Systems Biology of Metabolism

Gene Ontology: tool for the unification of biology

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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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

Exploring the metabolic and genetic control of gene expression on a genomic scale.

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Functional interactions between the gut microbiota and host metabolism

Tremaroli, V., & Bäckhed, F. (2012) · Nature · doi:10.1038/nature11552 · Cited by 4,387 · see citing works

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 · see citing works

Systems biology: a brief overview

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Commensal <i>Bifidobacterium</i> promotes antitumor immunity and facilitates anti–PD-L1 efficacy

Sivan, A., Corrales, L., Hubert, N., Williams, J. B., Aquino-Michaels, K., Earley, Z. M., Benyamin, F. W., Man Lei, Y., Jabri, B., Alegre, M.-L., et al. (2015) · Science · doi:10.1126/science.aac4255 · Cited by 3,888 · see citing works

Small molecule activators of sirtuins extend Saccharomyces cerevisiae lifespan

Howitz, K. T., Bitterman, K. J., Cohen, H. Y., Lamming, D. W., Lavu, S., Wood, J. G., Zipkin, R. E., Chung, P., Kisielewski, A., Zhang, L.-L., et al. (2003) · Nature · doi:10.1038/nature01960 · Cited by 3,796 · see citing works

Anticancer immunotherapy by CTLA-4 blockade relies on the gut microbiota

Vétizou, M., Pitt, J. M., Daillère, R., Lepage, P., Waldschmitt, N., Flament, C., Rusakiewicz, S., Routy, B., Roberti, M. P., Duong, C. P. M., et al. (2015) · Science · doi:10.1126/science.aad1329 · Cited by 3,488 · see citing works

AMPK regulates energy expenditure by modulating NAD+ metabolism and SIRT1 activity

Cantó, C., Gerhart-Hines, Z., Feige, J. N., Lagouge, M., Noriega, L., Milne, J. C., Elliott, P. J., Puigserver, P., & Auwerx, J. (2009) · Nature · doi:10.1038/nature07813 · Cited by 3,300 · see citing works

On the Dependency of Cellular Protein Levels on mRNA Abundance

Liu, Y., Beyer, A., & Aebersold, R. (2016) · Cell · doi:10.1016/j.cell.2016.03.014 · Cited by 3,221 · see citing works

Gut metagenome in European women with normal, impaired and diabetic glucose control

Karlsson, F. H., Tremaroli, V., Nookaew, I., Bergström, G., Behre, C. J., Fagerberg, B., Nielsen, J., & Bäckhed, F. (2013) · Nature · doi:10.1038/nature12198 · Cited by 3,115 · see citing works

Production of the antimalarial drug precursor artemisinic acid in engineered yeast

Ro, D.-K., Paradise, E. M., Ouellet, M., Fisher, K. J., Newman, K. L., Ndungu, J. M., Ho, K. A., Eachus, R. A., Ham, T. S., Kirby, J., et al. (2006) · Nature · doi:10.1038/nature04640 · Cited by 2,805 · see citing works

Computational systems biology.

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Transcriptional regulatory code of a eukaryotic genome

Harbison, C. T., Gordon, D. B., Lee, T. I., Rinaldi, N. J., MacIsaac, K. D., Danford, T., Hannett, N. M., Tagne, J., Reynolds, D. B., Yoo, J. Y., et al. (2004) · Nature · doi:10.1038/nature02800 · Cited by 2,116 · see citing works

Integrated Genomic and Proteomic Analyses of a Systematically Perturbed Metabolic Network

Ideker, T., Thorsson, V., Ranish, J. A., Christmas, R., Buhler, J., Eng, J. K., Bumgarner, R., Goodlett, D. R., Aebersold, R., & Hood, L. (2001) · Science · doi:10.1126/science.292.5518.929 · Cited by 2,050 · see citing works

Reductive glutamine metabolism by IDH1 mediates lipogenesis under hypoxia

Metallo, C. M., Gameiro, P. A., Bell, E. L., Mattaini, K. R., Yang, J., Hiller, K., Jewell, C. M., Johnson, Z. R., Irvine, D. J., Guarente, L., et al. (2012) · Nature · doi:10.1038/nature10602 · Cited by 1,791 · see citing works

A N <scp>EW</scp> A <scp>PPROACH TO</scp> D <scp>ECODING</scp> L <scp>IFE</scp> : Systems Biology

