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Improving growth of Cupriavidus necator H16 on formate using adaptive laboratory evolution-informed engineering

Conversion of CO 2 to value-added products presents an opportunity to reduce GHG emissions while generating revenue. Formate, which can be generated by the electrochemical reduction of CO 2 , has been proposed as a promising intermediate compound for microbial upgrading. Here we present progress towards improving the soil bacterium Cupriavidus necator H16, which is capable of growing on formate as its sole source of carbon and energy using the Calvin-Benson-Bassham (CBB) cycle, as a host for formate utilization. Using adaptive laboratory evolution, we generated several isolates that exhibited faster growth rates on formate. The genomes of these isolates were sequenced, and resulting mutations were systematically reintroduced by metabolic engineering, to identify those that improved growth. Further, the metabolic impact of several mutations was investigated further using RNA-seq transcriptomics. We found that deletion of a transcriptional regulator implicated in quorum sensing, PhcA, reduced expression of several operons and led to improved growth on formate. Growth was also improved by deleting large genomic regions present on the extrachromosomal megaplasmid pHG1, particularly two hydrogenase operons and the megaplasmid CBB operon, one of two copies present in the genome. Based on these findings, we generated a rationally engineered ..delta..phcA and megaplasmid-deficient strain that exhibited a 24% faster maximum growth rate on formate. Moreover, this strain achieved a 7% growth rate improvement on succinate and a 19% increase on fructose, demonstrating the broad utility of microbial genome reduction. This strain has the potential to serve as an improved microbial chassis for biological conversion of formate to value-added products.

59 BASIC BIOLOGICAL SCIENCES↗

Unifying Model-Based and Reactive Programming within a Model-Based Executive

Real-time, model-based, deduction has recently emerged as a vital component in AI's tool box for developing highly autonomous reactive systems. Yet one of the current hurdles towards developing model-based reactive systems is the number of methods simultaneously employed, and their corresponding melange of programming and modeling languages. This paper offers an important step towards unification. We introduce RMPL, a rich modeling language that combines probabilistic, constraint-based modeling with reactive programming constructs, while offering a simple semantics in terms of hidden state Markov processes. We introduce probabilistic, hierarchical constraint automata (PHCA), which allow Markov processes to be expressed in a compact representation that preserves the modularity of RMPL programs. Finally, a model-based executive, called Reactive Burton is described that exploits this compact encoding to perform efficIent simulation, belief state update and control sequence generation.

Williams, Brian C.↗