Utilizing genome-scale models to optimize nutrient supply for sustained algal growth and lipid productivity

Abstract

Nutrient availability is critical for growth of algae and other microbes used for generating valuable biochemical products. Determining the optimal levels of nutrient supplies to cultures can eliminate feeding of excess nutrients, lowering production costs and reducing nutrient pollution into the environment. With the advent of omics and bioinformatics methods, it is now possible to construct genome-scale models that accurately describe the metabolism of microorganisms. In this study, a genome-scale model of the green alga Chlorella vulgaris (iCZ946) was applied to predict feeding of multiple nutrients, including nitrate and glucose, under both autotrophic and heterotrophic conditions. The objective function was changed from optimizing growth to instead minimizing nitrate and glucose uptake rates, enabling predictions of feed rates for these nutrients. The metabolic model control (MMC) algorithm was validated for autotrophic growth, saving 18% nitrate while sustaining algal growth. Additionally, we obtained similar growth profiles by simultaneously controlling glucose and nitrate supplies under heterotrophic conditions for both high and low levels of glucose and nitrate. Finally, the nitrate supply was controlled in order to retain protein and chlorophyll synthesis, albeit at a lower rate, under nitrogen-limiting conditions. This model-driven cultivation strategy doubled the total volumetric yield of biomass, increased fatty acid methyl ester (FAME) yield by 61%, and enhanced lutein yield nearly 3 fold compared to nitrogen starvation. This study introduces a control methodology that integrates omics data and genome-scale models in order to optimize nutrient supplies based on the metabolic state of algal cells in different nutrient environments. This approach could transform bioprocessing control into a systems biology-based paradigm suitable for a wide range of species in order to limit nutrient inputs, reduce processing costs, and optimize biomanufacturing for the next generation of desirable biotechnology products.

Integration of physiologically relevant photosynthetic energy flows into whole genome models of light-driven metabolism

“…Computational tools are integral to these workflows. Relevant to the design step of the process, GEMs have been extensively used to rationally engineer metabolism to generate a wide variety of phenotypes (Czajka et al, 2021;Bang et al, 2020;Li et al, 2019). We explored implementing the modeling framework towards light-driven bioproduct formation, as adoption of these approaches is underrepresented in phototrophic systems.…”

A genome‐scale nutrient minimization forecast algorithm for controlling essential amino acid levels in CHO cell cultures

“…On the other hand, the two-point extrapolation approach exhibits the greatest flexibility to handle various kinds of growth behaviors because, unlike the equation fitting methods, its predictions are not predicated on the cells exhibiting any kind of defined growth patterns. Indeed, the two-point extrapolation forecast approach has been implemented successfully with nutrient minimization models by our group to control nitrate level for Chlorella vulgaris cultures (Li et al, 2019). Such approaches based on two consecutive points are often used to estimate specific rates due to their simplicity, but such methods can be overly sensitive to measurement error and may lead to high variability when used to predict CHO cell culture (Goudar et al, 2005;Goudar, 2012).…”

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“…GSMs are commonly used for evaluating knockout strategies 36 or media components 37 to optimize yield of a metabolite of interest. AdvanceSyn Toolkit is built on top of 38 which is a proxy for growth rate, or fluxes when one or more media component(s) or enzyme expression(s) were changed.…”