Ideker, T., Galitski, T., & Hood, L. (2001) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev.genom.2.1.343 · Cited by 1,664 · see citing works

NAD ⁺ /NADH and NADP ⁺ /NADPH in Cellular Functions and Cell Death: Regulation and Biological Consequences

Ying, W. (2008) · Antioxidants &amp; Redox Signaling · doi:10.1089/ars.2007.1672 · Cited by 1,494 · see citing works

Interdependence of Cell Growth and Gene Expression: Origins and Consequences

Scott, M., Gunderson, C. W., Mateescu, E. M., Zhang, Z., & Hwa, T. (2010) · Science · doi:10.1126/science.1192588 · Cited by 1,470 · see citing works

A Linear Steady-State Treatment of Enzymatic Chains. General Properties, Control and Effector Strength

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Engineering Cellular Metabolism

Nielsen, J., & Keasling, J. D. (2016) · Cell · doi:10.1016/j.cell.2016.02.004 · Cited by 1,400 · see citing works

Global reconstruction of the human metabolic network based on genomic and bibliomic data

Duarte, N. C., Becker, S. A., Jamshidi, N., Thiele, I., Mo, M. L., Vo, T. D., Srivas, R., & Palsson, B. Ø. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0610772104 · Cited by 1,374 · see citing works

Symptomatic atherosclerosis is associated with an altered gut metagenome

Karlsson, F. H., Fåk, F., Nookaew, I., Tremaroli, V., Fagerberg, B., Petranovic, D., Bäckhed, F., & Nielsen, J. (2012) · Nature Communications · doi:10.1038/ncomms2266 · Cited by 1,371 · cited 1× · see citing works

Microbial production of fatty-acid-derived fuels and chemicals from plant biomass

Steen, E. J., Kang, Y., Bokinsky, G., Hu, Z., Schirmer, A., McClure, A., del Cardayré, S. B., & Keasling, J. D. (2010) · Nature · doi:10.1038/nature08721 · Cited by 1,338 · see citing works

Calorie Restriction-like Effects of 30 Days of Resveratrol Supplementation on Energy Metabolism and Metabolic Profile in Obese Humans

Timmers, S., Konings, E., Bilet, L., Houtkooper, R. H., van de Weijer, T., Goossens, G. H., Hoeks, J., van der Krieken, S., Ryu, D., Kersten, S., et al. (2011) · Cell Metabolism · doi:10.1016/j.cmet.2011.10.002 · Cited by 1,320 · see citing works

A Whole-Cell Computational Model Predicts Phenotype from Genotype

Karr, J. R., Sanghvi, J. C., Macklin, D. N., Gutschow, M. V., Jacobs, J. M., Bolival, B., Assad-Garcia, N., Glass, J. I., & Covert, M. W. (2012) · Cell · doi:10.1016/j.cell.2012.05.044 · Cited by 1,300 · cited 2× · see citing works

Discovering regulatory and signalling circuits in molecular interaction networks

Ideker, T., Ozier, O., Schwikowski, B., & Siegel, A. F. (2002) · Bioinformatics · doi:10.1093/bioinformatics/18.suppl_1.s233 · Cited by 1,197 · see citing works

Toward a Science of Metabolic Engineering

Bailey, J. E. (1991) · Science · doi:10.1126/science.2047876 · Cited by 1,170 · see citing works

Metabolic engineering of Escherichia coli for direct production of 1,4-butanediol

Yim, H. H., Haselbeck, R. J., Niu, W., Pujol-Baxley, C., Burgard, A. P., Boldt, J., Khandurina, J., Trawick, J. D., Osterhout, R. E., Stephen, R., et al. (2011) · Nature Chemical Biology · doi:10.1038/nchembio.580 · Cited by 1,114 · see citing works

Genome-Scale Reconstruction of the <i>Saccharomyces cerevisiae</i> Metabolic Network

Förster, J., Famili, I., Fu, P., Palsson, B. Ø., & Nielsen, J. (2003) · Genome Research · doi:10.1101/gr.234503 · Cited by 1,069 · see citing works

The Moderately Efficient Enzyme: Evolutionary and Physicochemical Trends Shaping Enzyme Parameters

Bar-Even, A., Noor, E., Savir, Y., Liebermeister, W., Davidi, D., Tawfik, D. S., & Milo, R. (2011) · Biochemistry · doi:10.1021/bi2002289 · Cited by 1,050 · see citing works

