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At least 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Spatiotemporal 4D Whole-cell Modeling of a Minimal Autotroph Reveals Central Carbon Metabolism Regulated Locally by Protein Megacomplexes via Post-translational Modifications under Light Disturbance

Photosynthetic microorganisms rely on multiple pathways in central carbon metabolism to adapt to fluctuating light and energy availability across diel cycles. Mechanistic insight into the regulatory dynamics of this adaptation requires integrating processes spanning disparate timescales, from rapid redox-dependent post-translational modifications (PTMs) to slower changes in protein expression and metabolic pathway usage. To address this complexity beyond genome-based inference and traditional modeling, we develop a whole-cell four-dimensional (3D + time) model of the marine cyanobacterium Prochlorococcus marinus MED4 that explicitly represents the spatial organization of enzymatic and molecular processes in central carbon metabolism under light perturbation. We employ a perturbation-based research design to experimentally generate time-series, multi-omics measurements that provide molecular descriptors and cryo-ET derived 3D segmented volumes as constraints for this dynamic 4D framework. The integration of experiments and modeling across defined light regimes enables quantitative validation of system-level responses and forecasting under distinct light disturbances. We test the hypothesis that light-dependent redox PTMs regulating the structural assembly of a protein megacomplex, the “dark complex,” modulate metabolic flux at a conserved regulatory node of the Calvin–Benson cycle (CBC) in cyanobacteria. Our model shows that subcellular spatial organization buffers rapid light-induced changes in thylakoid reaction rates, which are followed by redox-PTM-mediated sequestration or release of CBC enzymes in the dark complex, ultimately impacting carbon fixation dynamics within carboxysomes. Comparison with an equivalently parameterized well-mixed stochastic model demonstrates that post-translational regulation not only buffers transcriptional noise and diffusion-driven fluctuations but also stabilizes phenotypic outcomes, underscoring the importance of spatial heterogeneity in phenotypic robustness. This ability to probe adaptive, spatiotemporally resolved mechanisms in photosynthetic machinery and central carbon metabolism addresses a critical gap in genotype-to-phenotype inference and expands modeling and design capabilities for understudied or genetically intractable autotrophs such as P. marinus MED4.

Johnson, Connah G.↗
DOE OSTI2020

Optogenetics and biosensors set the stage for metabolic cybergenetics

We report cybergenetic systems use computer interfaces to enable feed-back controls over biological processes in real time. The complex and dynamic nature of cellular metabolism makes cybergenetics attractive for controlling engineered metabolic pathways in microbial fermentations. Cybergenetics would not only create new avenues of research into cellular metabolism, it would also enable unprecedented strategies for pathway optimization and bioreactor operation and automation. Implementation of metabolic cybergenetics, however, will require new capabilities from actuators, biosensors, and control algorithms. The recent application of optogenetics in metabolic engineering, the expanding role of genetically encoded biosensors in strain development, and continued progress in control algorithms for biological processes suggest that this technology will become available in the not so distant future.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Building kinetic models for metabolic engineering

Kinetic formalisms of metabolism link metabolic fluxes to enzyme levels, metabolite concentrations and their allosteric regulatory interactions. Though they require the identification of physiologically relevant values for numerous parameters, kinetic formalisms uniquely establish a mechanistic link across heterogeneous omics datasets and provide an overarching vantage point to effectively inform metabolic engineering strategies. Advances in computational power, gene annotation coverage, and formalism standardization have led to significant progress over the past few years. However, careful interpretation of model predictions, limited metabolic flux datasets, and assessment of parameter sensitivity remain as challenges. In this study we highlight fundamental considerations which influence model quality and prediction, advances in methodologies, and success stories of deploying kinetic models to guide metabolic engineering.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Learning from nature’s plant engineers: Hijacking metabolism and development beyond genetics

Plant metabolism underpins the food, fiber, and fuel that support our economy, driving strong interest in new strategies to rewire plant metabolism for emerging applications. While most synthetic biology efforts are reliant on genetic engineering, plants can be manipulated in many other ways that remain comparatively underexplored. Across nature, diverse organisms, including bacteria, fungi, and insects, have evolved sophisticated mechanisms to exploit plant metabolic richness, reshaping it for purposes that span from basic nutrition to the construction of complex, novel structures for shelters. These interspecies interactions and non-model systems represent unique manners in which plants can be reprogrammed or hijacked by other organisms, offering inspiration for novel approaches to engineering plant metabolism. By better understanding the basis of how organisms induce these remarkable transformations in plants, we can expand the conceptual boundaries of synthetic biology and reveal alternative routes to manipulating plants for the production of a diverse array of valuable compounds and materials. Deeper insight into these mechanisms will yield novel blueprints for rethinking the scope and breadth in which we can redesign plant metabolism across many applications.

