Cited by 252Microbial Metabolic Engineering and BioproductionGene Regulatory Network AnalysisBioinformatics and Genomic Networks

The DOI matches its Crossref record; 178 of the 178 references it cites were found in a registry or an index.

Systems Biology of Metabolism

Nielsen, Jens·2017·Annual Review of Biochemistry

DOI 10.1146/annurev-biochem-061516-044757

Open this paper in CoChat — free account

Abstract

Metabolism is highly complex and involves thousands of different connected reactions; it is therefore necessary to use mathematical models for holistic studies. The use of mathematical models in biology is referred to as systems biology. In this review, the principles of systems biology are described, and two different types of mathematical models used for studying metabolism are discussed: kinetic models and genome-scale metabolic models. The use of different omics technologies, including transcriptomics, proteomics, metabolomics, and fluxomics, for studying metabolism is presented. Finally, the application of systems biology for analyzing global regulatory structures, engineering the metabolism of cell factories, and analyzing human diseases is discussed.

Integrity signals

Registry

Crossref · 10.1146/annurev-biochem-061516-044757

Cites

178 of 178 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

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?

Gene Ontology: tool for the unification of biology
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) · Nature Genetics · doi:10.1038/75556 · Cited by 43,206
Understanding the Warburg Effect: The Metabolic Requirements of Cell Proliferation
Vander Heiden, M. G., Cantley, L. C., & Thompson, C. B. (2009) · Science · doi:10.1126/science.1160809 · Cited by 16,197
Tissue-based map of the human proteome
Uhlén, M., Fagerberg, L., Hallström, B. M., Lindskog, C., Oksvold, P., Mardinoglu, A., Sivertsson, Å., Kampf, C., Sjöstedt, E., Asplund, A., et al. (2015) · Science · doi:10.1126/science.1260419 · Cited by 15,286
Self-citation
A human gut microbial gene catalogue established by metagenomic sequencing
MetaHIT Consortium, Qin, J., Li, R., Raes, J., Arumugam, M., Burgdorf, K. S., Manichanh, C., Nielsen, T., Pons, N., Levenez, F., et al. (2010) · Nature · doi:10.1038/nature08821 · Cited by 11,624
Diet rapidly and reproducibly alters the human gut microbiome
David, L. A., Maurice, C. F., Carmody, R. N., Gootenberg, D. B., Button, J. E., Wolfe, B. E., Ling, A. V., Devlin, A. S., Varma, Y., Fischbach, M. A., et al. (2014) · Nature · doi:10.1038/nature12820 · Cited by 10,100
A metagenome-wide association study of gut microbiota in type 2 diabetes
Qin, J., Li, Y., Cai, Z., Li, S., Zhu, J., Zhang, F., Liang, S., Zhang, W., Guan, Y., Shen, D., et al. (2012) · Nature · doi:10.1038/nature11450 · Cited by 6,762
Exploring the metabolic and genetic control of gene expression on a genomic scale.
Derisi, J., Iyer, V., & Brown, P. (1997) · Science · doi:10.1126/science.278.5338.680 · Cited by 4,832
Functional interactions between the gut microbiota and host metabolism
Tremaroli, V., & Bäckhed, F. (2012) · Nature · doi:10.1038/nature11552 · Cited by 4,387
Resveratrol improves health and survival of mice on a high-calorie diet
Baur, J. A., Pearson, K. J., Price, N. L., Jamieson, H. A., Lerin, C., Kalra, A., Prabhu, V. V., Allard, J. S., Lopez-Lluch, G., Lewis, K., et al. (2006) · Nature · doi:10.1038/nature05354 · Cited by 4,290
Systems biology: a brief overview
Kitano, H. (2002) · Science · doi:10.1126/science.1069492 · Cited by 4,265

Show all 178 references

Research works printed but not linked: 3

Kacser H, 1973, Symp. Soc. Exp. Biol., 27, 65
Kell DB, 1989, Symp. Soc. Gen. Microbiol., 44, 61
Yun J, 2015, BRAF, 350, 1391

