with a matrix of regulation in file “old_GRN”.

Now, it’s time to start taking a gNAP!

Monitor

This shows the function of forward analysis.

This shows the function of reverse analysis.

"Monitor" is one of the main module of our software as a monitor keeping watch on running. The command lines in "Monitor" intuitively shows the detail in the process of software running. There are two main functions in this part. One of which is analysis function that simulate the infection of new gene's importing before and the simple evaluation of new GRN. The new GRN matrix is output into a file called “new_GRN”. This function may cost about 3 minutes, so taking a nap will be a good choice. The procession running and command lines' showing are synchronous. Prediction function is another one which can be operated with adding the desired target expression instead of inputting the exogenous gene sequence. The running time of this part is determined by the precision and the number of iteration time in prediction algorithms. "Monitor" is very easy to operate through some simple action buttons and intelligent gene selection mode.

The result of this monitor can be found in "Result" and "Display".

Result

"Result" uses simply words to show the results of all functions of gNAP intuitively. There are two parts in the interface showing different results about the modules of analyze and predict.

In the part of analysis, the stability has been scored into five degrees and users can choose a specific gene showing its information. All the information can generate an SBOL file as well as searching genetic information in RegulonDB. In the other part of prediction, target expression and predicted interactions are shown in a table. Users could pick out the significant regulation as a reference of genetic choice.

The most succinct words have been used in this part to give users the most intuitive and clearest results. The graphs and vivid network structure will be put into "Display" with JAVA's rich GUI library.

Display

“Display” is the data visualization part of our software. To reach a better impression, it had been written in JAVA language. There are three parts in “Display” module: show regulation, show gene change and show network. The prediction of interaction between new gene and original genes can be seen in “show regulation” part with both regulated and regulating. “Show gene change” part shows the changing of all genes with the time changing. What’s more, users could see the interactions of whole network in “show network” part. Dragging, choosing, picking out the genes in GUI is available. When choosing one gene in a giant network, all genes who have interactions with that one will be picked out and shown in a small network.

Code

All source code of our software have been pushed up to Github and there is a help document generated by Doxygen based on our notations in the command lines. You can download the PDF file [here] which contains the introductions about all Classes and Functions used in our software.

The command line source files are written in C++ language and visualization part are written in Java language. Both of them can be complied across platforms. The GUI source files are also written in C++ language with Qt Creator, it can also be compiled across various platforms using Qt 5.1.0, which can be downloaded here.

Summary

In general, most mathematical models were used for analyze the experimental results. GNA also analyze the GRN but just focus on regulation in original network. However, our software focuses on the infection of imported gene which is quite usual in synthetic biology instead of analyze the GRN only. In the same time, gNAP uses various algorithms and methotheir lab gloves will be really useful to researchers and save them a lot of time.