The efficiency of genetic engineering can be hindered by myriad possibilities of genetic structures and complicated details. Our team, SYSU-Software aim to use computer algorithms to exploit the existing massive data and reduce redundancy in the engineering procedure. Therefore, we create Maloadis, an integrated automated genetic circuit design platform. Maloadis implement automated top-down design with GeneNet algorithm, and is capable of designing and rating possible genetic circuits according to users’ requirements. It also exploits the abundant information provided by genetic circuit images by extracting parts and structures from them to search for related previous work through trained neuro network. To improve success rate in wet-lab experiment, Maloadis predicts gene expression level with integrated models, and offers suggestions to shorten experiment cycle using Bayesian Optimization algorithm. We present Maloadis as a de novo approach to facilitate synthetic biology design automation.
Have trouble successfully designing a genetic circuit?
Automatically work out possible genetic circuits according to user's target gene expression demand
Rate the possible genetic circuits according to their chance of success
Search images by extracting part's and structural information from genetic circuit images
Simulate genetic circuit expression
Improve wet-lab results by providing suggestions on parameter optimization
Who Am I?
From target gene expression function to genetic structure
Recognize and match information in circuit images
Less experiments, better results
One click simulation of gene circuits constructed by users