A community-driven global reconstruction of human metabolism

Thiele, I., Swainston, N., Fleming, R. M. T., Hoppe, A., Sahoo, S., Aurich, M. K., Haraldsdottir, H., Mo, M. L., Rolfsson, O., Stobbe, M. D., et al. (2013) · Nature Biotechnology · doi:10.1038/nbt.2488 · Cited by 1,019 · see citing works

A comprehensive genome-scale reconstruction of Escherichia coli metabolism—2011

Orth, J. D., Conrad, T. M., Na, J., Lerman, J. A., Nam, H., Feist, A. M., & Palsson, B. Ø. (2011) · Molecular Systems Biology · doi:10.1038/msb.2011.65 · Cited by 1,006 · see citing works

Complete biosynthesis of opioids in yeast

Galanie, S. S., Thodey, K., Trenchard, I. J., Interrante, M. V. F., & Smolke, C. D. (2015) · Science · doi:10.1126/science.aac9373 · Cited by 989 · see citing works

In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data

Edwards, J. S., Ibarra, R. U., & Palsson, B. Ø. (2001) · Nature Biotechnology · doi:10.1038/84379 · Cited by 941 · see citing works

Metabolic dependencies drive species co-occurrence in diverse microbial communities

Zelezniak, A., Andrejev, S., Ponomarova, O., Mende, D. R., Bork, P., & Patil, K. R. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1421834112 · Cited by 930 · see citing works

The <i>Escherichia coli</i> MG1655 <i>in silico</i> metabolic genotype: Its definition, characteristics, and capabilities

Edwards, J. S., & Palsson, B. O. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.97.10.5528 · Cited by 875 · see citing works

Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth

Ibarra, R. U., Edwards, J. S., & Palsson, B. O. (2002) · Nature · doi:10.1038/nature01149 · Cited by 857 · cited 2× · see citing works

A Mitochondrial Pyruvate Carrier Required for Pyruvate Uptake in Yeast, <i>Drosophila</i> , and Humans

Bricker, D. K., Taylor, E. B., Schell, J. C., Orsak, T., Boutron, A., Chen, Y.-C., Cox, J. E., Cardon, C. M., Van Vranken, J. G., Dephoure, N., et al. (2012) · Science · doi:10.1126/science.1218099 · Cited by 843 · see citing works

13C Metabolic Flux Analysis

Wiechert, W. (2001) · Metabolic Engineering · doi:10.1006/mben.2001.0187 · Cited by 801 · see citing works

Metabolic engineering for the microbial production of 1,3-propanediol.

Nakamura, C. E., & Whited, G. (2003) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2003.08.005 · Cited by 801 · see citing works

Overflow metabolism in Escherichia coli results from efficient proteome allocation

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E-CELL: software environment for whole-cell simulation

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Sirtuin 1 Modulates Cellular Responses to Hypoxia by Deacetylating Hypoxia-Inducible Factor 1α

Lim, J.-H., Lee, Y.-M., Chun, Y.-S., Chen, J., Kim, J.-E., & Park, J.-W. (2010) · Molecular Cell · doi:10.1016/j.molcel.2010.05.023 · Cited by 758 · see citing works

Identification and Functional Expression of the Mitochondrial Pyruvate Carrier

Herzig, S., Raemy, E., Montessuit, S., Veuthey, J.-L., Zamboni, N., Westermann, B., Kunji, E. R. S., & Martinou, J.-C. (2012) · Science · doi:10.1126/science.1218530 · Cited by 747 · see citing works

The quantitative and condition-dependent Escherichia coli proteome

Schmidt, A., Kochanowski, K., Vedelaar, S. R., Ahrné, E., Volkmer, B., Callipo, L., Knoops, K., Bauer, M., Aebersold, R. H., & Heinemann, M. (2015) · Nature Biotechnology · doi:10.1038/nbt.3418 · Cited by 737 · see citing works

Using Genome-scale Models to Predict Biological Capabilities

O’Brien, E. J., Monk, J. M., & Palsson, B. Ø. (2015) · Cell · doi:10.1016/j.cell.2015.05.019 · Cited by 709 · see citing works

Systematic evaluation of objective functions for predicting intracellular fluxes in Escherichia coli

Schuetz, R. J., Kuepfer, L., & Sauer, U. (2007) · Molecular Systems Biology · doi:10.1038/msb4100162 · Cited by 684 · see citing works

