Improving prediction fidelity of cellular metabolism with kinetic descriptions

Current Opinion in Biotechnology

Volumn 36, Issue , 2015, Pages 57-64

  Chowdhury, Anupam ^a   Khodayari, Ali ^a   Maranas, Costas D ^a  

^a The Pennsylvania State University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

KINETICS; METABOLISM; PHYSIOLOGY;

BIOLOGICAL PROCESS; CELLULAR METABOLISM; ENGINEERING TECHNIQUES; HIGH-THROUGHPUT DATA; KINETIC DESCRIPTION; KINETIC INFORMATION; MODELING TECHNIQUE; UNIFIED FRAMEWORK;

METABOLIC ENGINEERING;

CELL PROTEIN;

ACCURACY; CELL ASSAY; CELL ENGINEERING; CELL FUNCTION; CELL LEVEL; CELL METABOLISM; HUMAN; KINETICS; METABOLIC REGULATION; MOLECULAR DYNAMICS; MOLECULAR MODEL; PHENOTYPE; PREDICTION; PRIORITY JOURNAL; PROCESS DESIGN; PROTEIN DETERMINATION; PROTEIN FUNCTION; PROTEIN PROCESSING; REVIEW; BIOLOGICAL MODEL; METABOLIC ENGINEERING; PROCEDURES;

KINETICS; METABOLIC ENGINEERING; MODELS, BIOLOGICAL; PHENOTYPE;

EID: 84940048571     PISSN: 09581669     EISSN: 18790429     Source Type: Journal    
DOI: 10.1016/j.copbio.2015.08.011     Document Type: Review

References (65)

