Multiplex methods provide effective integration of multi-omic data in genome-scale models

BMC Bioinformatics

Volumn 17, Issue 4, 2016, Pages

  Angione, Claudio ^a   Conway, Max ^b   Lió, Pietro ^b  

^a TEESSIDE UNIVERSITY   (United Kingdom)

^b UNIVERSITY OF CAMBRIDGE   (United Kingdom)

Author keywords

Flux balance analysis; Fluxomics; Genome scale models; Multi omics; Multiplex networks; Network fusion; Transcriptomics

Indexed keywords

ASSOCIATION REACTIONS; ESCHERICHIA COLI; GENES; METABOLISM; SCALES (WEIGHING INSTRUMENTS);

FLUX BALANCE ANALYSIS; FLUXOMICS; GENOME-SCALE MODEL; MULTI-OMICS; MULTIPLEX NETWORKS; NETWORK FUSION; TRANSCRIPTOMICS;

NETWORK LAYERS;

ESCHERICHIA COLI; EXPERIMENTAL MODEL; GENOME; METABOLISM; SPECIES; TRANSCRIPTOMICS; ADAPTATION; BACTERIAL GENOME; BIOLOGICAL MODEL; ENVIRONMENT; GENE REGULATORY NETWORK; GENETICS; GENOMICS; PROCEDURES; SYSTEMS BIOLOGY;

BACTERIAL PROTEIN;

ADAPTATION, PHYSIOLOGICAL; BACTERIAL PROTEINS; ENVIRONMENT; ESCHERICHIA COLI; GENE REGULATORY NETWORKS; GENOME, BACTERIAL; GENOMICS; METABOLIC NETWORKS AND PATHWAYS; MODELS, BIOLOGICAL; SYSTEMS BIOLOGY;

EID: 84978924623     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/s12859-016-0912-1     Document Type: Article

References (45)