Enriching the gene set analysis of genome-wide data by incorporating directionality of gene expression and combining statistical hypotheses and methods

Väremo, L., Nielsen, J., & Nookaew, I. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt111 · Cited by 683 · see citing works

Uncovering transcriptional regulation of metabolism by using metabolic network topology

Patil, K. R., & NIELSEN, J. K. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0406811102 · Cited by 649 · see citing works

Network-based prediction of human tissue-specific metabolism

Shlomi, T., Cabili, M. N., Herrgård, M. J., Palsson, B. Ø., & Ruppin, E. (2008) · Nature Biotechnology · doi:10.1038/nbt.1487 · Cited by 637 · see citing works

Network Rigidity and Metabolic Engineering in Metabolite Overproduction

Stephanopoulos, G. N., & Vallino, J. J. (1991) · Science · doi:10.1126/science.1904627 · Cited by 601 · see citing works

From zero to hero—Design-based systems metabolic engineering of Corynebacterium glutamicum for l-lysine production

Becker, J., Zelder, O., Häfner, S., Schröder, H., & Wittmann, C. (2011) · Metabolic Engineering · doi:10.1016/j.ymben.2011.01.003 · Cited by 594 · see citing works

Metabolic engineering of <i>Escherichia coli</i> for the production of <scp>l</scp> -valine based on transcriptome analysis and <i>in silico</i> gene knockout simulation

Park, J. H., Lee, K. H., Kim, T. Y., & Lee, S. Y. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0702609104 · Cited by 592 · see citing works

A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology

Herrgård, M. J., Swainston, N., Dobson, P., Dunn, W. B., Arga, K. Y., Arvas, M., Blüthgen, N., Borger, S., Costenoble, R., Heinemann, M., et al. (2008) · Nature Biotechnology · doi:10.1038/nbt1492 · Cited by 588 · see citing works

Quantitative Analysis of Fission Yeast Transcriptomes and Proteomes in Proliferating and Quiescent Cells

Marguerat, S., Schmidt, A., Codlin, S., Chen, W., Aebersold, R., & Bähler, J. (2012) · Cell · doi:10.1016/j.cell.2012.09.019 · Cited by 571 · see citing works

Dynamic modeling of the central carbon metabolism of Escherichia coli

Chassagnole, C., Noisommit‐Rizzi, N., Schmid, J. W., Mauch, K., & Reuß, M. (2002) · Biotechnology and Bioengineering · doi:10.1002/bit.10288 · Cited by 553 · see citing works

Here is the evidence, now what is the hypothesis? The complementary roles of inductive and hypothesis‐driven science in the post‐genomic era

Kell, D. B., & Oliver, S. G. (2004) · BioEssays · doi:10.1002/bies.10385 · Cited by 535 · see citing works

Mathematical Models of Protein Kinase Signal Transduction

Heinrich, R., Neel, B. G., & Rapoport, T. A. (2002) · Molecular Cell · doi:10.1016/s1097-2765(02)00528-2 · Cited by 511 · see citing works

Genome-scale metabolic modelling of hepatocytes reveals serine deficiency in patients with non-alcoholic fatty liver disease

Mardinoğlu, A., Ågren, R., Kampf, C., Asplund, A., Uhlén, M., & Nielsen, J. (2014) · Nature Communications · doi:10.1038/ncomms4083 · Cited by 507 · see citing works

Haem oxygenase is synthetically lethal with the tumour suppressor fumarate hydratase

Frezza, C., Zheng, L., Folger, O., Rajagopalan, K. N., MacKenzie, E. D., Jerby, L., Micaroni, M., Chaneton, B., Adam, J., Hedley, A., et al. (2011) · Nature · doi:10.1038/nature10363 · Cited by 503 · see citing works

Genome‐scale models of metabolism and gene expression extend and refine growth phenotype prediction

O'Brien, E. J., Lerman, J. A., Chang, R. L., Hyduke, D. R., & Palsson, B. Ø. (2013) · Molecular Systems Biology · doi:10.1038/msb.2013.52 · Cited by 471 · see citing works

Predicting selective drug targets in cancer through metabolic networks

Folger, O., Jerby‐Arnon, L., Frezza, C., Gottlieb, E., Ruppin, E., & Shlomi, T. (2011) · Molecular Systems Biology · doi:10.1038/msb.2011.35 · Cited by 469 · see citing works