Sorted by citations

2023 international olympic committee's (IOC) consensus statement on relative energy deficiency in sport (REDs)
Mountjoy, M., Ackerman, K. E., Bailey, D. M., Burke, L. M., Constantini, N., Hackney, A. C., Heikura, I. A., Melin, A., Pensgaard, A. M., Stellingwerff, T., et al. (2023) · British Journal of Sports Medicine · doi:10.1136/bjsports-2023-106994 · Cited by 724
Systems biology and multi-omics integration: viewpoints from the metabolomics research community
Pinu, F. R., Beale, D. J., Paten, A. M., Kouremenos, K., Swarup, S., Schirra, H. J., & Wishart, D. (2019) · Metabolites · doi:10.3390/metabo9040076 · Cited by 723
Pseudomonas putida as a functional chassis for industrial biocatalysis: from native biochemistry to trans-metabolism
Nikel, P. I., & de Lorenzo, V. (2018) · Metabolic Engineering · doi:10.1016/j.ymben.2018.05.005 · Cited by 480
Yeast Systems Biology: Model Organism and Cell Factory
Nielsen, J. (2019) · Biotechnology Journal · doi:10.1002/biot.201800421 · Cited by 298
Self-citation
Drosophila as a model for ageing
Piper, M. D. W., & Partridge, L. (2018) · Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease · doi:10.1016/j.bbadis.2017.09.016 · Cited by 273
The Potential of Metabolomics in Biomedical Applications
González-Covarrubias, V., Martínez‐Martínez, E., & del Bosque‐Plata, L. (2022) · Metabolites · doi:10.3390/metabo12020194 · Cited by 249
Recent advances in systems and synthetic biology approaches for developing novel cell-factories in non-conventional yeasts
Patra, P., Das, M., Kundu, P., & Ghosh, A. (2021) · Biotechnology Advances · doi:10.1016/j.biotechadv.2021.107695 · Cited by 217
Metabolic Heterogeneity of Cancer Cells: An Interplay between HIF-1, GLUTs, and AMPK
Moldogazieva, N. T., Mokhosoev, I. M., & Terentiev, A. A. (2020) · Cancers · doi:10.3390/cancers12040862 · Cited by 181
Metabolomics Signatures in Type 2 Diabetes: A Systematic Review and Integrative Analysis
Sun, Y., Gao, H., Fan, Z., He, Y., & Yan, Y. (2019) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/clinem/dgz240 · Cited by 151
Metabolic control of nitrogen fixation in rhizobium-legume symbioses
Schulte, C. C. M., Borah, K., Wheatley, R. M., Terpolilli, J. J., Saalbach, G., Crang, N., de Groot, D. H., Ratcliffe, R. G., Kruger, N. J., Papachristodoulou, A., et al. (2021) · Science Advances · doi:10.1126/sciadv.abh2433 · Cited by 132
Current Status and Applications of Adaptive Laboratory Evolution in Industrial Microorganisms
Lee, S., & Kim, P. (2020) · Journal of Microbiology and Biotechnology · doi:10.4014/jmb.2003.03072 · Cited by 131
A bio-based route to the carbon-5 chemical glutaric acid and to bionylon-6,5 using metabolically engineered Corynebacterium glutamicum
Rohles, C. M., Gläser, L., Kohlstedt, M., Gießelmann, G., Pearson, S., del Campo, A., Becker, J., & Wittmann, C. (2018) · Green Chemistry · doi:10.1039/c8gc01901k · Cited by 121
The Warburg Effect Explained: Integration of Enhanced Glycolysis with Heterogeneous Mitochondria to Promote Cancer Cell Proliferation
Alberghina, L. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms242115787 · Cited by 123
Genome-scale metabolic network reconstruction of model animals as a platform for translational research
Wang, H., Robinson, J. L., Kocabaş, P., Gustafsson, J., Anton, M., Cholley, P., Huang, S., Gobom, J., Svensson, T., Uhlén, M., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2102344118 · Cited by 126
Self-citation
Mechanisms and mathematical modeling of ROS production by the mitochondrial electron transport chain
Chenna, S., Koopman, W. J. H., Prehn, J. H. M., & Connolly, N. M. C. (2022) · American Journal of Physiology-Cell Physiology · doi:10.1152/ajpcell.00455.2021 · Cited by 122
Glucose as a Major Antioxidant: When, What for and Why It Fails?
Cherkas, A., Holota, S. M., Мдзинарашвили, Т., Gabbianelli, R., & Žarković, N. (2020) · Antioxidants · doi:10.3390/antiox9020140 · Cited by 111
Engineered Methanotrophy: A Sustainable Solution for Methane-Based Industrial Biomanufacturing
Nguyen, A. D., & Lee, E. Y. (2020) · Trends in biotechnology · doi:10.1016/j.tibtech.2020.07.007 · Cited by 102
