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Team:Peking R/Project
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| - | <p class="notbookmaintitle">Project Description</p> | + | <p class="notbookmaintitle" align=center>Project Description</p> |
<p>In synthetic biology there are basically two approaches for genetic program fine-tuning: hardcoding and softcoding. Hardcoding refers to coding functions and parameters in the fixed formats and values. For example, mutagenesis at promoters or ribosomal binding sites (RBS) generates large number of mutants, each of which, however, is fixed with its performance. Therefore, laborious screening and assaying for object function is needed. The opposite is softcoding, which refers to designs that allow for the customization of performance of genetic programs via external input, such as inducers, without having to edit the DNA sequence case by case.</p> | <p>In synthetic biology there are basically two approaches for genetic program fine-tuning: hardcoding and softcoding. Hardcoding refers to coding functions and parameters in the fixed formats and values. For example, mutagenesis at promoters or ribosomal binding sites (RBS) generates large number of mutants, each of which, however, is fixed with its performance. Therefore, laborious screening and assaying for object function is needed. The opposite is softcoding, which refers to designs that allow for the customization of performance of genetic programs via external input, such as inducers, without having to edit the DNA sequence case by case.</p> | ||
| - | <p>In our project, we are aiming at establishing an extensible and versatile platform for the softcoding of genetic program in bacteria, composed of a toolbox and a methodology | + | <p>In our project, we are aiming at establishing an extensible and versatile platform for the softcoding of genetic program in bacteria, composed of a toolbox and a methodology. The toolbox consists of interoperable and truly modular ligand-responsive riboswitches/ribozymes, while the methodology is automated design of synthetic ribosome binding sites (RBS) with customized translation rate. When combining them together, a quantitative correlation between the concentration of specific ligand and synthetic RBS strength can be established. Therefore, when tuning genetic program, customized RBS strength at multiple sites can be high-throughputly achieved without having to conduct laborious mutagenesis and characterization, followed by easily determining the configuration of RBS(s) strength. Then RBS sequences that meet this configuration will be automatically designed via computer algorithms. </p> |
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Project Description
In synthetic biology there are basically two approaches for genetic program fine-tuning: hardcoding and softcoding. Hardcoding refers to coding functions and parameters in the fixed formats and values. For example, mutagenesis at promoters or ribosomal binding sites (RBS) generates large number of mutants, each of which, however, is fixed with its performance. Therefore, laborious screening and assaying for object function is needed. The opposite is softcoding, which refers to designs that allow for the customization of performance of genetic programs via external input, such as inducers, without having to edit the DNA sequence case by case.
In our project, we are aiming at establishing an extensible and versatile platform for the softcoding of genetic program in bacteria, composed of a toolbox and a methodology. The toolbox consists of interoperable and truly modular ligand-responsive riboswitches/ribozymes, while the methodology is automated design of synthetic ribosome binding sites (RBS) with customized translation rate. When combining them together, a quantitative correlation between the concentration of specific ligand and synthetic RBS strength can be established. Therefore, when tuning genetic program, customized RBS strength at multiple sites can be high-throughputly achieved without having to conduct laborious mutagenesis and characterization, followed by easily determining the configuration of RBS(s) strength. Then RBS sequences that meet this configuration will be automatically designed via computer algorithms.
