Photosynthetic microorganisms have the potential for sustainable production of chemical feedstocks and products but have had limited success due to a lack of tools and deeper understanding of metabolic pathway regulation. The application of instationary metabolic flux analysis (INST-MFA) to photosynthetic microorganisms has allowed researchers to quantify fluxes and identify bottlenecks and metabolic inefficiencies to improve strain performance or gain insight into cellular physiology. Additionally, flux measurements can also highlight deviations between measured and predicted fluxes, revealing weaknesses in metabolic models and highlighting areas where a lack of understanding still exists. In this review, we outline the experimental steps necessary to successfully perform photosynthetic flux experiments and analysis. We also discuss the challenges unique to photosynthetic microorganisms and how to account for them, including: light supply, quenching, concentration, extraction, analysis, and flux calculation. We hope that this will enable a larger number of researchers to successfully apply isotope assisted metabolic flux analysis (13C-MFA) to their favorite photosynthetic organism.
We have developed the first transient metabolic model for diurnal growth of algae based on experimental data and capable of predicting phenotype from genotype. This model enables us to evaluate the impact of genetic and environmental changes on the growth, biomass composition and intracellular fluxes of the model green alga,
Chlamydomonas reinhardtii
. The availability of this model will enable faster and more efficient design of cells for production of fuels, chemicals, and pharmaceuticals.
Algal cells experience strong circadian rhythms under diurnal light, with regular changes in both biomass composition and transcriptomic environment. However, most metabolic models - critical tools for bioengineering organisms - assume a steady state. The conflict between these assumptions and the reality of the cellular environment make such models inappropriate for algal cells, creating a significant obstacle in engineering cells that are viable under natural light. By transforming a set of discreet transcriptomic measurements from synchronized Chlamydomonas cells grown in a 12/12 diel light regime into continuous curves, we produced a complete representation of the cell's transcriptome that can be interrogated at any arbitrary timepoint. We clustered these curves, in order to find genes that were expressed in similar patterns, and then also used it to build a metabolic model that can accumulate and catabolize different biomass components over the course of a day. This model predicts qualitative phenotypical outcomes for the sta6 mutant, including excess lipid accumulation and a failure to thrive when grown diurnally in minimal media, representing a qualitative prediction of phenotype from genotype even under dynamic conditions. We also extended this approach to simulate all single-knockout mutants with genes represented in the model and identified potential targets for rational engineering efforts.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.