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

Broddrick, Jared T.; Ware, Maxwell A.; Jallet, Denis; Palsson, Bernhard Ø.; Peers, Graham

doi:10.1111/tpj.15965

Cited by 8 publications

(2 citation statements)

References 80 publications

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“…These models may incorporate information from single‐cell transcriptomics analysis, which are often more readily available than metabolic data. Cell‐type‐specific models can then be incorporated into multi‐scale models (Broddrick et al., 2022; Chew et al., 2016; Smith et al., 2019), contributing to better understand the functioning and plasticity of the plant system, with clear implications for systems‐based breeding and metabolic engineering (Amthor et al., 2019; Zhu, Lynch, et al., 2016). Because these models are underdetermined, they produce predictions of metabolic network function under different conditions, making FBA and MFA complementary approaches.…”

Section: Methods For Studying Cell‐type‐specific Metabolismmentioning

confidence: 99%

Cell‐type‐specific metabolism in plants

et al. 2023

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SUMMARY Every plant organ contains tens of different cell types, each with a specialized function. These functions are intrinsically associated with specific metabolic flux distributions that permit the synthesis of the ATP, reducing equivalents and biosynthetic precursors demanded by the cell. Investigating such cell‐type‐specific metabolism is complicated by the mosaic of different cells within each tissue combined with the relative scarcity of certain types. However, techniques for the isolation of specific cells, their analysis in situ by microscopy, or modeling of their function in silico have permitted insight into cell‐type‐specific metabolism. In this review we present some of the methods used in the analysis of cell‐type‐specific metabolism before describing what we know about metabolism in several cell types that have been studied in depth; (i) leaf source and sink cells; (ii) glandular trichomes that are capable of rapid synthesis of specialized metabolites; (iii) guard cells that must accumulate large quantities of the osmolytes needed for stomatal opening; (iv) cells of seeds involved in storage of reserves; and (v) the mesophyll and bundle sheath cells of C4 plants that participate in a CO2 concentrating cycle. Metabolism is discussed in terms of its principal features, connection to cell function and what factors affect the flux distribution. Demand for precursors and energy, availability of substrates and suppression of deleterious processes are identified as key factors in shaping cell‐type‐specific metabolism.

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Section: Methods For Studying Cell‐type‐specific Metabolismmentioning

confidence: 99%

Cell‐type‐specific metabolism in plants

et al. 2023

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“…Light absorption, photodamage, and the unique redox metabolism associated with photosynthesis are key aspects in a computational description of photoautotrophic growth. Compared to heterotrophic growth, only few studies provide a quantitative computational analysis of light-limited phototrophic growth and integrate physiologically relevant photosynthetic properties into large-scale models of light driven metabolism [23].…”

Section: Introductionmentioning

confidence: 99%

A quantitative description of light-limited cyanobacterial growth using flux balance analysis

Höper,

Komkova,

Zavřel

et al. 2024

Preprint

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The metabolism of phototrophic cyanobacterial is an integral part of global biogeochemical cycles, and the capability of cyanobacteria to assimilate atmospheric CO2into organic carbon has manifold potential applications for a sustainable biotechnology. To elucidate the properties of cyanobacterial metabolism and growth, computational reconstructions of the genome-scale metabolic networks play an increasingly important role. Here, we present an updated reconstruction of the metabolic network of the cyanobacteriumSynechocystissp. PCC 6803 and its analysis using flux balance analysis (FBA). To overcome limitations of conventional FBA, and to allow for the integration of quantitative experimental analyses, we develop a novel approach to describe light absorption and light utilization. Our approach incorporates photoinhibition and a variable quantum yield into the constraint-based description of light-limited phototrophic growth. We show that the resulting model is capable to predict quantitative properties of cyanobacterial growth, including photosynthetic oxygen evolution and the ATP/NADPH ratio required for growth and cellular maintenance. Our approach retains the computational and conceptual simplicity of FBA and is readily applicable to other phototropic microorganisms.

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Integration of proteomic data with genome‐scale metabolic models: A methodological overview

Zare,

Fleming

2024

Protein Science

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The integration of proteomics data with constraint‐based reconstruction and analysis (COBRA) models plays a pivotal role in understanding the relationship between genotype and phenotype and bridges the gap between genome‐level phenomena and functional adaptations. Integrating a generic genome‐scale model with information on proteins enables generation of a context‐specific metabolic model which improves the accuracy of model prediction. This review explores methodologies for incorporating proteomics data into genome‐scale models. Available methods are grouped into four distinct categories based on their approach to integrate proteomics data and their depth of modeling. Within each category section various methods are introduced in chronological order of publication demonstrating the progress of this field. Furthermore, challenges and potential solutions to further progress are outlined, including the limited availability of appropriate in vitro data, experimental enzyme turnover rates, and the trade‐off between model accuracy, computational tractability, and data scarcity. In conclusion, methods employing simpler approaches demand fewer kinetic and omics data, consequently leading to a less complex mathematical problem and reduced computational expenses. On the other hand, approaches that delve deeper into cellular mechanisms and aim to create detailed mathematical models necessitate more extensive kinetic and omics data, resulting in a more complex and computationally demanding problem. However, in some cases, this increased cost can be justified by the potential for more precise predictions.

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

Cited by 8 publications

References 80 publications

Cell‐type‐specific metabolism in plants

Cell‐type‐specific metabolism in plants

A quantitative description of light-limited cyanobacterial growth using flux balance analysis

Integration of proteomic data with genome‐scale metabolic models: A methodological overview

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