Bioproduction of the Recombinant Sweet Protein Thaumatin: Current State of the Art and Perspectives
Joseph, J. A., Akkermans, S., Nimmegeers, P., & van Impe, J. F. M. (2019) · Frontiers in Microbiology · doi:10.3389/fmicb.2019.00695 · Cited by 95
How Rhizobia Adapt to the Nodule Environment
Ledermann, R., Schulte, C. C. M., & Poole, P. S. (2021) · Journal of Bacteriology · doi:10.1128/jb.00539-20 · Cited by 87
Nitrogen limitation reveals large reserves in metabolic and translational capacities of yeast
Yu, R., Campbell, K., Pereira, R., Björkeroth, J., Qi, Q., Vorontsov, E. A., Sihlbom, C., & Nielsen, J. (2020) · Nature Communications · doi:10.1038/s41467-020-15749-0 · Cited by 83
Self-citation
The Impact of Systems Biology on Bioprocessing
Campbell, K., Xia, J., & Nielsen, J. (2017) · Trends in biotechnology · doi:10.1016/j.tibtech.2017.08.011 · Cited by 83
Self-citation
Effect of exogenous N-acyl-homoserine lactones on the anammox process at 15 ℃: Nitrogen removal performance, gene expression and metagenomics analysis
Liu, L., Xu, S., Wang, F., Yan, Z., Tian, Z., & Ji, M. (2021) · Bioresource Technology · doi:10.1016/j.biortech.2021.125760 · Cited by 78
Systems Metabolic Engineering Meets Machine Learning: A New Era for Data‐Driven Metabolic Engineering
Presnell, K. V., & Alper, H. S. (2019) · Biotechnology Journal · doi:10.1002/biot.201800416 · Cited by 69
From pharmacogenetics to pharmaco-omics: Milestones and future directions
Auwerx, C., Sadler, M. C., Reymond, A., & Kutalik, Z. (2022) · Human Genetics and Genomics Advances · doi:10.1016/j.xhgg.2022.100100 · Cited by 72
Getting Bacteria in Shape: Synthetic Morphology Approaches for the Design of Efficient Microbial Cell Factories
Volke, D. C., & Nikel, P. I. (2018) · Advanced Biosystems · doi:10.1002/adbi.201800111 · Cited by 66
Synthetic microbial communities: A gateway to understanding resistance, resilience, and functionality in spontaneously fermented food microbiomes
Nikoloudaki, O., Aheto, F., Cagno, R. D., & Gobbetti, M. (2024) · Food Research International · doi:10.1016/j.foodres.2024.114780 · Cited by 69
Building Synthetic Cells─From the Technology Infrastructure to Cellular Entities
Rothschild, L. J., Averesch, N. J. H., Strychalski, E. A., Moser, F., Glass, J. I., Perez, R., Yekinni, I. O., Rothschild-Mancinelli, B., Kingman, G. A. R., Wu, F., et al. (2024) · ACS Synthetic Biology · doi:10.1021/acssynbio.3c00724 · Cited by 68
Systems-based approaches to study immunometabolism
Purohit, V., Wagner, A., Yosef, N., & Kuchroo, V. K. (2022) · Cellular and Molecular Immunology · doi:10.1038/s41423-021-00783-9 · Cited by 64
Rewiring Central Carbon Metabolism Ensures Increased Provision of Acetyl-CoA and NADPH Required for 3-OH-Propionic Acid Production
Qin, N., Li, L., Ji, X., Li, X., Zhang, Y., Larsson, C., Chen, Y., Nielsen, J., & Liu, Z. (2020) · ACS Synthetic Biology · doi:10.1021/acssynbio.0c00264 · Cited by 60
Self-citation
Emerging mechanistic insights into the regulation of specialized metabolism in plants
Méteignier, L., Nützmann, H., Papon, N., Osbourn, A., & Courdavault, V. (2022) · Nature Plants · doi:10.1038/s41477-022-01288-7 · Cited by 59
Cascaded valorization of brown seaweed to produce l-lysine and value-added products using Corynebacterium glutamicum streamlined by systems metabolic engineering
Hoffmann, S. L., Kohlstedt, M., Jungmann, L., Hutter, M. C., Poblete‐Castro, I., Becker, J., & Wittmann, C. (2021) · Metabolic Engineering · doi:10.1016/j.ymben.2021.07.010 · Cited by 57
Mapping the complexities of Relative Energy Deficiency in Sport (REDs): development of a physiological model by a subgroup of the International Olympic Committee (IOC) Consensus on REDs
Burke, L. M., Ackerman, K. E., Heikura, I. A., Hackney, A. C., & Stellingwerff, T. (2023) · British Journal of Sports Medicine · doi:10.1136/bjsports-2023-107335 · Cited by 59
Towards next-generation cell factories by rational genome-scale engineering
Yilmaz, S., Nyerges, Á., John van der, O. O. S. T., Church, G. M., & Claassens, N. J. (2022) · Nature Catalysis · doi:10.1038/s41929-022-00836-w · Cited by 56