1. Royce L.A., Yoon J.M., Chen Y., Rickenbach E., Shanks J.V., Jarboe L.R. Evolution for exogenous octanoic acid tolerance improves carboxylic acid production and membrane integrity. Metab Eng 2015, 29:180-188.
2. Link H., Christodoulou D., Sauer U. Advancing metabolic models with kinetic information. Curr Opin Biotechnol 2014, 29:8-14.
3. van Eunen K., Kiewiet J.A., Westerhoff H.V., Bakker B.M. Testing biochemistry revisited: how in vivo metabolism can be understood from in vitro enzyme kinetics. PLoS Comput Biol 2012, 8:e1002483.
4. Wang L., Birol I., Hatzimanikatis V. Metabolic control analysis under uncertainty: framework development and case studies. Biophys J 2004, 87:3750-3763.
5. Visser D., Heijnen J.J. Dynamic simulation and metabolic re-design of a branched pathway using linlog kinetics. Metab Eng 2003, 5:164-176.
6. Sorribas A., Hernandez-Bermejo B., Vilaprinyo E., Alves R. Cooperativity and saturation in biochemical networks: a saturable formalism using Taylor series approximations. Biotechnol Bioeng 2007, 97:1259-1277.
7. Nielsen J. Metabolic control analysis of biochemical pathways based on a thermokinetic description of reaction rates. Biochem J 1997, 321(Pt 1):133-138.
8. Liebermeister W., Klipp E. Bringing metabolic networks to life: convenience rate law and thermodynamic constraints. Theor Biol Med Model 2006, 3:41.
9. Almquist J., Cvijovic M., Hatzimanikatis V., Nielsen J., Jirstrand M. Kinetic models in industrial biotechnology - improving cell factory performance. Metab Eng 2014, 24:38-60.
10. Tan Y., Liao J.C. Metabolic ensemble modeling for strain engineers. Biotechnol J 2012, 7:343-353.
11. Chakrabarti A., Miskovic L., Soh K.C., Hatzimanikatis V. Towards kinetic modeling of genome-scale metabolic networks without sacrificing stoichiometric, thermodynamic and physiological constraints. Biotechnol J 2013, 8:1043-1057.
12. Khodayari A., Zomorrodi A.R., Liao J.C., Maranas C.D. A kinetic model of Escherichia coli core metabolism satisfying multiple sets of mutant flux data. Metab Eng 2014, 25:50-62.
13. Khodayari A., Chowdhury A., Maranas C.D. Succinate overproduction: A case study of computational strain design using a comprehensive Escherichia Coli kinetic model. Front Bioeng Biotechnol 2014, 2:76.
14. Marbach D., Costello J.C., Kuffner R., Vega N.M., Prill R.J., Camacho D.M., Allison K.R., Consortium D., Kellis M., Collins J.J., et al. Wisdom of crowds for robust gene network inference. Nat Methods 2012, 9:796-804.
15. Hyduke D.R., Lewis N.E., Palsson B.O. Analysis of omics data with genome-scale models of metabolism. Mol bioSystems 2013, 9:167-174.
16. Kotte O., Zaugg J.B., Heinemann M. Bacterial adaptation through distributed sensing of metabolic fluxes. Mol Systems Biol 2010, 6:355.
17. De Smet R., Marchal K. Advantages and limitations of current network inference methods. Nat Rev Microbiol 2010, 8:717-729.
18. Brooks A.N., Reiss D.J., Allard A., Wu W.J., Salvanha D.M., Plaisier C.L., Chandrasekaran S., Pan M., Kaur A., Baliga N.S. A system-level model for the microbial regulatory genome. Mol Systems Biol 2014, 10:740.
19. Fendt S.M., Oliveira A.P., Christen S., Picotti P., Dechant R.C., Sauer U. Unraveling condition-dependent networks of transcription factors that control metabolic pathway activity in yeast. Mol Syst Biol 2010, 6:432.
20. Haverkorn van Rijsewijk B.R., Nanchen A., Nallet S., Kleijn R.J., Sauer U. Large-scale 13C-flux analysis reveals distinct transcriptional control of respiratory and fermentative metabolism in Escherichia coli. Mol Syst Biol 2011, 7:477.
21. Batey R.T. Riboswitches: still a lot of undiscovered country. Rna 2015, 21:560-563.
22. Updegrove T.B., Shabalina S.A., Storz G. How do base-pairing small RNAs evolve?. FEMS Microbiol Rev 2015, 39:379-391.
23. Morris K.V., Mattick J.S. The rise of regulatory RNA. Nat Rev Genet 2014, 15:423-437.
24. Kochanowski K., Sauer U., Noor E. Posttranslational regulation of microbial metabolism. Curr Opin Microbiol 2015, 27:10-17.
25. Gersch M., Hackl M.W., Dubiella C., Dobrinevski A., Groll M., Sieber S.A. A mass spectrometry platform for a streamlined investigation of proteasome integrity, posttranslational modifications, and inhibitor binding. Chem Biol 2015, 22:404-411.
26. Strumillo M., Beltrao P. Towards the computational design of protein post-translational regulation. Bioorg Med Chem 2015, 23:2877-2882.
27. Kerkhofs J., Geris L. A semiquantitative framework for gene regulatory networks: increasing the time and quantitative resolution of boolean networks. PLoS One 2015, 10:e0130033.
28. Blazier A.S., Papin J.A. Integration of expression data in genome-scale metabolic network reconstructions. Front Physiol 2012, 3:299.
29. Cotten C., Reed J.L. Mechanistic analysis of multi-omics datasets to generate kinetic parameters for constraint-based metabolic models. BMC Bioinform 2013, 14:32.
30. Machado D., Herrgard M. Systematic evaluation of methods for integration of transcriptomic data into constraint-based models of metabolism. PLoS Comput Biol 2014, 10:e1003580.
31. Saa P., Nielsen L.K. A general framework for thermodynamically consistent parameterization and efficient sampling of enzymatic reactions. PLoS Comput Biol 2015, 11:e1004195.
32. Rybakova K.N., Tomaszewska A., van Mourik S., Blom J., Westerhoff H.V., Carlberg C., Bruggeman F.J. Tracing the molecular basis of transcriptional dynamics in noisy data by using an experiment-based mathematical model. Nucleic Acids Res 2015, 43:153-161.
33. Espah Borujeni A., Channarasappa A.S., Salis H.M. Translation rate is controlled by coupled trade-offs between site accessibility, selective RNA unfolding and sliding at upstream standby sites. Nucleic Acids Res 2014, 42:2646-2659.
34. 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. A whole-cell computational model predicts phenotype from genotype. Cell 2012, 150:389-401.
