The DOI matches its Crossref record; 62 of the 62 references it cites were found in a registry or an index.
Bottom-up Metabolic Reconstruction of Arabidopsis and Its Application to Determining the Metabolic Costs of Enzyme Production
Open accessAbstract
Large-scale modeling of plant metabolism provides the possibility to compare and contrast different cellular and environmental scenarios with the ultimate aim of identifying the components underlying the respective plant behavior. The existing models of Arabidopsis (Arabidopsis thaliana) are top-down assembled, whereby the starting point is the annotated genome, in particular, the metabolic genes. Hence, dead-end metabolites and blocked reactions can arise that are subsequently addressed by using gap-filling algorithms in combination with species-unspecific genes. Here, we present a bottom-up-assembled, large-scale model that relies solely on Arabidopsis-specific annotations and results in the inclusion of only manually curated reactions. While the existing models are largely condition unspecific by employing a single biomass reaction, we provide three biomass compositions that pertain to realistic and frequently examined scenarios: carbon-limiting, nitrogen-limiting, and optimal growth conditions. The comparative analysis indicates that the proposed Arabidopsis core model exhibits comparable efficiency in carbon utilization and flexibility to the existing network alternatives. Moreover, the model is utilized to quantify the energy demand of amino acid and enzyme de novo synthesis in photoautotrophic growth conditions. Illustrated by the case of the most abundant protein in the world, Rubisco, we determine its synthesis cost in terms of ATP requirements. This, in turn, allows us to explore the tradeoff between protein synthesis and growth in Arabidopsis. Altogether, the model provides a solid basis for completely species-specific integration of high-throughput data, such as gene expression levels, and for condition-specific investigations of in silico metabolic engineering strategies.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“A number of studies have applied FBA to C3 leaf metabolism (de Oliveira Dal’Molin et al., 2010, 2016; Poolman et al., 2013, 2014; Arnold and Nikoloski, 2014; Cheung et al., 2014, 2015; Lakshmanan et al., 2015)”
From Leaf Energy Balance Requires Mitochondrial Respiration and Export of Chloroplast NADPH in the Light (2019) · cites this paper as Arnold and Nikoloski, 2014
Citations
Cites
KEGG: Kyoto Encyclopedia of Genes and Genomes
Ogata, H., Goto, S., Sato, K., Fujibuchi, W., Bono, H., & Kanehisa, M. (1999) · Nucleic Acids Research · doi:10.1093/nar/27.1.29 · Cited by 31,232
Thimm, O., Bläsing, O. E., Gibon, Y., Nagel, A., Meyer, S., Krüger, P., Selbig, J., Müller, L. A., Rhee, S. Y., & Stitt, M. (2004) · The Plant Journal · doi:10.1111/j.1365-313x.2004.02016.x · Cited by 3,356
The Arabidopsis Information Resource (TAIR): improved gene annotation and new tools
Lamesch, P., Berardini, T., Li, D., Swarbreck, D., Wilks, C., Sasidharan, R., Müller, R., Dreher, K. A., Alexander, D. L., Garcia-Hernandez, M., et al. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkr1090 · Cited by 2,378
In Vivo Half-Life of a Protein Is a Function of Its Amino-Terminal Residue
Bachmair, A., Finley, D. J., & Varshavsky, A. J. (1986) · Science · doi:10.1126/science.3018930 · Cited by 1,976
A protocol for generating a high-quality genome-scale metabolic reconstruction
Thiele, I., & Palsson, B. Ø. (2010) · Nature Protocols · doi:10.1038/nprot.2009.203 · Cited by 1,789
Activities at the Universal Protein Resource (UniProt)
The UniProt Consortium (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt1140 · Cited by 1,736
The use of metabolomics to dissect plant responses to abiotic stresses
Obata, T., & Fernie, A. R. (2012) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-012-1091-5 · Cited by 912
Constraining the metabolic genotype–phenotype relationship using a phylogeny of in silico methods
Lewis, N. E., Nagarajan, H., & Palsson, B. O. (2012) · Nature Reviews Microbiology · doi:10.1038/nrmicro2737 · Cited by 779
Tcherkez, G. G. B., Farquhar, G. D., & Andrews, T. J. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0600605103 · Cited by 777
Akashi, H. D., & Gojobori, T. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.062526999 · Cited by 687
Flagged first, then by citations
Cited by
Sorted by citations
Items 101–126 of 126
Flux-sum coupling analysis of metabolic network models
Seyis, M., Razaghi‐Moghadam, Z., & Nikoloski, Z. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012972 · Cited by 3
Self-citationA Bayesian model for genomic prediction using metabolic networks
Onogi, A. (2023) · Bioinformatics Advances · doi:10.1093/bioadv/vbad106 · Cited by 3
Correa, S. M., Burgos, A., Inostroza, A. C., Xu, K., Brotman, Y., & Nikoloski, Z. (2024) · PLANT PHYSIOLOGY · doi:10.1093/plphys/kiae615 · Cited by 3
Self-citationObservability of plant metabolic networks is reflected in the correlation of metabolic profiles.