Cell factory design with advanced metabolic modelling empowered by artificial intelligence
Lu, H., Xiao, L., Liao, W., Yan, X., & Nielsen, J. (2024) · Metabolic Engineering · doi:10.1016/j.ymben.2024.07.003 · Cited by 57
Self-citation
A genome-scale metabolic network of the aroma bacterium Leuconostoc mesenteroides subsp. cremoris
Özcan, E., Selvi, S. S., Nıkerel, E., Teusink, B., Öner, E. T., & Çakır, T. (2019) · Applied Microbiology and Biotechnology · doi:10.1007/s00253-019-09630-4 · Cited by 55
Evaluating plant growth–defense trade-offs by modeling the interaction between primary and secondary metabolism
Zrimec, J., Correa, S. M., Zagorščak, M., Petek, M., Bleker, C., Stare, K., Schuy, C., Sonnewald, S., Gruden, K., & Nikoloski, Z. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2502160122 · Cited by 53
Ogataea polymorpha as a next-generation chassis for industrial biotechnology
Xie, L., Yu, W., Gao, J., Wang, H., & Zhou, Y. J. (2024) · Trends in biotechnology · doi:10.1016/j.tibtech.2024.03.007 · Cited by 49
TrpNet: Understanding Tryptophan Metabolism across Gut Microbiome
Lu, Y. P., Chong, J., Shen, S., Chammas, J.-B., Chalifour, L. E., & Xia, J. (2021) · Metabolites · doi:10.3390/metabo12010010 · Cited by 46
Ancient balancing selection maintains incompatible versions of the galactose pathway in yeast
Boocock, J., Sadhu, M. J., Durvasula, A., Bloom, J. S., & Kruglyak, L. (2021) · Science · doi:10.1126/science.aba0542 · Cited by 41
Next-Generation Genome-Scale Metabolic Modeling through Integration of Regulatory Mechanisms
Chung, C. H., Lin, D., Eames, A., & Chandrasekaran, S. (2021) · Metabolites · doi:10.3390/metabo11090606 · Cited by 48
Integrating –omics data into genome-scale metabolic network models: principles and challenges
Ramon, C., Gollub, M. G., & Stelling, J. (2018) · Essays in Biochemistry · doi:10.1042/ebc20180011 · Cited by 48
SLIMEr: probing flexibility of lipid metabolism in yeast with an improved constraint-based modeling framework
Sánchez, B. J., Li, F., Kerkhoven, E. J., & Nielsen, J. (2019) · BMC Systems Biology · doi:10.1186/s12918-018-0673-8 · Cited by 46
Self-citation
The Evolution of Systems Biology and Systems Medicine: From Mechanistic Models to Uncertainty Quantification
Qiao, L., Khalilimeybodi, A., Linden-Santangeli, N. J., & Rangamani, P. (2025) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-102723-065309 · Cited by 57
Metabolic Model of thePhytophthora infestans-Tomato Interaction Reveals Metabolic Switches during Host Colonization
Rodenburg, S. Y. A., Seidl, M. F., Judelson, H. S., Vu, A. L., Govers, F., & de Ridder, D. (2019) · mBio · doi:10.1128/mbio.00454-19 · Cited by 44
Rapid hazard characterization of environmental chemicals using a compendium of human cell lines from different organs
Chen, Z. (2020) · ALTEX · doi:10.14573/altex.2002291 · Cited by 43
Learning the Regulatory Code of Gene Expression
Zrimec, J., Buric, F., Kokina, M., García, V., & Zelezniak, A. (2021) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2021.673363 · Cited by 45
Analysis of human metabolism by reducing the complexity of the genome-scale models using redHUMAN
Masid, M., Ataman, M., & Vassily Hatzimanikatis, A. (2020) · Nature Communications · doi:10.1038/s41467-020-16549-2 · Cited by 43
Resolving the Rules of Robustness and Resilience in Biology Across Scales
Crespi, E. J., Burnap, R. L., Chen, J., Das, M., Gassman, N. R., Rosa, E., Simmons, R. B., Wada, H., Wang, Z. Q., Xiao, J., et al. (2021) · Integrative and Comparative Biology · doi:10.1093/icb/icab183 · Cited by 43
The Model System Saccharomyces cerevisiae Versus Emerging Non-Model Yeasts for the Production of Biofuels
Lacerda, M. P. F., Oh, E. J., & Eckert, C. A. (2020) · Life · doi:10.3390/life10110299 · Cited by 41
A new paradigm for drug discovery in the treatment of complex diseases: drug discovery and optimization
Yuan, Y., Yu, L.-L., Bi, C., Huang, L., Su, B., Nie, J., Dou, Z., Yang, S., & Li, Y. (2025) · Chinese Medicine · doi:10.1186/s13020-025-01075-4 · Cited by 42
Genome‐wide characterization of Phytophthora infestans metabolism: a systems biology approach
