Probabilistic approaches to optimize synthetic organisms

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Synthetic biology is based on the “forward engineering” of biological systems: from a toolbox of well-defined genetic components, the basic building blocks are used to create combinations not present in nature. Synthetic biology is increasingly used when the synthesis of a complex molecule is either prohibitively expensive or impossible to create in the laboratory or factory. The design of novel or re-engineered enzymatic pathways into microbes promises a cost effective alternative. However this remains challenging even within genetically tractable organisms such as baker's yeast Saccharomyces cerevisiae. The resultant systems are often functional but far from optimal.Systems Biology is based on the “reverse engineering” of biological systems. Here high-throughput -omic technologies (eg next generation sequencing) are typically used to profile a large number of samples of a target organism. Often the goal is to infer from these profiles the underlying biological machinery. The use of high-throughput profiling and bioinformatic analysis together provide a more holistic molecular perspective of the state of an organism by detailing the state of individual genes, pathways and processes.This project will build systems biology models to aid in the creation of synthetic organisms. Probabilistic models are used to build an “intermediary” between genotype (genetic perturbations to induce a trait) and phenotype (expression of the target trait). The so-called transcriptional signature network (TSN) allows us to reason about the global state of the synthetic organism as we iteratively perturb it to increase efficacy. The ability to consider the molecular state of the system at each design step will allow us to shift seamlessly between forward and reverse engineering. We show proof of concept for our approach by optimizing an existing synthetic yeast system that produces benzylisoquinoline alkaloids, a class of plant secondary metabolites that includes antitussive, antibacterial, and antineoplastic drugs. Yields of the current system are too low for practical applications, a common situation in synthetic biology. A semi-rational directed evolutionary approach is used with our TSN to systematically perturb the synthetic system and improve alkaloid yields. The outcomes of this highly interdisciplinary research will have many benefits to Canada. Our short term goals to optimize existing microbial factories for biofuels and drugs could have positive commercial impact. Moreover, these unique, complicated compounds are of interest in research (eg alternative opioids for pain). The longer term intellectual challenge is to build predictive models that will make microbial factory construction routine and automated. This could open entire new research and industrial opportunities.