Difference between revisions of "Team:SJTU-Software/project"

Introduction
Our software, BASE, has four functions: search, recommendation, evaluation and upload. Via search function, users can search for parts or devices using IDs or features as keywords. In recommendation interface, users can draw their devices. They can also give some keywords when drag an icon to the chain to get a list of parts which fit the require and other parts best. When using evaluation, users firstly enter a device that they designed, then our software can give a score and advice for each part to improve their devices. Finally, users can upload their device to BASE’s database. For the first three functions, we develop a set of scoring system to evaluate the effectiveness and ease of use of the parts and devices. And the last 3 functions provide a whole set of helping system for device design.

Method
We get the data of bricks from IGEM part registry. A total of 28,637 biobricks are recorded in the database. Then we divide them into two groups, one for parts and the other for devices, according to whether the biobrick has subparts. Among them, 14,744 are parts and 13,893 are devices. For each biobrick, there’re four different websites:
http://parts.igem.org/cgi/xml/part.cgi?part=BBa_B0034
http://parts.igem.org/cgi/partsdb/part_info.cgi?part_name=BBa_B0034
http://parts.igem.org/partsdb/get_part.cgi?part=BBa_B0034 
http://parts.igem.org/Part:BBa_B0034:Experience
When collecting data, we simply replace the ID “B0034” with other bricks’ ID.
We then extract information from the websites. The information includes Com_id, Author, Enter_time, Ctype, Part_status, Sample_status, Part_results, Star_rating, Uses, DNA_status, Qualitative_experience, Group_favorite, Del, Groups, Confirmed_times, Number_comments, Ave_rating, Des. And we take 12 of the above factors into account when we're scoring biobricks.
As for optimizing the weight of these factors, we firstly analyze the distribution of value of the factors to choose the factors that can distinguish the parts most effectively. Then we select 40 parts and 40 devices as the training sets. Finally we get the weight by combining results of several methods. The optimized weight is set as the default weight.

Results
1.Scores for different values of factors
To build a scoring system, we start at giving scores to the values of these factors. With the help of wet lab researchers, we rank the values of discrete type according to their effect on researches, and choose a relatively good method to transform successive values into values between 0 and 1. For discrete values, we have a scoring table as below.

And the optimized weight of the factors are shown in the table below.
Table2:weight for each factor

2.Devices scoring method with relationship between parts
This method is mainly used in the evaluation function. For a device which is just designed by users, the score we get through the first method actually mean nothing, as there’re no information for the device on the registry. So we need to develop a new evaluation system based on its composing parts and relationships between the parts.
When evaluating the relationships between parts, we take several factors into consideration, such as the frequency and the average score when the parts are used together and so on.
Firstly the weight of the two aspects is confirmed. The default ratio is 65% for the parts and 35% for the relationships. In the first aspect, given that wet lab researchers care more about the outcome of a device, the weight of coding types is 80% while that of others is 20%. But in the second aspect, all relationships between coding parts and others share the same weight.
Since there’re two scoring system for devices, the weight in the second one is adjusted to make the scores made by different method close so that scores for new devices can have the comparability with those already in the database.
3.Adding parts one by one
This method is mainly used in recommendation function. It’s similar to the second one, but it only cares about the new adding part and relationships when doing the recommendations. The weight’s also adjusted to fit the other two method.

Reference

Morgan Madec, Yves Gendrault, Christophe Lallement, Member, IEEE, Jacques Haiech. A game-of-life like simulator for design-oriented modeling of BioBricks in synthetic biology, 34th Annual International Conference of the IEEE EMBS, San Diego, California USA, 28 August - 1 September, 2012

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John M. Walker, Wilfried Weber, Martin Fussenegger, Methods in Molecular Biology: Synthetic+Gene+Networks, Humana Press, 2012