1. Chalise P, Koestler DC, Bimali M, Yu Q, Fridley BL. Integrative clustering methods for high-dimensional molecular data. Transl Cancer Res. 2014; 3(3):202.
2. Chindelevitch L, Trigg J, Regev A, Berger B. An exact arithmetic toolbox for a consistent and reproducible structural analysis of metabolic network models. Nat Commun. 2014; 5:4893.
3. Saha R, Chowdhury A, Maranas CD. Recent advances in the reconstruction of metabolic models and integration of omics data. Curr Opin Biotechnol. 2014; 29:39-45.
4. Bordbar A, Monk JM, King ZA, Palsson BO. Constraint-based models predict metabolic and associated cellular functions. Nat Rev Genet. 2014; 15(2):107-20.
5. Faith JJ, Hayete B, Thaden JT, Mogno I, Wierzbowski J, Cottarel G, et al.Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles. PLoS Biol. 2007; 5(1):8.
6. Shen R, Olshen AB, Ladanyi M. Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis. Bioinformatics. 2009; 25(22):2906-12.
7. Wang B, Mezlini AM, Demir F, Fiume M, Tu Z, Brudno M, et al. Similarity network fusion for aggregating data types on a genomic scale. Nat Methods. 2014; 11(3):333-7.
8. Orth JD, Conrad TM, Na J, Lerman JA, Nam H, Feist AM, et al.A comprehensive genome-scale reconstruction of Escherichia coli metabolism. Mol Syst Biol. 2011; 7(1):535.
9. Notebaart RA, Szappanos B, Kintses B, Pál F, Györkei Á, Bogos B, et al.Network-level architecture and the evolutionary potential of underground metabolism. Proc Natl Acad Sci. 2014; 111(32):11762-7.
10. Guzmán GI, Utrilla J, Nurk S, Brunk E, Monk JM, Ebrahim A, et al.Model-driven discovery of underground metabolic functions in escherichia coli. Proc Natl Acad Sci. 2015; 112(3):929-34.
11. Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller W, et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 1997; 25(17):3389-402.
12. Palsson BØ. Systems biology: Constraint-based reconstruction and analysis: Cambridge University Press; 2015.
13. Machado D, Herrgård M. Systematic evaluation of methods for integration of transcriptomic data into constraint-based models of metabolism. PLoS Comput Biol. 2014; 10:1003580.
14. Angione C, Costanza J, Carapezza G, Lió P, Nicosia G. A design automation framework for computational bioenergetics in biological networks. Mol BioSyst. 2013; 9(10):2554-64.
15. Angione C, Lió P. Predictive analytics of environmental adaptability in multi-omic network models. Sci Rep. 2015; 5:15147.
16. Angione C, Pratanwanich N, Lió P. A hybrid of metabolic flux analysis and bayesian factor modeling for multi-omics temporal pathway activation. ACS Synth Biol. 2015; 4(8):880-9.
17. Durot M, Bourguignon PY, Schachter V. Genome-scale models of bacterial metabolism: reconstruction and applications. FEMS Microbiol Rev. 2009; 33(1):164-90.
18. Mar JC, Matigian NA, Mackay-Sim A, Mellick GD, Sue CM, Silburn PA, et al. Variance of gene expression identifies altered network constraints in neurological disease. PLoS Genet. 2011; 7(8):1002207.
19. Pál C, Papp B, Hurst LD. Highly expressed genes in yeast evolve slowly. Genetics. 2001; 158(2):927-31.
20. Fraser HB, Hirsh AE, Steinmetz LM, Scharfe C, Feldman MW. Evolutionary rate in the protein interaction network. Science. 2002; 296(5568):750-2.
21. Paltanea M, Tabirca S, Scheiber E, Tangney M. Logarithmic growth in biological processes. In: Computer Modelling and Simulation (UKSim), 2010 12th International Conference On. IEEE: 2010. p. 116-21.
22. Guimaraes JC, Rocha M, Arkin AP. Transcript level and sequence determinants of protein abundance and noise in Escherichia coli. Nucleic Acids Res. 2014; 42(8):4791-9.
23. Macfarlane R. An enzyme cascade in the blood clotting mechanism, and its function as a biochemical amplifier: Nature Publishing Group; 1964.
24. Nishida E, Gotoh Y. The map kinase cascade is essential for diverse signal transduction pathways. Trends Biochem Sci. 1993; 18(4):128-31.
25. Firczuk H, Kannambath S, Pahle J, Claydon A, Beynon R, Duncan J, et al. An in vivo control map for the eukaryotic mRNA translation machinery. Mol Syst Biol. 2013; 9(1):635.
26. Shimizu K. Metabolic flux analysis based on 13c-labeling experiments and integration of the information with gene and protein expression patterns. Adv Biochem Eng Biotechnol. 2004; 91:1.
27. Peng L, Shimizu K. Global metabolic regulation analysis for Escherichia coli k12 based on protein expression by 2-dimensional electrophoresis and enzyme activity measurement. Appl Microbiol Biotechnol. 2003; 61(2):163-78.
28. Angione C, Carapezza G, Costanza J, Lió P, Nicosia G. Pareto optimality in organelle energy metabolism analysis. Comput Biol Bioinformatics IEEE/ACM Trans. 2013; 10(4):1032-44.
29. Estrada E, Gómez-Gardeñes J. Communicability reveals a transition to coordinated behavior in multiplex networks. Phys Rev E. 2014; 89(4):042819.
30. Nicosia V, Bianconi G, Latora V, Barthelemy M. Growing multiplex networks. Phys Rev Lett. 2013; 111(5):058701.
31. Arias CF, Catalán P, Manrubia S, Cuesta JA. toyLIFE: a computational framework to study the multi-level organisation of the genotype-phenotype map. Sci Rep. 2014; 4:7549. [doi: 10.1038/srep07549].
32. Fong SS, Joyce AR, Palsson BØ. Parallel adaptive evolution cultures of Escherichia coli lead to convergent growth phenotypes with different gene expression states. Genome Res. 2005; 15(10):1365-72.
33. Birch EW, Udell M, Covert MW. Incorporation of flexible objectives and time-linked simulation with flux balance analysis. J Theor Biol. 2014; 345:12-21.
34. Schuetz R, Zamboni N, Zampieri M, Heinemann M, Sauer U. Multidimensional optimality of microbial metabolism. Science. 2012; 336(6081):601-4.
35. Zakrzewski P, Medema MH, Gevorgyan A, Kierzek AM, Breitling R, Takano E. Multimeteval: comparative and multi-objective analysis of genome-scale metabolic models. PloS ONE. 2012; 7(12):51511.
36. Meysman P, Sonego P, Bianco L, Fu Q, Ledezma-Tejeida D, Gama-Castro S, et al. Colombos v2. 0: an ever expanding collection of bacterial expression compendia. Nucleic Acids Res. 2014; 42(D1):649-53.
37. Hui S, Silverman JM, Chen SS, Erickson DW, Basan M, Wang J, et al. Quantitative proteomic analysis reveals a simple strategy of global resource allocation in bacteria. Mol Syst Biol. 2015; 11(2):784.
38. Brandes A, Lun DS, Ip K, Zucker J, Colijn C, Weiner B, et al. Inferring carbon sources from gene expression profiles using metabolic flux models. PloS ONE. 2012; 7(5):36947.
39. Ferrell JE. Feedback regulation of opposing enzymes generates robust, all-or-none bistable responses. Curr Biol. 2008; 18(6):244-5.
40. Angione C, Conway M, Lió P. Spectral clustering performed on the fused network. https://github.com/maxconway/supplementarydata/blob/master/BMCbioinformatics-2016/Fused_similarities.csv.
41. Therneau T, Atkinson B, Ripley B. Rpart: Recursive Partitioning and Regression Trees. 2015. R package version 4.1-9. http://CRAN.R-project.org/package=rpart.
42. Castellani G, Intrator N, Remondini D. Systems biology and brain activity in neuronal pathways by smart device and advanced signal processing. Front Genet. 2014; 5:253.
43. Angione C, Costanza J, Carapezza G, Lió P, Nicosia G. Analysis and design of molecular machines. Theor Comput Sci. 2015; 599:102-17.
44. Boccaletti S, Bianconi G, Criado R, Del Genio C, Gómez-Gardeñes J, Romance M, et al. The structure and dynamics of multilayer networks. Phys Rep. 2014; 544(1):1-122.
45. Moni MA, Liò P. Network-based analysis of comorbidities risk during an infection: Sars and hiv case studies. BMC Bioinformatics. 2014; 15(1):333.