Identification of line-specific strategies for improving carotenoid production in synthetic maize through data-driven mathematical modeling

Plant synthetic biology is still in its infancy. However, synthetic biology approaches have been used to manipulate and improve the nutritional and health value of staple food crops such as rice, potato and maize. With current technologies, production yields of the synthetic nutrients are a result of trial and error, and systematic rational strategies to optimize those yields are still lacking. Here, we present a workflow that combines gene expression and quantitative metabolomics with mathematical modeling to identify strategies for increasing production yields of nutritionally important carotenoids in the seed endosperm synthesized through alternative biosynthetic pathways in synthetic lines of white maize, which is normally devoid of carotenoids. Quantitative metabolomics and gene expression data are used to create and fit parameters of mathematical models that are specific to four independent maize lines. Sensitivity analysis and simulation of each model is used to predict which gene activities should be further engineered in order to increase production yields for carotenoid accumulation in each line. Some of these predictions (e.g. increasing Zmlycb/Gllycb will increase accumulated β-carotenes) are valid across the four maize lines and consistent with experimental observations in other systems. Other predictions are line specific. The workflow is adaptable to any other biological system for which appropriate quantitative information is available. Furthermore, we validate some of the predictions using experimental data from additional synthetic maize lines for which no models were developed.

This work was partially funded by grants BFU2010-17704 from the Spanish MINECO and from small grants CMB and TR255 from the University of Lleida to RA, TIN2011-28689-C02-02 and TIN2014-53234-C2-2-R to FS, BIO2011-23324, BIO02011-22525 and PIM2010PKB-0074 for MINECO to PC and CFZ, a European Research Council IDEAS Advanced Grant Program (BIOFORCE) (to PC), ERC-2013-PoC 619161 (to PC) and RecerCaixa (to PC). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.