Quantifying the Successes and Failures of Evolution to Further Synthetic Biology

Michael Sismour

The goal of synthetic biology is to build new biological systems from the ground up, yielding new forms of life and non-living chemical systems that recapitulate some of the properties of life such as self-replication and evolution. This bottom up approach-synthesis-promises to produce organisms that help mankind and provides a new way to study biology. Biology has a natural method for synthesis and engineering: evolution. Processes such as mutation, lateral gene transfer, and gene duplication create (synthesize) new forms of life while natural selection allows nature to pick those new forms that are best suited for a particular function. Directed evolution and in vitro selection offer a powerful way to mimic nature's engineering tool in the laboratory. However, laboratory evolution and in vitro selection remains a challenge because the intricacies of these processes are not well known. In addition, by using these "synthesis tools" one does not gain much knowledge through the process of synthesis itself because only the successful organisms or chemical systems are observed and studied at the end of the process. The ability to quantitatively distinguish among those new life forms that work well, those that work less well, and those that do not work at all allows the process of synthesis to serve as a learning tool; patterns can be recognized that uncover biological design principles. Synthetic and physical organic chemistry used this principle to better understand the reactivity and structure of organic molecules, and we are now applying this powerful technique to study biology. By better quantification of the spectrum from failed to successful biological systems, we can begin to uncover these underlying principles allowing the development of more robust methods to evolve useful "living tools" and a greater understanding of biological systems.

We present here a method to better understand in vitro selection and directed evolution on a molecular level. Our method couples multiplexed DNA sequencing, in vitro selection, and phenotypic measurements to study not only the optimal molecules, but to quantify all molecules that can perform a desired function. We use low-level enrichment for in vitro selections of DNA aptamers and DNAzymes (catalytic DNA molecules) and ultra high-throughput screening methods to link both phenotype and genotype of approximately one billion sequences. To correlate genotype with phenotype, a next-generation DNA sequencer is used to sequence the DNA and to measure kinetic rates for DNAzymes and binding constants for DNA aptamers. Rather than exploring molecules obtained at the end of a selection, molecules are examined after only a few rounds of enrichment, allowing the identification of "smart" aptamers and DNAzymes with tuned physical properties. This method maps the sequence space of these molecules allowing investigation of how different parameters affect the outcome of a selection or evolution, thus yielding valuable information to future efforts to evolve biological systems. Future work includes the development of methods to release and capture desired beads from the screening instrument, adapt the instrument for massively-paralleled biophysics studies, and the application of the above technique to characterize functional RNA molecules, peptides, and small molecules.