Evolutionary programming as a platform for in silico metabolic engineering

Patil, K. R., Rocha, I., Förster, J., & Nielsen, J. (2005) · BMC Bioinformatics · doi:10.1186/1471-2105-6-308 · Cited by 455 · see citing works

Metabolic engineering of Saccharomyces cerevisiae: a key cell factory platform for future biorefineries

Hong, K.-K., & Nielsen, J. (2012) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-012-0945-1 · Cited by 451 · see citing works

Assessing the Human Gut Microbiota in Metabolic Diseases

Karlsson, F. H., Tremaroli, V., Nielsen, J., & Bäckhed, F. (2013) · Diabetes · doi:10.2337/db13-0844 · Cited by 450 · see citing works

Integrative model of the response of yeast to osmotic shock

Klipp, E., Nordlander, B., Krüger, R., Gennemark, P., & Hohmann, S. (2005) · Nature Biotechnology · doi:10.1038/nbt1114 · Cited by 447 · see citing works

Microbial production of short-chain alkanes

Choi, Y. J., & Lee, S. Y. (2013) · Nature · doi:10.1038/nature12536 · Cited by 446 · see citing works

The Edinburgh human metabolic network reconstruction and its functional analysis

Ma, H., Sorokin, A., Mazein, A., Selkov, A., Selkov, E., Demin, O., & Goryanin, I. (2007) · Molecular Systems Biology · doi:10.1038/msb4100177 · Cited by 435 · see citing works

Systems Properties of the Haemophilus influenzaeRd Metabolic Genotype

Edwards, J. S., & Palsson, B. Ø. (1999) · Journal of Biological Chemistry · doi:10.1074/jbc.274.25.17410 · Cited by 434 · see citing works

Altered sterol composition renders yeast thermotolerant

Caspeta, L., Chen, Y., Ghiaci, P., Feizi, A., Buskov, S., Hallström, B. M., Petranović, D., & Nielsen, J. (2014) · Science · doi:10.1126/science.1258137 · Cited by 434 · see citing works

Reconstruction of Genome-Scale Active Metabolic Networks for 69 Human Cell Types and 16 Cancer Types Using INIT

Ågren, R., Bordel, S., Mardinoğlu, A., Pornputtapong, N., Nookaew, I., & Nielsen, J. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002518 · Cited by 430 · see citing works

Resveratrol is not a direct activator of SIRT1 enzyme activity

Beher, D., Wu, J., Cumine, S., Kim, K. W., Lu, S., Atangan, L., & Wang, M. (2009) · Chemical Biology &amp; Drug Design · doi:10.1111/j.1747-0285.2009.00901.x · Cited by 426 · see citing works

Production of fatty acid-derived oleochemicals and biofuels by synthetic yeast cell factories

Zhou, Y. J., Buijs, N. A. A., Zhu, Z., Qin, J., Siewers, V., & Nielsen, J. (2016) · Nature Communications · doi:10.1038/ncomms11709 · Cited by 422 · see citing works

The RAVEN Toolbox and Its Use for Generating a Genome-scale Metabolic Model for Penicillium chrysogenum

Ågren, R., Liu, L., Shoaie, S., Vongsangnak, W., Nookaew, I., & Nielsen, J. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002980 · Cited by 418 · see citing works

Quantifying Diet-Induced Metabolic Changes of the Human Gut Microbiome

Shoaie, S., Ghaffari, P., Kovatcheva‐Datchary, P., Mardinoğlu, A., Sen, P., Pujos‐Guillot, E., de Wouters, T., Juste, C., Rizkalla, S. W., Chilloux, J., et al. (2015) · Cell Metabolism · doi:10.1016/j.cmet.2015.07.001 · Cited by 417 · see citing works

Systematic Evaluation of Methods for Integration of Transcriptomic Data into Constraint-Based Models of Metabolism

Machado, D., & Herrgård, M. J. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003580 · Cited by 403 · see citing works

Global metabolite analysis of yeast: evaluation of sample preparation methods

Villas-Bôas, S. G., Højer‐Pedersen, J., Åkesson, M., Smedsgaard, J., & Nielsen, J. (2005) · Yeast · doi:10.1002/yea.1308 · Cited by 399 · see citing works

Caloric restriction, SIRT1 and longevity

Cantó, C., & Auwerx, J. (2009) · Trends in Endocrinology &amp; Metabolism · doi:10.1016/j.tem.2009.03.008 · Cited by 395 · see citing works

