Synthetic biology is a new field in which engineers, biologists, and chemists have come to together to transform genetic engineering into a discipline in which the design and construction of novel biological systems are simplified via engineering foundations such as standards and abstraction, i.e. data formats and modeling at separate yet connected levels of complexity. Recently, we have begun to witness the true potential of synthetic biology, noted here in the form of yeast and bacterial systems that have been genetically engineered to consume toxic waste, kill tumor cells, produce anti-malaria drug precursors, and convert glucose to biofuel. What all of these projects have in common, however, are long turnaround times and significant requirements on investigator expertise. To help meet these challenges, we propose to develop standards and abstraction methods for synthetic biology and leverage them in our freely available genetic design automation (GDA) tool, iBioSim.
In order to enable synthetic biologists to efficiently exchange functional data on the DNA components from which biological systems are constructed, we will contribute to the development of a modeling extension for the nascent Synthetic Biology Open Language (SBOL) standard. To automate the synthesis or mapping of a behavioral specification to DNA components that instantiate the desired behavior, we will adapt technology mapping algorithms from electronic design automation (EDA) to use SBOL and account for the peculiarities of genetic technology, e.g. molecular cross-talk between components. Lastly, in order to enable efficient analysis and verification of newly synthesized system designs, we will develop an automated abstraction methodology for converting from low-level, reaction-based models of biochemistry to smaller reaction-based models and high-level, state-based models. These standards and abstraction methods should ultimately facilitate more efficient design of biological systems of increased complexity and greater utility.