Rodenburg, S. Y. A., Seidl, M. F., de Ridder, D., & Govers, F. (2017) · Molecular Plant Pathology · doi:10.1111/mpp.12623 · Cited by 37
Kinetic models of metabolism that consider alternative steady-state solutions of intracellular fluxes and concentrations
Tuure, H., Fengos, G., Ataman, M., Mišković, L., & Vassily Hatzimanikatis, A. (2018) · Metabolic Engineering · doi:10.1016/j.ymben.2018.10.005 · Cited by 40
On the evolution of coenzyme biosynthesis
Kirschning, A. (2022) · Natural Product Reports · doi:10.1039/d2np00037g · Cited by 37
Genome-scale metabolic networks in time and space
Øyås, O., & Stelling, J. (2017) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2017.12.003 · Cited by 34
A genome‐scale TF–DNA interaction network of transcriptional regulation of Arabidopsis primary and specialized metabolism
Tang, M., Li, B., Zhou, X., Bolt, T. M., Li, J. J., Cruz, N., Gaudinier, A., Ngo, R. T., Clark-Wiest, C., Kliebenstein, D. J., et al. (2021) · Molecular Systems Biology · doi:10.15252/msb.202110625 · Cited by 36
Strategies and challenges for metabolic rewiring
Yu, T., Dabirian, Y., Liu, Q., Siewers, V., & Nielsen, J. (2019) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2019.03.004 · Cited by 35
Self-citation
Approaches to Computational Strain Design in the Multiomics Era
John, P. C. S., & Bomble, Y. J. (2019) · Frontiers in Microbiology · doi:10.3389/fmicb.2019.00597 · Cited by 39
Coupled metabolic‐hydrodynamic modeling enabling rational scale‐up of industrial bioprocesses
Wang, G., Haringa, C., Tang, W., Noorman, H. J., Chu, J., Zhuang, Y., & Zhang, S. (2019) · Biotechnology and Bioengineering · doi:10.1002/bit.27243 · Cited by 36
Substrate-dependent CO2 fixation in heterotrophic bacteria revealed by stable isotope labelling
Spona-Friedl, M., Braun, A., Huber, C., Eisenreich, W., Griebler, C., Kappler, A. A., & Elsner, M. (2020) · FEMS Microbiology Ecology · doi:10.1093/femsec/fiaa080 · Cited by 34
Model reduction of genome-scale metabolic models as a basis for targeted kinetic models
van Rosmalen, R. P., Smith, R. W., dos Santos, V. A. P. M., Fleck, C. C., & Diez, M. S. (2021) · Metabolic Engineering · doi:10.1016/j.ymben.2021.01.008 · Cited by 36
An atlas of metallome and metabolome interactions and associations with incident diabetes in the Strong Heart Family Study
Sanchez, T. R., Hu, X. Y., Zhao, J., Tran, V., LoIacono, N. J., Go, Y., Goessler, W., Cole, S. A., Umans, J. G., Jones, D. P., et al. (2021) · Environment International · doi:10.1016/j.envint.2021.106810 · Cited by 35
COVID-19: repositioning nutrition research for the next pandemic
Jaggers, G. K., Watkins, B. A., & Rodriguez, R. L. (2020) · Nutrition Research · doi:10.1016/j.nutres.2020.07.005 · Cited by 34
Quantitative -omic data empowers bottom-up systems biology
Yurkovich, J. T., & Palsson, B. O. (2018) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2018.01.009 · Cited by 33
Environmental metal exposure, seminal plasma metabolome and semen quality: Evidence from Chinese reproductive-aged men
Xu, S., Wu, Y., Chen, Y., Lü, W., Wang, Y., Gao, B., & Zhang, J. (2022) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2022.155860 · Cited by 33
Recent advances in culture medium design for enhanced production of monoclonal antibodies in CHO cells: A comparative study of machine learning and systems biology approaches
Kavoni, H., Savizi, I. S. P., Lewis, N. E., & Shojaosadati, S. A. (2024) · Biotechnology Advances · doi:10.1016/j.biotechadv.2024.108480 · Cited by 36
Monitoring and modelling the glutamine metabolic pathway: a review and future perspectives
Mirveis, Z., Howe, O. L., Cahill, P. A., Patil, N., & Byrne, H. J. (2023) · Metabolomics · doi:10.1007/s11306-023-02031-9 · Cited by 35
Genome‐scale modeling for Bacillus coagulans to understand the metabolic characteristics
Chen, Y., Sun, Y., Liu, Z., Dong, F., Li, Y., & Wang, Y. (2020) · Biotechnology and Bioengineering · doi:10.1002/bit.27488 · Cited by 30
Oral Sources of Salivary Metabolites
Hyvärinen, E., Kashyap, B., & Kullaa, A. M. (2023) · Metabolites · doi:10.3390/metabo13040498 · Cited by 30
