Metabolic pathway engineering involves the design and optimization of gene circuits for diverse functions, such as for the production of high-value chemicals, biofuels, pharmaceuticals, and sustainable foods. To iteratively improve the function of a metabolic pathway, synthetic biologists typically go through the design, build, and test cycle several rounds to optimize each and single part of the gene circuit.
Two types of optimization strategies are generally used. One classic method is sequential optimization, where major bottlenecks are identified in the initial pathway design and conquered individually, thus gradually optimizes pathway performance. However, regulatory networks are often complex and interact with each other in unpredictable ways. Therefore, combinatorial optimization, where multiply parts of a pathway are synergistically tested and optimized, has been gaining more favor in recent years. In combinatorial optimization, multiple elements are varied simultaneously which allows for the systematic screening of the multidimensional space.
Compared to the traditional sequential debugging method, combinatorial approach clearly spans a more complete design space and is able to identify a global optimum, which may not be accessible by sequential optimization. Another major difference between sequential and combinatorial optimization is that the combinatorial method requires for the testing of hundreds and thousands constructs in parallel, whereas we usually only manipulate one genetic part and test less than ten constructs at a time in sequential optimization. Therefore, having an efficient DNA assembly platform for building combinatorial DNA libraries for downstream screening is essential for combinatorial optimization.
Each bottleneck is diversified and tested individually
Tests less than 10 constructs at a time
Test one part at a time
Time-consuming and costly
Synergistically test and optimize all variable parts in the pathway design
Covers a more complete design space and is able to identify the global optimum
Tests thousands of constructs in parallel
Test multiple parts simultaneously
Efficient and cost-effective
There are many commonly used method for multi-fragment DNA assembly, each with their own advantages and intrinsic limitations. For example, Golden Gate assembly is based on Type IIS restriction enzyme cutting and ligation. It is very efficient to assemble more than five fragments at a time, however, this assembly method has sequence limitations and can not assemble fragments containing any relevant enzyme cutting site. On the other hand, common homology based cloning methods using in vitro homologous recombination, have no sequence limitations, but the assembly efficiency drops significantly when assembling more than five fragments. In addition, this cloning method is expensive and low throughput. To facilitate the assembly of combinatorial libraries for metabolic pathway optimization, a high throughput platform that is able to perform hundreds, or even thousands, of assemblies in parallel with no sequence limitation would be ideal.
GenScript's proprietary GenBuilder™ DNA assembly platform is able to assemble up to 12 parts in one round and allows for the addition of any enzyme cutting sites needed for your downstream application. Build upon this seamless assembly platform, GenScript now offers combinatorial DNA library services that can help speed up the build phase of your iterative metabolic pathway and microbial strain design cycle. This platform is able to assemble up to 108 constructs in one library design, with up to 4 variable regions that you can test for. With more than 15 years of experience in gene synthesis, GenScript can build inserts up to 15 kb in length using the GenBuilder™ assembly platform. If you are working in metabolic pathway and microbial strain engineering, GenScript's combinatorial DNA library is the idea tool you would need for the combinatorial optimization of your design.