Sarkiss, Anthony E. [University of California, Ber↗
DOE OSTI2021

Continuous NF-κB pathway inhibition promotes expansion of human phenotypical hematopoietic stem/progenitor cells through metabolism regulation

Highlights: • Small molecular inhibition of NF-κB pathway efficiently expands human HSPCs. • NF-κB inhibition with TPCA-1 improves the self-renewal potential and biological function maintenance of expanded CD34{sup +} cells. • NF-κB pathway inhibition reprograms metabolism and enhances glycolysis. • Continuous NF-κB inhibition is required in the culture process to facilitate primitive HSC maintenance. Hematopoietic stem/progenitor cells (HSPCs) ex vivo expansion is critical in facilitating their widespread clinical application. NF-κB pathway is implicated in the energy homeostasis and metabolic adaptation. To explore the effect of NF-κB pathway on the ex vivo HSPC expansion and metabolism, the 50 nM–1 μM inhibitor of NF-κB pathway TPCA-1 was used to expand cord blood derived CD34{sup +} cells in serum-free culture. The expansion folds, function, mitochondrial profile and metabolism of HSPCs were determined. After 10 days of culture with 100 nM TPCA-1, the expansion of total cells CD34{sup +}CD38{sup −} cells and CD34{sup +}CD38{sup −}CD45RA{sup −}CD90{sup +}CD49f{sup +} cells were significantly increased compared to the cytokine priming alone. Notably, TPCA-1 treatment generated ~ 2-fold greater percentage of CD34{sup +}EPCR{sup +} and CD34{sup +}CD38{sup −}CD45RA{sup −}CD90{sup +}CD49f{sup +} cells compared to cytokine only conditions. Moreover, TPCA-1 expanded CD34{sup +} cells displayed enhanced serial colonies forming potential and secondary expansion capability. NF-κB inhibition increased the expression of self-renewal related genes, while downregulated the expression of mitochondrial biogenesis regulator (Pgc1α) and mitochondrial chaperones and proteases (ClpP, Hsp10, Hsp60). Mitochondrial mass and membrane potential were markedly decreased with TPCA-1 treatment, leading to the reduced mitochondrial reactive oxygen species (ROS) level in HSPCs. NF-κB inhibition displayed augmented glycolysis rate with compromising mitochondrial metabolism. This study demonstrated that NF-κB pathway inhibition improved glycolysis and limited ROS production that promoted the ex vivo expansion and maintenance of functional HSPCs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Linking Internal Dosimetries of the Propyl Metabolic Series in Rats and Humans Using Physiologically Based Pharmacokinetic (PBPK) Modeling

The metabolic series approach has successfully linked internal dosimetries of metabolically related compounds reducing cost and time for chemical risk assessments. Here, we developed a physiologically based pharmacokinetic (PBPK) model in rats and humans for the propyl metabolic series including propyl acetate, 1-propanol, propionaldehyde, and propionic acid. Manufacturers use these compounds as organic solvents and intermediates during chemical synthesis. Public exposures can occur through using consumer products containing propyl compounds like cosmetics, aerosol sprays, or foods. To develop the PBPK model, we measured in vitro metabolism of propyl acetate in blood and liver S9 fractions. We measured concentrations of propyl compounds in blood following intravenous (iv) infusion of 13C-propanol or 13C-propionic acid and closed chamber inhalation exposures to propyl acetate or propanol in rats. Using these studies and other published data, we modified an existing PBPK model for the butyl metabolic series to simulate time course concentrations of propyl compounds in rats and humans. Consistent with measured in vitro and in vivo data, the optimized propyl series model predicts rapid clearance of propyl acetate, higher concentrations of propanol in blood from propyl acetate inhalation compared to propanol inhalation, and low concentrations of propionic acid in blood from exposures to propyl acetate or propanol. Regulators can use this model as a tool for propyl compound risk assessment by linking internal dosimetries under various exposure scenarios.