13C metabolic flux analysis-guided metabolic engineering of Escherichia coli for improved acetol production from glycerol
Yao, R., Li, J., Feng, L., Zhang, X., & Hu, H. (2019) · Biotechnology for Biofuels · doi:10.1186/s13068-019-1372-4 · Cited by 29
The Comprehensive “Omics” Approach from Metabolomics to Advanced Omics for Development of Immune Checkpoint Inhibitors: Potential Strategies for Next Generation of Cancer Immunotherapy
Yoon, S. J., Lee, C. B., Chae, S. U., Jo, S. J., & Bae, S. K. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22136932 · Cited by 27
The integration of machine learning into traditional Chinese medicine
Hong, Y., Zhu, S., Liu, Y., Tian, C., Xu, H., Chen, G., Tao, L., & Xie, T. (2024) · Journal of Pharmaceutical Analysis · doi:10.1016/j.jpha.2024.101157 · Cited by 30
Systems Biology: A Multi-Omics Integration Approach to Metabolism and the Microbiome
Son, J. W., Shoaie, S., & Lee, S. (2020) · Endocrinology and Metabolism · doi:10.3803/enm.2020.303 · Cited by 26
A novel yeast hybrid modeling framework integrating Boolean and enzyme-constrained networks enables exploration of the interplay between signaling and metabolism
Österberg, L., Domenzain, I., Münch, J., Nielsen, J., Hohmann, S., & Cvijović, M. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008891 · Cited by 24
Self-citation
Nonlinear programming reformulation of dynamic flux balance analysis models
de Oliveira, R. D., Roux, G. A. C. L., & Mahadevan, R. (2022) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2022.108101 · Cited by 23
Intersection of Immunology and Metabolism in Myocardial Disease
Thorp, E. B., & Karlstaedt, A. (2024) · Circulation Research · doi:10.1161/circresaha.124.323660 · Cited by 27
Visualizing metabolic network dynamics through time-series metabolomic data
Buchweitz, L. F., Yurkovich, J. T., Blessing, C., Kohler, V., Schwarzkopf, F., King, Z. A., Yang, L., Jóhannsson, F., Sigurjónsson, Ó. E., Rolfsson, Ó., et al. (2020) · BMC Bioinformatics · doi:10.1186/s12859-020-3415-z · Cited by 22
Providing new insights on the biphasic lifestyle of the predatory bacterium Bdellovibrio bacteriovorus through genome-scale metabolic modeling
Herencias, C., Salgado, S., Prieto, M. A., & Nogales, J. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007646 · Cited by 23
Biofilms in the gravity sewer interfaces: making a friend from a foe
Augustyniak, A., Sikora, P., Grygorcewicz, B., Despot, D., Braun, B., Rakoczy, R., Szewzyk, U., Barjenbruch, M., & Stephan, D. A. (2021) · Reviews in Environmental Science and Bio/Technology · doi:10.1007/s11157-021-09582-0 · Cited by 21
The essential role of mRNA degradation in understanding and engineering E. coli metabolism
Roux, C., Etienne, T. A., Hajnsdorf, E., Ropers, D., Carpousis, A. J., Cocaign‐Bousquet, M., & Girbal, L. (2021) · Biotechnology Advances · doi:10.1016/j.biotechadv.2021.107805 · Cited by 20
Inhibition of mitochondrial complex I leading to NAD+/NADH imbalance in type 2 diabetic patients who developed late stent thrombosis: Evidence from an integrative analysis of platelet bioenergetics and metabolomics
Gao, M.-J., Cui, N., Liu, X., & Wang, X. (2022) · Redox Biology · doi:10.1016/j.redox.2022.102507 · Cited by 21
Hierarchical metabolic engineering for rewiring cellular metabolism
Chai, T., Tao, Y., Zhao, C., & Chen, X. (2025) · FEMS Microbiology Reviews · doi:10.1093/femsre/fuaf047 · Cited by 24
The effects of calcium–silicate–hydrate (C–S–H) seeds on reference microorganisms
Augustyniak, A., Sikora, P., Jabłońska, J., Cendrowski, K., John, E., Stephan, D. A., & Mijowska, E. (2020) · Applied Nanoscience · doi:10.1007/s13204-020-01347-5 · Cited by 20
Global Cellular Metabolic Rewiring Adapts Corynebacterium glutamicum to Efficient Nonnatural Xylose Utilization
Sun, X. H., Mao, Y., Luo, J., Liu, P., Jiang, M., He, G., Zhang, Z., Cao, Q., Shen, J., Ma, H., et al. (2022) · Applied and Environmental Microbiology · doi:10.1128/aem.01518-22 · Cited by 20
Metabolic signatures of regulation by phosphorylation and acetylation
Smith, K., Shen, F., Lee, H., & Chandrasekaran, S. (2022) · iScience · doi:10.1016/j.isci.2021.103730 · Cited by 22
