Metabolic Models of Protein Allocation Call for the Kinetome

Cell Systems

Volumn 5, Issue 6, 2017, Pages 538-541

  Nilsson, Avlant ^a   Nielsen, Jens ^a,b   Palsson, Bernhard O ^b,c  

^a CHALMERS UNIVERSITY OF TECHNOLOGY   (Sweden)

^b Chalmers University of Technology   (Denmark)

^c UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ENZYME; PROTEIN;

AMINO ACID SEQUENCE; BACTERIAL GROWTH; BACTERIAL VIABILITY; BOOTSTRAPPING; CELL FUNCTION; ENZYME KINETICS; ENZYME SUBSTRATE; ESCHERICHIA COLI; GENOME; GENOME SCALE METABOLIC MODEL; GLYCOLYSIS; GROWTH RATE; HUMAN; IN VITRO STUDY; KINETIC PARAMETERS; METABOLIC PARAMETERS; METABOLOMICS; MICHAELIS CONSTANT; MYCOPLASMA GENITALIUM; NONHUMAN; NOTE; PH; PRIORITY JOURNAL; PROTEIN METABOLISM; PROTEIN PHOSPHORYLATION; SIMULATION; SYSTEMS BIOLOGY; ANIMAL; BIOLOGICAL MODEL; BIOLOGY; COMPUTER SIMULATION; ENZYME ACTIVATION; EVOLUTION; KINETICS; METABOLISM; THEORETICAL MODEL; WHOLE GENOME SEQUENCING;

ANIMALS; BIOLOGICAL EVOLUTION; COMPUTATIONAL BIOLOGY; COMPUTER SIMULATION; ENZYME ACTIVATION; HUMANS; KINETICS; MODELS, BIOLOGICAL; MODELS, THEORETICAL; PROTEINS; WHOLE GENOME SEQUENCING;

EID: 85042407052     PISSN: 24054712     EISSN: 24054720     Source Type: Journal    
DOI: 10.1016/j.cels.2017.11.013     Document Type: Note

References (29)