The role of TOR in autophagy regulation from yeast to plants and mammals

Díaz‐Troya, S., Pérez‐Pérez, M. E., Florencio, F. J., & Crespo, J. L. (2008) · Autophagy · doi:10.4161/auto.6555 · Cited by 394 · see citing works

Diversion of aspartate in ASS1-deficient tumours fosters de novo pyrimidine synthesis

Rabinovich, S., Adler, L., Yizhak, K., Sarver, A., Silberman, A., Agron, S., Stettner, N., Sun, Q., Brandis, A., Helbling, D., et al. (2015) · Nature · doi:10.1038/nature15529 · Cited by 382 · see citing works

Defining the transcriptome and proteome in three functionally different human cell lines

Lundberg, E., Fagerberg, L., Klevebring, D., Matic, I., Geiger, T., Cox, J., Älgenäs, C., Lundeberg, J., Mann, M., & Uhlen, M. (2010) · Molecular Systems Biology · doi:10.1038/msb.2010.106 · Cited by 373 · see citing works

Identification of anticancer drugs for hepatocellular carcinoma through personalized genome‐scale metabolic modeling

Ågren, R., Mardinoğlu, A., Asplund, A., Kampf, C., Uhlén, M., & Nielsen, J. (2014) · Molecular Systems Biology · doi:10.1002/msb.145122 · Cited by 372 · see citing works

In silico aided metabolic engineering of Saccharomyces cerevisiae for improved bioethanol production

Bro, C., Regenberg, B., FORSTER, J., & Nielsen, J. (2005) · Metabolic Engineering · doi:10.1016/j.ymben.2005.09.007 · Cited by 367 · see citing works

In silico design and adaptive evolution of Escherichia coli for production of lactic acid

Fong, S. S., Burgard, A. P., Herring, C. D., Knight, E. M., Blattner, F. R., Maranas, C. D., & Palsson, B. Ø. (2005) · Biotechnology and Bioengineering · doi:10.1002/bit.20542 · Cited by 363 · see citing works

<i>Saccharomyces cerevisiae</i> phenotypes can be predicted by using constraint-based analysis of a genome-scale reconstructed metabolic network

Famili, I., Förster, J., Nielsen, J., & Palsson, B. O. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2235812100 · Cited by 353 · see citing works

The Cost of Protein Production

Kafri, M., Metzl-Raz, E., Jona, G., & Barkai, N. (2015) · Cell Reports · doi:10.1016/j.celrep.2015.12.015 · Cited by 348 · see citing works

Cardiac Action and Pacemaker Potentials based on the Hodgkin-Huxley Equations

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De novo production of resveratrol from glucose or ethanol by engineered Saccharomyces cerevisiae

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Understanding the interactions between bacteria in the human gut through metabolic modeling

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Dynamic control of gene expression in Saccharomyces cerevisiae engineered for the production of plant sesquitepene α-santalene in a fed-batch mode

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Real-time metabolome profiling of the metabolic switch between starvation and growth

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Non-Enzymatic Glycolysis and Pentose Phosphate Pathway-Like Reactions in a Plausible Archean Ocean

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The next wave in metabolome analysis

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Integration of clinical data with a genome‐scale metabolic model of the human adipocyte

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It Is All about MetabolicFluxes

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Optimization of Ethanol Production in Saccharomyces cerevisiae by Metabolic Engineering of the Ammonium Assimilation

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Integration of gene expression data into genome-scale metabolic models

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Recent advances in reconstruction and applications of genome-scale metabolic models

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Metabolic flux analysis at ultra short time scale: Isotopically non-stationary 13C labeling experiments

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Metabolic model integration of the bibliome, genome, metabolome and reactome of Aspergillus niger

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Metformin Decreases Glucose Oxidation and Increases the Dependency of Prostate Cancer Cells on Reductive Glutamine Metabolism

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Optimizing genome-scale network reconstructions

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Mapping the interaction of Snf1 with TORC1 in Saccharomyces cerevisiae

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Transcriptional regulation is insufficient to explain substrate-induced flux changes in Bacillus subtilis

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Spermidine: A novel autophagy inducer and longevity elixir

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Sampling the solution space in genome-scale metabolic networks reveals transcriptional regulation in key enzymes

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Increasing galactose consumption by Saccharomyces cerevisiae through metabolic engineering of the GAL gene regulatory network

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Modeling cancer metabolism on a genome scale

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Longevity, aging and rapamycin

