Component Assembly Optimization Based On Adaptive Ant Colony Algorithm

Synthetic biology is an important branch of life science in the 21st century.It combines physics,engineering,chemistry and computer science.And the primary purpose of synthetic biology is to design production of metabolites which is less or even doesn't exist in the nature to meet people's needs,however,biologists in synthetic assembly of the biological system or device just rely on the past experience to try to do biological experiments which often needs several years leading to the synthesis of biological system or device error probability increasing,and time-consuming,laborious,costly.This makes it difficult for biologists to study the synthesis of complex genetic components.Ant colony algorithm,widely used in computer science,is good at solving combinatorial optimization problem.After summarizing the relevant knowledge of synthetic biology at home and abroad,a new solution was proposed.The work of this thesis includes the following points:(1)Analyze the most widely and latest feature data from iGEM website,and fix inconsistent data in the dataset.By analyzing the internal feature of these data,typical feature data can be imported into software GenoCAD.According to the specific requirements of the project,a series of grammar rules in software GenoCAD can be designed.(2)The frequency of parts and pairs of parts is obtained from the feature data,and then a mathematical model is built according to the relationship between biological parts,and the improved ant colony algorithm is used to solve it.In addition,the designed algorithm system can be used by professionals or non-professionals,or it can be packaged as a system and transplanted to the computer platform.(3)The current ant colony algorithm with good robustness and stable performance and basic ant colony algorithm are compared with the improved algorithm in this thesis for robot path planning.In this paper,it is convenient to build biological system by combining statistical language model and improved ant system.The comparative analysis shows that our model is efficient.