Tonner, P. D. | bioRxiv Science

bioRxiv2020

Improved stability of an engineered function using adapted bacterial strains

Engineering useful functions into cells is one of the primary goals of synthetic biology. However, engineering novel functions that remain stable for multiple generations remains a significant challenge. Here we report the importance of host fitness on the stability of an engineered function. We find that the initial fitness of the host cell affects the stability of the engineered function. We demonstrate that adapting a strain to the intended growth condition increases fitness and in turn improves the stability of the engineered function over hundreds of generations. This approach offers a simple and effective method to increase the stability of engineered functions without genomic modification or additional engineering and will be useful in improving the stability of novel, engineered functions in living cells. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=99 SRC="FIGDIR/small/979385v1_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@dfeebeorg.highwire.dtl.DTLVardef@1b31a24org.highwire.dtl.DTLVardef@1795435org.highwire.dtl.DTLVardef@8775c4_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2019

A Bayesian Non-parametric Mixed-Effects Model of MicrobialPhenotypes

Substantive changes in gene expression, metabolism, and the proteome are manifested in overall changes in microbial population growth. Quantifying how microbes grow is therefore fundamental to areas such as genetics, bioengineering, and food safety. Traditional parametric growth curve models capture the population growth behavior through a set of summarizing parameters. However, estimation of these parameters from data is confounded by random effects such as experimental variability, batch effects or differences in experimental material. A systematic statistical method to identify and correct for such confounding effects in population growth data is not currently available. Further, our previous work has demonstrated that parametric models are insufficient to explain and predict microbial response under non-standard growth conditions. Here we develop a hierarchical Bayesian non-parametric model of population growth that identifies the latent growth behavior and response to perturbation, while simultaneously correcting for random effects in the data. This model enables more accurate estimates of the biological effect of interest, while better accounting for the uncertainty due to technical variation. Additionally, modeling hierarchical variation provides estimates of the relative impact of various confounding effects on measured population growth.

systems biology↗