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Personal model‐assisted identification of NAD+ and glutathione metabolism as intervention target in NAFLD

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Industrial Systems Biology of Saccharomyces cerevisiae Enables Novel Succinic Acid Cell Factory

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Unravelling evolutionary strategies of yeast for improving galactose utilization through integrated systems level analysis

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Impact of systems biology on metabolic engineering ofSaccharomyces cerevisiae

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Pseudo-transition Analysis Identifies the Key Regulators of Dynamic Metabolic Adaptations from Steady-State Data

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13C metabolic flux analysis in complex systems

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Revising the Representation of Fatty Acid, Glycerolipid, and Glycerophospholipid Metabolism in the Consensus Model of Yeast Metabolism

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Towards kinetic modeling of genome‐scale metabolic networks without sacrificing stoichiometric, thermodynamic and physiological constraints

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Transcriptomics resources of human tissues and organs

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Computer model for glucose‐limited growth of a single cell of Escherichia coli B/r‐A

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Integration of metabolome data with metabolic networks reveals reporter reactions

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Metabolic Trade-offs in Yeast are Caused by F1F0-ATP synthase

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Architecture of transcriptional regulatory circuits is knitted over the topology of bio-molecular interaction networks

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Unraveling the complexity of flux regulation: A new method demonstrated for nutrient starvation in Saccharomyces cerevisiae

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A unifying view of 21st century systems biology

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Chromosome 3p loss of heterozygosity is associated with a unique metabolic network in clear cell renal carcinoma

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Improvement of Galactose Uptake in Saccharomyces cerevisiae through Overexpression of Phosphoglucomutase: Example of Transcript Analysis as a Tool in Inverse Metabolic Engineering

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Design of computational retrobiosynthesis tools for the design of de novo synthetic pathways

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Quantitative Analysis of Glycerol Accumulation, Glycolysis and Growth under Hyper Osmotic Stress

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Mathematical Models for Fermentation Processes

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Identification of metabolic model: Citrate production from glucose by Candida lipolytica

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Dynamic metabolic flux analysis (DMFA): A framework for determining fluxes at metabolic non-steady state

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Structural Systems Biology Evaluation of Metabolic Thermotolerance in Escherichia coli

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Proteome- and Transcriptome-Driven Reconstruction of the Human Myocyte Metabolic Network and Its Use for Identification of Markers for Diabetes

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Integrated multilaboratory systems biology reveals differences in protein metabolism between two reference yeast strains

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Reconstruction of the yeast Snf1 kinase regulatory network reveals its role as a global energy regulator

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Probing the performance limits of the Escherichia coli metabolic network subject to gene additions or deletions

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Multiscale Modeling of Metabolism and Macromolecular Synthesis in E. coli and Its Application to the Evolution of Codon Usage

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Version 6 of the consensus yeast metabolic network refines biochemical coverage and improves model performance

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Characterizing Strain Variation in Engineered E. coli Using a Multi-Omics-Based Workflow

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Mapping Yeast Transcriptional Networks

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New paradigms for metabolic modeling of human cells

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Genome-Scale Modeling of the Protein Secretory Machinery in Yeast

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Genome scale models of yeast: towards standardized evaluation and consistent omic integration

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Isolation of a Regulatory Mutant of Fructose-1, 6-diphosphatase in Saccharomyces carlsbergensis

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Yeast cell factories on the horizon

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Are we ready for genome-scale modeling in plants?

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Dynamic phosphoproteomics reveals TORC1-dependent regulation of yeast nucleotide and amino acid biosynthesis

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The roles of galactitol, galactose‐1‐phosphate, and phosphoglucomutase in galactose‐induced toxicity in Saccharomyces cerevisiae

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Unraveling condition‐dependent networks of transcription factors that control metabolic pathway activity in yeast

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Expression of Genes Encoding F 1 -ATPase Results in Uncoupling of Glycolysis from Biomass Production in Lactococcus lactis

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Glycosaminoglycan Profiling in Patients’ Plasma and Urine Predicts the Occurrence of Metastatic Clear Cell Renal Cell Carcinoma

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Quantification of statin effects on hepatic cholesterol synthesis by transient 13C-flux analysis

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Glucose de‐repression by yeast AMP ‐activated protein kinase SNF 1 is controlled via at least two independent steps

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Specific ATP and specific oxygen uptake rate in immobilized cell aggregates: Experimental results and theoretical analysis using a structured model of immobilized cell growth