Metabolism along the life journey of T cells
Peng, M., & Li, M. O. (2023) · Life Metabolism · doi:10.1093/lifemeta/load002 · Cited by 20
A large-scale metabolomics study to harness chemical diversity and explore biochemical mechanisms in ryegrass
Subbaraj, A., Huege, J., Fraser, K., Cao, M., Rasmussen, S., Faville, M. J., Harrison, S. J., & Jones, C. S. (2019) · Communications Biology · doi:10.1038/s42003-019-0289-6 · Cited by 19
Nonlinear Predictive Control of a Bioreactor by Surrogate Model Approximation of Flux Balance Analysis
de Oliveira, R. D., Guedes, M. N., Matias, J., & Roux, G. A. C. L. (2021) · Industrial & Engineering Chemistry Research · doi:10.1021/acs.iecr.1c01242 · Cited by 19
Genome-Scale Metabolic Modelling of Lifestyle Changes in Rhizobium leguminosarum
Schulte, C. C. M., Ramachandran, V. K., Papachristodoulou, A., & Poole, P. S. (2022) · mSystems · doi:10.1128/msystems.00975-21 · Cited by 18
Identification of Sex-Specific Plasma Biomarkers Using Metabolomics for Major Depressive Disorder in Children and Adolescents
Jiang, Y., Qin, M., Teng, T., Li, X., Yu, Y., Wang, J., Wu, H., He, Y., Zhou, X., & Xie, P. (2022) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2022.929207 · Cited by 19
Tn-Core: A Toolbox for Integrating Tn-seq Gene Essentiality Data and Constraint-Based Metabolic Modeling
diCenzo, G. C., Mengoni, A., & Fondi, M. (2018) · ACS Synthetic Biology · doi:10.1021/acssynbio.8b00432 · Cited by 17
Genome-scale metabolic model of oleaginous yeast Papiliotrema laurentii
Ventorim, R. Z., Ferreira, M. A. D. M., de Almeida, E. L. M., Kerkhoven, E. J., & da Silveira, W. B. (2022) · Biochemical Engineering Journal · doi:10.1016/j.bej.2022.108353 · Cited by 17
ReCodLiver0.9: Overcoming Challenges in Genome-Scale Metabolic Reconstruction of a Non-model Species
Hanna, E. M., Zhang, X., Eide, M., Fallahi, S., Furmanek, T., Yadetie, F., Zielinski, D. C., Goksøyr, A., & Jonassen, I. (2020) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2020.591406 · Cited by 16
Exploiting transcriptomic data for metabolic engineering: toward a systematic strain design
Kim, M., Park, B. G., Kim, J., Kim, J. Y., & Kim, B. (2018) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2018.01.020 · Cited by 17
Integrated untargeted and targeted metabolomics to reveal therapeutic effect and mechanism of Alpiniae oxyphyllae fructus on Alzheimer’s disease in APP/PS1 mice
Zhou, S., Liu, L., Zhang, Y., Zhang, Z., Li, H., Fan, F., He, J., Kang, J., & Zuo, L. (2023) · Frontiers in Pharmacology · doi:10.3389/fphar.2022.1104954 · Cited by 18
Systems biology approach in the formulation of chemically defined media for recombinant protein overproduction
Savizi, I. S. P., Soudi, T., & Shojaosadati, S. A. (2019) · Applied Microbiology and Biotechnology · doi:10.1007/s00253-019-10048-1 · Cited by 15
Statistical inference in ensemble modeling of cellular metabolism
Tuure, H., Boldi, M., & Vassily Hatzimanikatis, A. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007536 · Cited by 15
Advances in Astrocyte Computational Models: From Metabolic Reconstructions to Multi-omic Approaches
González, J., Pinzón, A. M., Angarita-Rodríguez, A., Aristizábal-Pachón, A. F., Barreto, G. E., & Martín-Jiménez, C. (2020) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2020.00035 · Cited by 15
Evaluating accessibility, usability and interoperability of genome-scale metabolic models for diverse yeasts species
Domenzain, I., Li, F., Kerkhoven, E. J., & Siewers, V. (2021) · FEMS Yeast Research · doi:10.1093/femsyr/foab002 · Cited by 13
The Antiviral Molecule 5-Pyridoxolactone Identified Post BmNPV Infection of the Silkworm, Bombyx mori
Hua, X., Zhang, Q., Xu, W., Wang, X., Wang, F., Zhao, P., & Xia, Q. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22147423 · Cited by 15
Computational biology predicts metabolic engineering targets for increased production of 103 valuable chemicals in yeast
Domenzain, I., Lu, Y. P., Wang, H., Shi, J., Lu, H., & Nielsen, J. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2417322122 · Cited by 20
Self-citation

