Search for: All records

Abstract Understanding how photosynthetic organisms including plants and microbes respond to their environment is crucial for optimizing agricultural practices and ensuring food and energy security, particularly in the context of climactic change and sustainability. This perspective embeds back-of-the-envelope calculations across a photosynthetic organism design and scale up workflow. Starting from the whole system level, we provide a recipe to pinpoint key genetic targets, examine the logistics of detailed computational modeling, explore environmentally driven phenotypes, and feasibility as an industrial biofuel production chassis. While complex computer models or high throughput in vivo studies often dominate scientific inquiry, this perspective highlights the power of simple calculations as a valuable tool for initial exploration and evaluating study feasibility. Fermi calculations are defined as quick, approximate estimations made using back-of-the-envelope calculations and straightforward reasoning to achieve order-of-magnitude accuracy, named after the physicist Enrico Fermi. We show how Fermi calculations, based on fundamental principles and readily available data, can offer a first pass understanding of metabolic shifts in plants and microbes in response to environmental and genetic changes. We also discuss how Fermi checks can be embedded in data-driven advanced computing workflows to enable bio-aware machine learning. Lastly, an understanding of state-of-the-art is necessary to guide study feasibility and identifying key levers to maximize cost to return ratios. Combining biology- and resource- aware Fermi calculations, this proposed approach enables researchers to prioritize resource allocation, identify gaps in predictions and experiments, and develop intuition about how observed responses of plants differ between controlled laboratory environments and industrial conditions. 

Abstract The growth and development of maize (Zea mays L.) largely depends on its nutrient uptake through the root. Hence, studying its growth, response, and associated metabolic reprogramming to stress conditions is becoming an important research direction. A genome-scale metabolic model (GSM) for the maize root was developed to study its metabolic reprogramming under nitrogen stress conditions. The model was reconstructed based on the available information from KEGG, UniProt, and MaizeCyc. Transcriptomics data derived from the roots of hydroponically grown maize plants were used to incorporate regulatory constraints in the model and simulate nitrogen-non-limiting (N+) and nitrogen-deficient (N−) condition. Model-predicted flux-sum variability analysis achieved 70% accuracy compared with the experimental change of metabolite levels. In addition to predicting important metabolic reprogramming in central carbon, fatty acid, amino acid, and other secondary metabolism, maize root GSM predicted several metabolites (l-methionine, l-asparagine, l-lysine, cholesterol, and l-pipecolate) playing a regulatory role in the root biomass growth. Furthermore, this study revealed eight phosphatidylcholine and phosphatidylglycerol metabolites which, even though not coupled with biomass production, played a key role in the increased biomass production under N-deficient conditions. Overall, the omics-integrated GSM provides a promising tool to facilitate stress condition analysis for maize root and engineer better stress-tolerant maize genotypes.