1. Adadi, R., Volkmer, B., Milo, R., Heinemann, M., Shlomi, T., Prediction of microbial growth rate versus biomass yield by a metabolic network with kinetic parameters. PLoS Comput. Biol., 8, 2012, e1002575.
2. Bar-Even, A., Noor, E., Savir, Y., Liebermeister, W., Davidi, D., Tawfik, D.S., Milo, R., The moderately efficient enzyme: evolutionary and physicochemical trends shaping enzyme parameters. Biochemistry 50 (2011), 4402–4410.
3. Beg, Q.K., Vazquez, A., Ernst, J., de Menezes, M.A., Bar-Joseph, Z., Barabási, A.L., Oltvai, Z.N., Intracellular crowding defines the mode and sequence of substrate uptake by Escherichia coli and constrains its metabolic activity. Proc. Natl. Acad. Sci. USA 104 (2007), 12663–12668.
4. Brunk, E., Mih, N., Monk, J., Zhang, Z., O'Brien, E.J., Bliven, S.E., Chen, K., Chang, R.L., Bourne, P.E., Palsson, B.O., Systems biology of the structural proteome. BMC Syst. Biol., 10, 2016, 26.
5. Burkhardt, T., Kaufmann, C.M., Letzel, T., Grassmann, J., Enzymatic assays coupled with mass spectrometry with or without embedded liquid chromatography. ChemBioChem 16 (2015), 1985–1992.
6. Chang, R.L., Andrews, K., Kim, D., Li, Z., Godzik, A., Palsson, B.O., Structural systems biology evaluation of metabolic thermotolerance in Escherichia coli. Science 340 (2013), 1220–1223.
7. Davidi, D., Noor, E., Liebermeister, W., Bar-Even, A., Flamholz, A., Tummler, K., Barenholz, U., Goldenfeld, M., Shlomi, T., Milo, R., Global characterization of in vivo enzyme catalytic rates and their correspondence to in vitro kcat measurements. Proc. Natl. Acad. Sci. USA 113 (2016), 3401–3406.
8. Edwards, J.S., Palsson, B.O., Systems properties of the Haemophilus influenzae Rd metabolic genotype. J. Biol. Chem. 274 (1999), 17410–17416.
9. Engqvist, M.K.M., Highlighting the need for systems-level experimental characterization of plant metabolic enzymes. Front. Plant Sci., 7, 2016, 1127.
10. Förster, J., Famili, I., Fu, P., Palsson, B.Ø., Nielsen, J., Genome-scale reconstruction of the Saccharomyces cerevisiae metabolic network. Genome Res. 13 (2003), 244–253.
11. Goelzer, A., Muntel, J., Chubukov, V., Jules, M., Prestel, E., Nölker, R., Mariadassou, M., Aymerich, S., Hecker, M., Noirot, P., et al. Quantitative prediction of genome-wide resource allocation in bacteria. Metab. Eng. 32 (2015), 232–243.
12. Karr, J.R., Sanghvi, J.C., Macklin, D.N., Gutschow, M.V., Jacobs, J.M., Bolival, B. Jr., Assad-Garcia, N., Glass, J.I., Covert, M.W., A whole-cell computational model predicts phenotype from genotype. Cell 150 (2012), 389–401.
13. Klumpp, S., Scott, M., Pedersen, S., Hwa, T., Molecular crowding limits translation and cell growth. Proc. Natl. Acad. Sci. USA 110 (2013), 16754–16759.
14. Labhsetwar, P., Cole, J.A., Roberts, E., Price, N.D., Luthey-Schulten, Z.A., Heterogeneity in protein expression induces metabolic variability in a modeled Escherichia coli population. Proc. Natl. Acad. Sci. USA 110 (2013), 14006–14011.
15. Lewis, N.E., Nagarajan, H., Palsson, B.O., Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods. Nat. Rev. Microbiol. 10 (2012), 291–305.
16. Molenaar, D., van Berlo, R., de Ridder, D., Teusink, B., Shifts in growth strategies reflect tradeoffs in cellular economics. Mol Syst Biol., 5, 2009, 323.
17. Nilsson, A., Nielsen, J., Genome scale metabolic modeling of cancer. Metabolic Engineering., 43, 2017, 103–112.
18. Nilsson, A., Nielsen, J., Metabolic trade-offs in yeast are caused by F1F0-ATP synthase. Sci. Rep., 6, 2016, 22264.
19. Nilsson, A., Mardinoglu, A., Nielsen, J., Predicting growth of the healthy infant using a genome scale metabolic model. NPJ Syst Biol Appl., 3, 2017, 3.
20. Noor, E., Flamholz, A., Bar-Even, A., Davidi, D., Milo, R., Liebermeister, W., The protein cost of metabolic fluxes: prediction from enzymatic rate laws and cost minimization. PLoS Comput. Biol., 12, 2016, e1005167.
21. O'Brien, E.J., Palsson, B.O., Computing the functional proteome: recent progress and future prospects for genome-scale models. Curr. Opin. Biotechnol. 34 (2015), 125–134.
22. Peebo, K., Valgepea, K., Maser, A., Nahku, R., Adamberg, K., Vilu, R., Proteome reallocation in Escherichia coli with increasing specific growth rate. Mol. Biosyst. 11 (2015), 1184–1193.
23. Sánchez, B.J., Zhang, C., Nilsson, A., Lahtvee, P.J., Kerkhoven, E.J., Nielsen, J., Improving the phenotype predictions of a yeast genome-scale metabolic model by incorporating enzymatic constraints. Mol. Syst. Biol., 13, 2017, 935.
24. Sanghvi, J.C., Regot, S., Carrasco, S., Karr, J.R., Gutschow, M.V., Bolival, B. Jr., Covert, M.W., Accelerated discovery via a whole-cell model. Nat. Methods 10 (2013), 1192–1195.
25. Savir, Y., Noor, E., Milo, R., Tlusty, T., Cross-species analysis traces adaptation of Rubisco toward optimality in a low-dimensional landscape. Proc. Natl. Acad. Sci. USA 107 (2010), 3475–3480.
26. Schomburg, I., Chang, A., Placzek, S., Söhngen, C., Rother, M., Lang, M., Munaretto, C., Ulas, S., Stelzer, M., Grote, A., et al. BRENDA in 2013: integrated reactions, kinetic data, enzyme function data, improved disease classification: new options and contents in BRENDA. Nucleic Acids Res. 41 (2013), D764–D772.
27. van Eunen, K., Bouwman, J., Daran-Lapujade, P., Postmus, J., Canelas, A.B., Mensonides, F.I., Orij, R., Tuzun, I., van den Brink, J., Smits, G.J., et al. Measuring enzyme activities under standardized in vivo-like conditions for systems biology. FEBS J. 277 (2010), 749–760.
28. van Hoek, M.J., Merks, R.M., Redox balance is key to explaining full vs. partial switching to low-yield metabolism. BMC Syst. Biol., 6, 2012, 22.
29. Zhuang, K., Vemuri, G.N., Mahadevan, R., Economics of membrane occupancy and respiro-fermentation. Mol. Syst. Biol., 7, 2011, 500.