35. O'Brien E.J., Palsson B.O. Computing the functional proteome: recent progress and future prospects for genome-scale models. Curr Opin Biotechnol 2015, 34C:125-134.
36. Carrera J., Estrela R., Luo J., Rai N., Tsoukalas A., Tagkopoulos I. An integrative, multi-scale, genome-wide model reveals the phenotypic landscape of Escherichia coli. Mol Syst Biol 2014, 10:735.
37. Zomorrodi A.R., Suthers P.F., Ranganathan S., Maranas C.D. Mathematical optimization applications in metabolic networks. Metab Eng 2012, 14:672-686.
38. Schomburg I., Chang A., Placzek S., Sohngen 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 2013, 41(Database issue):D764-D772.
39. Zomorrodi A.R., Lafontaine Rivera J.G., Liao J.C., Maranas C.D. Optimization-driven identification of genetic perturbations accelerates the convergence of model parameters in ensemble modeling of metabolic networks. Biotechnol J 2013, 8:1090-1104.
40. King Z.A., Lloyd C.J., Feist A.M., Palsson B.O. Next-generation genome-scale models for metabolic engineering. Curr Opin Biotechnol 2015, 35C:23-29.
41. Cintolesi A., Clomburg J.M., Rigou V., Zygourakis K., Gonzalez R. Quantitative analysis of the fermentative metabolism of glycerol in Escherichia coli. Biotechnol Bioeng 2012, 109:187-198.
42. Machado D., Rodrigues L.R., Rocha I. A kinetic model for curcumin production in Escherichia coli. Bio Systems 2014, 125:16-21.
43. Costa R.S., Hartmann A., Gaspar P., Neves A.R., Vinga S. An extended dynamic model of Lactococcus lactis metabolism for mannitol and 2,3-butanediol production. Mol bioSyst 2014, 10:628-639.
44. Rutkis R., Kalnenieks U., Stalidzans E., Fell D.A. Kinetic modelling of the Zymomonas mobilis Entner-Doudoroff pathway: insights into control and functionality. Microbiology 2013, 159(Pt 12):2674-2689.
45. Sowa S.W., Baldea M., Contreras L.M. Optimizing metabolite production using periodic oscillations. PLoS Comput Biol 2014, 10:e1003658.
46. Weaver L.J., Sousa M.M., Wang G., Baidoo E., Petzold C.J., Keasling J.D. A kinetic-based approach to understanding heterologous mevalonate pathway function in E. coli. Biotechnol Bioeng 2015, 112:111-119.
47. Farasat I., Kushwaha M., Collens J., Easterbrook M., Guido M., Salis H.M. Efficient search, mapping, and optimization of multi-protein genetic systems in diverse bacteria. Mol Syst Biol 2014, 10:731.
48. Salis H.M. The ribosome binding site calculator. Methods Enzymol 2011, 498:19-42.
49. Rollin J.A., Martin del Campo J., Myung S., Sun F., You C., Bakovic A., Castro R., Chandrayan S.K., Wu C.H., Adams M.W., et al. High-yield hydrogen production from biomass by in vitro metabolic engineering: Mixed sugars coutilization and kinetic modeling. Proc Natl Acad Sci USA 2015, 112:4964-4969.
50. Zhuang K., Yang L., Cluett W.R., Mahadevan R. Dynamic strain scanning optimization: an efficient strain design strategy for balanced yield, titer, and productivity. DySScO strategy for strain design. BMC Biotechnol 2013, 13:8.
51. Zhuang K., Izallalen M., Mouser P., Richter H., Risso C., Mahadevan R., Lovley D.R. Genome-scale dynamic modeling of the competition between Rhodoferax and Geobacter in anoxic subsurface environments. ISME J 2011, 5:305-316.
52. Cautha S.C., Gowen C.M., Lussier F., Gold N.D., Martin V.J., Mahadevan R. Model-driven design of a Saccharomyces cerevisiae platform strain with improved tyrosine production capabilities. Comput Appl Biotechnol 2013, 221-226.
53. Chowdhury A., Zomorrodi A.R., Maranas C.D. Bilevel optimization techniques in computational strain design. Computers Chem Eng 2015, 72:363-372.
54. Lun D.S., Rockwell G., Guido N.J., Baym M., Kelner J.A., Berger B., Galagan J.E., Church G.M. Large-scale identification of genetic design strategies using local search. Mol Syst Biol 2009, 5:296.
55. Chowdhury A., Zomorrodi A.R., Maranas C.D. k-OptForce: integrating kinetics with flux balance analysis for strain design. PLoS Comput Biol 2014, 10:e1003487.
56. Pozo C., Miró A., Guillén-Gosálbez G., Sorribas A., Alves R., Jiménez L. Gobal optimization of hybrid kinetic/FBA models via outer-approximation. Comput Chem Eng 2015, 72:325-333.
57. Stanford N.J., Lubitz T., Smallbone K., Klipp E., Mendes P., Liebermeister W. Systematic construction of kinetic models from genome-scale metabolic networks. PLoS One 2013, 8:e79195.
58. Crown S.B., Long C.P., Antoniewicz M.R. Integrated 13C-metabolic flux analysis of 14 parallel labeling experiments in Escherichia coli. Metab Eng 2015, 28:151-158.
59. Qi L.S., Larson M.H., Gilbert L.A., Doudna J.A., Weissman J.S., Arkin A.P., Lim W.A. Repurposing CRISPR as an RNA-guided platform for sequence-specific control of gene expression. Cell 2013, 152:1173-1183.
60. Petryszak R., Burdett T., Fiorelli B., Fonseca N.A., Gonzalez-Porta M., Hastings E., Huber W., Jupp S., Keays M., Kryvych N., et al. Expression Atlas update - a database of gene and transcript expression from microarray- and sequencing-based functional genomics experiments. Nucleic acids Res 2014, 42(Database issue):D926-D932.
61. Agarwal S., Yu H., Kohane I. BioNOT: a searchable database of biomedical negated sentences. BMC Bioinformat 2011, 12:420.
62. Blohm P., Frishman G., Smialowski P., Goebels F., Wachinger B., Ruepp A., Frishman D. Negatome 2.0: a database of non-interacting proteins derived by literature mining, manual annotation and protein structure analysis. Nucleic Acids Res 2014, 42(Database issue):D396-D400.
63. Yu X., Liu T., Zhu F., Khosla C. In vitro reconstitution and steady-state analysis of the fatty acid synthase from Escherichia coli. Proc Natl Acad Sci USA 2011, 108:18643-18648.
64. Morozov A.V., Siggia E.D. Connecting protein structure with predictions of regulatory sites. Proc Natl Acad Sci USA 2007, 104:7068-7073.
65. Bader G.D., Cary M.P., Sander C. Pathguide: a pathway resource list. Nucleic acids Res 2006, 34(Database issue):D504-D506.