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Genome-Wide Mapping of Binding Sites Reveals Multiple Biological Functions of the Transcription Factor Cst6p in Saccharomyces cerevisiae

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Modeling the growth and proteinase A production in continuous cultures of recombinant Saccharomyces cerevisiae

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Controllability analysis of transcriptional regulatory networks reveals circular control patterns among transcription factors

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Dynamic¹³C-labeling experiments prove important differences in protein turnover rate between two<i>Saccharomyces cerevisiae</i>strains

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Schizosaccharomyces pombegmd3+/alg11+ is a functional homologue ofSaccharomyces cerevisiaeALG11 which is involved inN-linked oligosaccharide synthesis

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Trehalose-6-phosphate synthase and stabilization of yeast glycolysis

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2023 international olympic committee's (IOC) consensus statement on relative energy deficiency in sport (REDs)

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Systems biology and multi-omics integration: viewpoints from the metabolomics research community

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Pseudomonas putida as a functional chassis for industrial biocatalysis: From native biochemistry to trans-metabolism

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Yeast Systems Biology: Model Organism and Cell Factory

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Drosophila as a model for ageing

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The Potential of Metabolomics in Biomedical Applications

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Recent advances in systems and synthetic biology approaches for developing novel cell-factories in non-conventional yeasts

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Metabolic Heterogeneity of Cancer Cells: An Interplay between HIF-1, GLUTs, and AMPK

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Metabolomics Signatures in Type 2 Diabetes: A Systematic Review and Integrative Analysis

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Metabolic control of nitrogen fixation in rhizobium-legume symbioses

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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 127 · see citing works

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 · see citing works

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 120 · see citing works

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 113 · see citing works

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 118 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 79 · see citing works

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 80 · see citing works

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 · see citing works

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 · see citing works

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 68 · see citing works

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 65 · see citing works

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 66 · see citing works

Building Synthetic Cells—From the Technology Infrastructure to Cellular Entities

Rothschild, L., Averesch, N., Strychalski, E., Moser, F., Glass, J. I., Perez, R. C., 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 62 · see citing works

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 62 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 54 · see citing works

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 · see citing works

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 52 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 46 · see citing works

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 44 · see citing works

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 45 · see citing works

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 55 · see citing works

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 · see citing works

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 · see citing works

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 43 · see citing works

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 40 · see citing works

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 41 · see citing works

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 39 · see citing works

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 40 · see citing works

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 · see citing works

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 38 · see citing works

On the evolution of coenzyme biosynthesis

Kirschning, A. (2022) · Natural Product Reports · doi:10.1039/d2np00037g · Cited by 37 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 37 · see citing works

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 34 · see citing works

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 · see citing works

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 34 · see citing works

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 34 · see citing works

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 33 · see citing works

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 · see citing works

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 · see citing works

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 33 · see citing works

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 30 · see citing works

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 · see citing works

Oral Sources of Salivary Metabolites

Hyvärinen, E., Kashyap, B., & Kullaa, A. M. (2023) · Metabolites · doi:10.3390/metabo13040498 · Cited by 30 · see citing works

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 · see citing works

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., & Bae, S. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22136932 · Cited by 19 · see citing works

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 26 · see citing works

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 25 · see citing works

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 · see citing works

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 · see citing works

Intersection of Immunology and Metabolism in Myocardial Disease

Thorp, E. B., & Karlstaedt, A. (2024) · Circulation Research · doi:10.1161/circresaha.124.323660 · Cited by 25 · see citing works

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 · see citing works

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 21 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 22 · see citing works

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 · see citing works

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 · see citing works

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 21 · see citing works

Metabolism along the life journey of T cells

Peng, M., & Li, M. O. (2023) · Life Metabolism · doi:10.1093/lifemeta/load002 · Cited by 20 · see citing works

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 · see citing works

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 18 · see citing works

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 · see citing works

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 18 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 16 · see citing works

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 15 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

Evaluating accessibility, usability and interoperability of genome-scale metabolic models for diverse yeasts species

Domenzain, I., Li, F.-R., Kerkhoven, E., & Siewers, V. (2021) · FEMS Yeast Research · doi:10.1093/femsyr/foab002 · Cited by 12 · see citing works

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 · see citing works

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 · see citing works

Background

(2015) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9781681080864115010004

Thermodynamics-based Significance Ranking of Candidates for Metabolomics

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