Smith, Jordan N.↗
DOE OSTI2025

Deciphering metabolic differentiation during Bacillus subtilis sporulation

Abstract The bacteriumBacillus subtilisundergoes asymmetric cell division during sporulation, producing a mother cell and a smaller forespore connected by the SpoIIQ-SpoIIIA (or Q-A) channel. The two cells differentiate metabolically, and the forespore becomes dependent on the mother cell for essential building blocks. Here, we investigate the metabolic interactions between mother cell and forespore using genome-scale metabolic and expression models as well as experiments. Our results indicate that nucleotides are synthesized in the mother cell and transported in the form of nucleoside di- or tri-phosphates to the forespore via the Q-A channel. However, if the Q-A channel is inactivated later in sporulation, then glycolytic enzymes can form an ATP and NADH shuttle, providing the forespore with energy and reducing power. Our integrated in silico and in vivo approach sheds light into the intricate metabolic interactions underlying cell differentiation inB. subtilis, and provides a foundation for future studies of metabolic differentiation.

Science & Technology - Other Topics↗
DOE OSTI2025

A constraint-based framework for exploring the impact of multireaction dependencies on metabolic functions

Abstract Metabolism operates under physico-chemical constraints that result in multireaction dependencies. Understanding how multireaction dependencies affect metabolic phenotypes remains challenging, hindering their biotechnological applications. Here, we propose the concept of a forcedly balanced complex that allows to efficiently determine the effects of specific multireaction dependencies on metabolic network functions in constrained-based models. Using this concept, we found that the fraction of multireaction dependencies induced by forcedly balanced complexes in genome-scale metabolic networks followed power law with exponential cut-off. We identified forcedly balanced complexes that are lethal in cancer but have little effect on growth in healthy tissue models. In addition, these forcedly balanced complexes are largely specific to models of particular cancer types. Therefore, multireaction dependencies resulting from forced balancing of complexes represent an innovative means to control cancers that, we argue, can be implemented via transporter engineering. The presented constraint-based approaches pave the way for using multireaction dependencies in metabolic engineering for diverse biotechnological applications.

Küken, Anika↗
DOE OSTI2024

Quantitative principles of microbial metabolism shared across scales

Metabolism is the complex network of chemical reactions occurring within every cell and organism, maintaining life, mediating ecosystem processes and affecting Earth’s climate. Experiments and models of microbial metabolism often focus on one specific scale, overlooking the connectivity between molecules, cells and ecosystems. Here we highlight quantitative metabolic principles that exhibit commonalities across scales, which we argue could help to achieve an integrated perspective on microbial life. Mass, electron and energy balance provide quantitative constraints on their flow within metabolic networks, organisms and ecosystems, shaping how each responds to its environment. The mechanisms underlying these flows, such as enzyme–substrate interactions, often involve encounter and handling stages that are represented by equations similar to those for cells and resources, or predators and prey. Here we propose that these formal similarities reflect shared principles and discuss how their investigation through experiments and models may contribute to a common language for studying microbial metabolism across scales.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Insights into the unique carboxylation reactions in the metabolism of propylene and acetone

Alkenes and ketones are two classes of ubiquitous, toxic organic compounds in natural environments produced in several biological and anthropogenic processes. In spite of their toxicity, these compounds are utilized as primary carbon and energy sources or are generated as intermediate metabolites in the metabolism of other compounds by many diverse bacteria. The aerobic metabolism of some of the smallest and most volatile of these compounds (propylene, acetone, isopropanol) involves novel carboxylation reactions resulting in a common product acetoacetate. Propylene is metabolized in a four-step pathway involving five enzymes where the penultimate step is a carboxylation reaction catalyzed by a unique disulfide oxidoreductase that couples reductive cleavage of a thioether linkage with carboxylation to produce acetoacetate. The carboxylation of isopropanol begins with conversion to acetone via an alcohol dehydrogenase. Acetone is converted to acetoacetate in a single step by an acetone carboxylase which couples the hydrolysis of MgATP to the activation of both acetone and bicarbonate, generating highly reactive intermediates that are condensed into acetoacetate at a Mn2+ containing the active site. Acetoacetate is then utilized in central metabolism where it is readily converted to acetyl-coenzyme A and subsequently converted into biomass or utilized in energy metabolism via the tricarboxylic acid cycle. This review summarizes recent structural and biochemical findings that have contributed significant insights into the mechanism of these two unique carboxylating enzymes.

Biochemistry & Molecular Biology↗
DOE OSTI2025

Glyceraldehyde-3-phosphate dehydrogenase homologs as bifunctional gatekeepers of metabolic segregation in Pseudomonas putida

Metabolically versatile Pseudomonas species can assimilate various glycolytic and gluconeogenic substrates. Simultaneous assimilation is known to segregate carbons from each substrate type into different metabolic pathways. However, the mechanisms of this metabolic segregation remain unresolved. Here, we investigate Pseudomonas putida KT2440 during processing of the sugar glucose through glycolysis versus the phenolic acid ferulate through gluconeogenesis. Metabolome profiling reveals up to twofold less tricarboxylic acid cycle metabolites but up to 10-fold higher metabolites of upper glycolysis, pentose-phosphate, and Entner–Doudoroff pathways in glucose-grown cells compared to ferulate-grown cells. After 13C-substrate switching, kinetic isotopic profiling captures rapid assimilation of new substrate carbons into initial catabolic pathways, but incorporation into downstream pathways is absent or incomplete. Proteomics identifies a 22-fold higher abundance of one homolog of glyceraldehyde-3-phosphate dehydrogenase (GAPDH, GapA) in cells fed on glucose relative to ferulate, while abundance of another homolog (GapB) remains unchanged. Growth phenotypes and quantitative metabolomics for single and double knockout mutants of these GAPDH homologs indicate only GapA involvement in glycolytic flux, which can be compensated by the Entner–Doudoroff pathway, and distinct preference of GapB with minimal role of GapA for gluconeogenic flux. Accordingly, growth of triple knockout mutant with deletion of gapA, gapB, and edd is possible only when glycolytic and gluconeogenic substrates are provided together to meet metabolic demands in a segregated fashion, but metabolic tradeoffs lead to slow growth. A mathematical, experimentally constrained, model of the GAPDH node shows that tuning of GapA and GapB concentrations enables transition between flux regimes for nutritional adaptability.

Zhou, Nanqing [Northwestern University, Evanston]↗
DOE OSTI2023

Accurate flux predictions using tissue-specific gene expression in plant metabolic modeling

The accurate prediction of complex phenotypes such as metabolic fluxes in living systems is a grand challenge for systems biology and central to efficiently identifying biotechnological interventions that can address pressing industrial needs. The application of gene expression data to improve the accuracy of metabolic flux predictions using mechanistic modeling methods such as flux balance analysis (FBA) has not been previously demonstrated in multi-tissue systems, despite their biotechnological importance. We hypothesized that a method for generating metabolic flux predictions informed by relative expression levels between tissues would improve prediction accuracy. Relative gene expression levels derived from multiple transcriptomic and proteomic datasets were integrated into FBA predictions of a multi-tissue, diel model of Arabidopsis thaliana’s central metabolism. This integration dramatically improved the agreement of flux predictions with experimentally based flux maps from 13 C metabolic flux analysis compared with a standard parsimonious FBA approach. Disagreement between FBA predictions and MFA flux maps was measured using weighted averaged percent error values, and for parsimonious FBA this was 169%–180% for high light conditions and 94%–103% for low light conditions, depending on the gene expression dataset used. This fell to 10%-13% and 9%-11% upon incorporating expression data into the modeling process, which also substantially altered the predicted carbon and energy economy of the plant.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Evolutionary trajectory of transcription factors and selection of targets for metabolic engineering

Transcription factors (TFs) provide potentially powerful tools for plant metabolic engineering as they often control multiple genes in a metabolic pathway. However, selecting the best TF for a particular pathway has been challenging, and the selection often relies significantly on phylogenetic relationships. Here, we offer examples where evolutionary relationships have facilitated the selection of the suitable TFs, alongside situations where such relationships are misleading from the perspective of metabolic engineering. We argue that the evolutionary trajectory of a particular TF might be a better indicator than protein sequence homology alone in helping decide the best targets for plant metabolic engineering efforts. This article is part of the theme issue ‘The evolution of plant metabolism’.

Life Sciences & Biomedicine - Other Topics↗
DOE OSTI2020

Arabidopsis SnRK1 negatively regulates phenylpropanoid metabolism via Kelch domain-containing F-box proteins

Phenylpropanoid metabolism represents a substantial metabolic sink for the photosynthetically fixed carbon. The evolutionarily conserved Sucrose Non-Fermenting Related Kinase 1 (SnRK1) is a major metabolic sensor that reprograms metabolism upon carbon deprivation. However, it is not clear if and how SnRK1-mediated sugar signaling pathway controls phenylpropanoid metabolism. Here, we show that Arabidopsis SnRK1 negatively regulates phenylpropanoid biosynthesis via a group of Kelch domain-containing F-Box (KFB) proteins that are responsible for the ubiquitination and degradation of phenylalanine ammonia lyase (PAL). Down-regulation of AtSnRK1 significantly promoted the accumulation of soluble phenolics and lignin polymers and drastically increased PAL cellular accumulation but only slightly altered its transcription level. Co-expression of SnRK1α with PAL in Nicotiana benthamiana leaves resulted in the severe attenuation of the latter's protein level, but protein interaction assays suggested PAL is not a direct substrate of SnRK1. Furthermore, up- or down-regulation of AtSnRK1 positively affected KFB PALs gene expression, and energy starvation upregulated KFB PAL expression, which partially depends on AtSnRK1. Collectively, our study reveals that SnRK1 negatively regulates phenylpropanoid biosynthesis, and KFB PALs act as regulatory components of the SnRK1 signaling network, transcriptionally regulated by SnRK1 and subsequently mediating proteasomal degradation of PAL in response to the cellular carbon availability.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Synergy and antagonism in a genome-scale model of metabolic hijacking by bacteriophages

Bacteriophage auxiliary metabolic genes (AMGs) alter host metabolism by hijacking reactions, but previous studies mostly inferred their roles from annotations, ignoring system-wide impacts and phage production. Here we integrate AMGs and phage assembly into a genome-scale metabolic model of Prochloroccocus marinus MED4 infected by P-HM2. We show that 17 directly hijacked reactions substantially affect more than 30% of the reactions in MED4 metabolism, including carbon fixation, photosynthesis, and nucleotide synthesis, distinguishing these AMGs as either phage aligned—shifting feasible reaction velocities in accordance with maximal phage production—or phage antialigned. Pareto optimization reveals that phage-aligned reactions alter phage-host growth trade-offs, while phage-antialigned reactions do not. We experimentally validate our predictions of system-level AMG impacts by measuring the N-dependent effect of P-HM2 cp12 expression on growth in a model relative of the genetically intractable MED4, Synechococcus elongatus. We also show that AMGs’ indirect impacts are synergistically and antagonistically coupled, providing systems-level insight into AMG perturbations and highlighting how nontrivial cascading effects shape host metabolism.

Rozum, Jordan C. [Pacific Northwest National Labor↗
DOE OSTI2026

Multi-omic characterization of a soil microbial consortium reveals critical role of succinate and glutamate metabolism during calcium carbonate precipitation

