AUTOMATED DESIGN AND OPTIMIZATION OF SYNTHETIC GENETIC SYSTEMS

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Bacteria are living, chemical computers. By assembling together different genetic parts (refs), such as promoters, ribosome binding sites, and protein coding sequences, into a DNA molecule of a specific sequence, genetic programs are constructed that confer many useful functions to a bacterium, including the ability to manufacture biofuels and drugs from renewable sugars (refs). In the field of synthetic biology, a central goal is obtaining the ability to design genetic programs in a predictable fashion (refs). Currently, such genetic programs are constructed and tested using time-consuming trial-and-error techniques, such as random mutagenesis. We are developing the methodologies to design synthetic genetic systems in a predictive and systematic way. The methods combine biophysical models of genetic part function, DNA sequence optimization techniques, and design principles for genetic systems to convert a target biological behavior into a specific DNA sequence. We are also creating a user-friendly web-based interface where members of the genetic and metabolic engineering communities can specify a target biological function and receive the DNA sequence of a genetic program that carries out that function. We request 100 000 SUs to pursue the following research goals: (i) improve our recently developed design method for synthetic ribosome binding site by expanding it towards the optimization of entire protein coding sequences;(ii) allow users to request optimization jobs on their protein coding sequences of interest and off-load this computation onto TeraGrid resources;(iii) solve the RBS Minimax problem for a 22 enzyme metabolic network to maximize production of a chemical precursor to a biofuel. The computational resources are divided as: 40 000 SUs on Roaming TeraGrid and 60 000 SUs on Abe, Ranger, and/or Steele systems.