Schwahn, K., Küken, A., Kliebenstein, D. J., Fernie, A. R., & Nikoloski, Z. (2016) · PLANT PHYSIOLOGY · doi:10.1104/pp.16.00900 · Cited by 3
Self-citationHashemi, S., Razaghi‐Moghadam, Z., & Nikoloski, Z. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011489 · Cited by 3
Self-citationKüken, A., Langary, D., Angeleska, A., & Nikoloski, Z. (2025) · npj Systems Biology and Applications · doi:10.1038/s41540-025-00608-9 · Cited by 2
Self-citationMetabolic Modeling and Flux Analysis: Intersection with Other Omics Techniques
Sushmitha, J., Kumar, C. J. T., Hrishikeshan, K. N., Singh, T., Kavya, T. S., & Vinutha, T. (2025) · Springer eBooks · doi:10.1007/978-981-96-4824-5_5 · Cited by 2
Wang, Y. (2025) · New Phytologist · doi:10.1111/nph.70100 · Cited by 1
Predicting plant thermal responses using a temperature-aware plant metabolic model: a primer
Wendering, P., & Nikoloski, Z. (2025) · in silico Plants · doi:10.1093/insilicoplants/diaf020 · Cited by 1
Self-citationMetabolic design considerations for recycling of respiratory CO 2 in leaves
Hartinger, C., Smith, E. N., & Sweetlove, L. J. (2026) · The Plant Journal · doi:10.1111/tpj.71003 · Cited by 1
Conceptos básicos de análisis de fluxes metabólicos
Henao, C. P. S., Ramírez-Malule, H., & López-Agudelo, V. A. (2020) · Ingeniería y Competitividad · doi:10.25100/iyc.v23i1.9509 · Cited by 1
Current Challenges in Plant Systems Biology
Daloso, D. M., & Williams, T. C. R. (2021) · Advances in experimental medicine and biology · doi:10.1007/978-3-030-80352-0_9 · Cited by 1
The effective deficiency of biochemical networks
Langary, D., Küken, A., & Nikoloski, Z. (2023) · Scientific Reports · doi:10.1038/s41598-023-41767-1 · Cited by 1
Self-citationIizuka, K. (2016) · White Rose eTheses Online (University of Leeds, The University of Sheffield, University of York)
Using a Multi-compartmental Metabolic Model to Predict Carbon Allocation in Arabidopsis thaliana
Zakhartsev, M. V. (2019) · Methods in molecular biology · doi:10.1007/978-1-4939-9562-2_27
Evans, E. M. (2018) · University of Minnesota Digital Conservancy (University of Minnesota)
Computational modeling of the evolutionary transition from C3 to C4 photosynthesisPreprint
Dadras, A., Marashi, S., Kavousi, K., & Banaei‐Moghaddam, A. M. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.01.20.477074
Die Rolle von Photorespiration unter dynamischen Lichtbedingungen
von Bismarck, T., & Wendering, P. (2024) · BIOspektrum · doi:10.1007/s12268-024-2218-6
Modelling leaf metabolism using a charge- and proton-balanced diel flux balance analysis model
Vipina, V., & Shameer, S. (2025) · in silico Plants · doi:10.1093/insilicoplants/diaf008
Omics approaches in secondary metabolite pathways of plants
Maurya, V., Singh, N., Sharma, A., Seth, R., Sharma, R. K., & Kumar, R. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-40529-7.00012-3
Cunha, E., da Silva, M. Â. F., Chaves, I., Demirci, H., Lagoa, D. R., Lima, D., Rocha, M., Rocha, I., & Dias, Ó. (2021) · Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)
Tiozon, R. N., Stolze, S. C., Harzen, A., Nakagami, H., Maeda, H. A., Fernie, A. R., & Yokoyama, R. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.16.706232
A proteomic approach for understanding the metabolic adaptations in non-small cell lung cancer
Martín-Bernabé, A. (2018) · theses.fr (ABES) · doi:10.70675/426e7b40z057az4326z8cfez7da3a5c12c7e
Kaste, J. A. M., Ji, R., Sydow, P. W., Sawers, R. J. H., & Matthews, M. L. (2026) · PLANT PHYSIOLOGY · doi:10.1093/plphys/kiag327
Plant metabolism in a dynamic temperature regime
Lo, W. T., Bagshaw, S. B., Brodsky, V., Süling, B. E., & Nägele, T. (2026) · Current Opinion in Plant Biology · doi:10.1016/j.pbi.2026.102987
Metadata
- Authors
- Arnold, A., & Nikoloski, Z.
- Affiliations
- Max Planck Institute of Molecular Plant Physiology (DE)
- Venue
- Plant Physiology
- Publisher
- Oxford University Press (OUP)
- Published
- 2014-05-07
- Volume
- 165
- Issue
- 3
- Pages
- 1380-1391
- ISSN
- 0032-0889 (ISSN-L)
- 1532-2548
- PMC
- PMC4081344
- OpenAlex
- W2055280476
- Cited by
- 131
- References
- 62
- Citations by year
- 2026: 5 · 2025: 14 · 2024: 7 · 2023: 18 · 2022: 7 · 2021: 18 · 2020: 11 · 2019: 10 · 2018: 13 · 2017: 5 · 2016: 8 · 2015: 10 · 2014: 5
- FWCI
- 3.83
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- PLANT PHYSIOLOGY (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Max Planck Digital Library (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- Arabidopsis thaliana, biomass composition, carbon limitation, nitrogen limitation, photoautotrophic growth, ATP requirement, metabolic engineering
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