Authors: Nielsen, J.
Affiliations: Science for Life Laboratory (SE)
Corresponding author: Nielsen, Jens
ORCID: Nielsen, Jens
Venue: Annual Review of Biochemistry
Publisher: Annual Reviews
Published: 2017-06-20
Volume: 86
Issue: 1
Pages: 245-275
ISSN: 0066-4154 (ISSN-L)
1545-4509
DOI: 10.1146/annurev-biochem-061516-044757
OpenAlex: W2594933406
Cited by: 252
References: 178
Citations by year: 2026: 26 · 2025: 25 · 2024: 36 · 2023: 31 · 2022: 32 · 2021: 31 · 2020: 31 · 2019: 24 · 2018: 12 · 2017: 4
FWCI: 1.82
Type: journal article
Language: en
Open access: closed
Locations: Annual Review of Biochemistry (publishedVersion)
PubMed (publishedVersion)
Chalmers Publication Library (Chalmers University of Technology) (submittedVersion)
Chalmers Research (Chalmers University of Technology) (submittedVersion)
Funders: Novo Nordisk Fonden (NNF10CC1016517)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
Keywords: systems biology, metabolism, kinetic model, genome-scale metabolic models, omics technologies, transcriptomics, proteomics, metabolomics, fluxomics, metabolic engineering, cell factories, metabolic disorders
MeSH terms: Bacteria, Fungi, Humans, Kinetics, Models, Biological, Models, Statistical, Genome, Proteomics, Systems Biology, Metabolomics, Transcriptome, 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

Systems Biology of Metabolism

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks