Mapping Genotype to Phenotype using Attribute Grammar
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Over the past 10 years, several synthetic biology research groups have proposed tools and domain-specific languages to help with the design of artificial DNA molecules. Community standards for exchanging data between these tools, such as the Synthetic Biology Open Language (SBOL), have been developed. It is increasingly important to be able to perform in silico simulation before the time and cost consuming wet lab realization of the constructs, which, as technology advances, also become in themselves more complex. By extending the concept of describing genetic expression as a language, we propose to model relations between genotype and phenotype using formal language theory. We use attribute grammars (AGs) to extract context-dependent information from genetic constructs and compile them into mathematical models, possibly giving clues about their phenotypes. They may be used as a backbone for biological Domain-Specific Languages (DSLs) and we developed a methodology to design these AG based DSLs. We gave examples of languages in the field of synthetic biology to model genetic regulatory networks with Ordinary Differential Equations (ODEs) based on various rate laws or with discrete boolean network models. We implemented a demonstration of these concepts in GenoCAD, a Computer Assisted Design (CAD) software for synthetic biology. GenoCAD guides users from design to simulation. Users can either design constructs with the attribute grammars provided or define their own project-specific languages. Outputting the mathematical model of a genetic construct is performed by DNA compilation based on the attribute grammar specified; the design of new languages by users necessitated the generation on-the-fly of such attribute grammar based DNA compilers. We also considered the impact of our research and its potential dual-use issues. Indeed, after the design exploration is performed in silico, the next logical step is to synthesize the designed construct's DNA molecule to build the construct in vivo. We implemented an algorithm to identify sequences of concern of any length that are specific to Select Agents and Toxins, helping to ensure safer use of our methods.
- Doctoral Dissertations