Microbially induced calcium carbonate precipitation (MICP) holds potential for use in soil stabilization and carbon sequestration, with the overall efficiency of the process being a major determinant for use in many environmental and civil engineering applications. While the biogeochemical pathways and enzymes driving MICP are known, the microbial metabolic networks and community dynamics underlying such precipitation remain poorly characterized. To address this gap, we developed a four-member consortium of soil bacteria (Curtobacterium flaccumfaciens, Rhodococcus qingshengii, Microbacterium sp., and Bacillus toyonensis), termed carbon storing consortium - A (CSC-A), that is capable of MICP. Prior work shows that MICP production is higher in CSC-A compared to the sum of carbonate produced by each member, suggesting carbonate production is driven by consortium dynamics. To that end we used a multi-omic integration approach of genomics, transcriptomics, and metabolomics to investigate potential inter-species interactions that may influence the MICP phenotype. Genomic life history characterizations identified evidence of niche specialization by B. toyonensis and Microbacterium, while metatranscriptomic analysis suggests R. qingshengii is a keystone species during growth in urea. By comparing individual species’ metabolomes to the metabolic profile of a shared well of precipitated metabolites, we identified over 200 metabolites predicted to be produced or consumed by CSC-A members. Integrating both data types to search the KEGG reactome highlighted a network centered around glutamine metabolism and branched chain amino acid biosynthesis under regulation during CSC-A growth in urea. Succinate metabolism was also a major node in this network and laboratory assays confirmed that increasing the amount of succinate in the growth medium leads to increased carbonate precipitation by CSC-A, a critical confirmation of our modeling approach. By isolating and identifying the interconnected metabolic components underlying MICP in CSC-A, we identified keystone taxa, metabolites, and pathways important for future optimization of the application of this consortia to carbonate precipitation.

carbon storing consortium - A (CSC-A)↗
DOE OSTI2022

Global patterns of diversity and metabolism of microbial communities in deep-sea hydrothermal vent deposits

When deep-sea hydrothermal fluids mix with cold oxygenated fluids, minerals precipitate out of solution and form hydrothermal deposits. These actively venting deep-sea hydrothermal deposits support a rich diversity of thermophilic microorganisms which are involved in a range of carbon, sulfur, nitrogen, and hydrogen metabolisms. Global patterns of thermophilic microbial diversity in deep-sea hydrothermal ecosystems have illustrated the strong connectivity between geological processes and microbial colonization, but little is known about the genomic diversity and physiological potential of these novel taxa. Here we explore this genomic diversity in 42 metagenomes from four deep-sea hydrothermal vent fields and a deep-sea volcano collected from 2004 to 2018 and document their potential implications in biogeochemical cycles. Our dataset represents 3635 metagenome-assembled genomes encompassing 511 novel and recently identified genera from deep-sea hydrothermal settings. Some of the novel bacterial (107) and archaeal genera (30) that were recently reported from the deep-sea Brothers volcano were also detected at the deep-sea hydrothermal vent fields, while 99 bacterial and 54 archaeal genera were endemic to the deep-sea Brothers volcano deposits. We report some of the first examples of medium- (≥ 50% complete, ≤ 10% contaminated) to high-quality (> 90% complete, < 5% contaminated) MAGs from phyla and families never previously identified, or poorly sampled, from deep-sea hydrothermal environments. We greatly expand the novel diversity of Thermoproteia, Patescibacteria (Candidate Phyla Radiation, CPR), and Chloroflexota found at deep-sea hydrothermal vents and identify a small sampling of two potentially novel phyla, designated JALSQH01 and JALWCF01. Metabolic pathway analysis of metagenomes provides insights into the prevalent carbon, nitrogen, sulfur, and hydrogen metabolic processes across all sites and illustrates sulfur and nitrogen metabolic “handoffs” in community interactions. We confirm that Campylobacteria and Gammaproteobacteria occupy similar ecological guilds but their prevalence in a particular site is driven by shifts in the geochemical environment. Our study of globally distributed hydrothermal vent deposits provides a significant expansion of microbial genomic diversity associated with hydrothermal vent deposits and highlights the metabolic adaptation of taxonomic guilds. Collectively, our results illustrate the importance of comparative biodiversity studies in establishing patterns of shared phylogenetic diversity and physiological ecology, while providing many targets for enrichment and cultivation of novel and endemic taxa.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

A standardized workflow for kinetic metabolic model curation and dissemination

Kinetic metabolic models provide invaluable insights into cellular metabolism, supporting applications in synthetic biology, metabolic engineering, and systems biology. However, reproducibility and utility of these models hinge on clear and rigorous documentation, standardized annotation, and accessible visualization. This paper presents a workflow for building, annotating, visualizing, and sharing kinetic metabolic models. Our method integrates community standards and open-source tools to ensure reproducibility, interoperability, and user accessibility. This procedure enables researchers to produce reusable and well-documented kinetic models, advancing their role as powerful tools in metabolic research.

Cook, Margaret [Univ. of Washington, Seattle, WA (↗