Optimization of multi-omic genome-scale models: Methodologies, hands-on tutorial, and perspectives

Methods in Molecular Biology

Volumn 1716, Issue , 2018, Pages 389-408

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

^a TEESSIDE UNIVERSITY   (United Kingdom)

^b UNIVERSITY OF CAMBRIDGE   (United Kingdom)

Author keywords

Data integration; Flux balance analysis; Machine learning; Metabolic models; Multi objective optimization; Multi omics

Indexed keywords

BACTERIAL GROWTH; BACTERIAL METABOLISM; CODON USAGE; COMMUNITY STRUCTURE; COMPARATIVE STUDY; DATA ANALYSIS SOFTWARE; ENZYME METABOLISM; ESCHERICHIA COLI; EVOLUTIONARY ALGORITHM; GENE EXPRESSION; GENE EXPRESSION PROFILING; GENE FUNCTION; GENE INACTIVATION; GENE MAPPING; GENETIC ALGORITHM; GENETIC ENGINEERING; GENETIC MANIPULATION; GENETIC TRANSCRIPTION; GENOME ANALYSIS; GENOMICS; MACHINE LEARNING; METABOLIC CAPACITY; METABOLIC ENGINEERING; MICROBIAL BIOMASS; MICROBIAL COMMUNITY; MODEL; NONHUMAN; PHENOTYPE; PREDICTION; PRINCIPAL COMPONENT ANALYSIS; PROCESS OPTIMIZATION; PROTEIN STRUCTURE; RNA SEQUENCE; SIMULATION; SYNECHOCOCCUS ELONGATUS; SYNECHOCYSTIS SP. PCC 6803; SYSTEM ANALYSIS; SYSTEMS BIOLOGY; TRANSCRIPTOMICS; BACTERIAL GENOME; BACTERIUM; BIOLOGICAL MODEL; GENE REGULATORY NETWORK; GENETICS; METABOLISM; PROCEDURES;

BACTERIA; GENE EXPRESSION PROFILING; GENE REGULATORY NETWORKS; GENOME, BACTERIAL; MACHINE LEARNING; METABOLIC NETWORKS AND PATHWAYS; MODELS, BIOLOGICAL; SYSTEMS BIOLOGY;

EID: 85037701016     PISSN: 10643745     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-1-4939-7528-0_18     Document Type: Chapter

References (68)

1. Louca S, Doebeli M (2015) Calibration and analysis of genome-based models for microbial ecology. Elife 4:e08208.
2. Nilsson A, Nielsen J (2016) Genome scale metabolic modeling of cancer. Metab Eng 43(B):103–112.
3. Orth JD, Thiele I, Palsson BØ (2010) What is flux balance analysis? Nat Biotechnol 28(3):245–248.
4. Zieliński ŁP, Smith AC, Smith AG, Robinson AJ (2016) Metabolic flexibility of mitochondrial respiratory chain disorders predicted by computer modelling. Mitochondrion 31:45–55.
5. Palsson BØ (2011) Systems biology: simulation of dynamic network states. Cambridge University Press, Cambridge.
6. Jayaraman A, Hahn J (2009) Methods in Bioengineering: systems analysis of biological networks. Artech House methods in bioengineering series. Artech House, Boston. https://books.google.co.uk/books?id=Haod3KR-tR8C.
7. Bordbar A, Monk JM, King ZA, Palsson BO (2014) Constraint-based models predict metabolic and associated cellular functions. Nat Rev Genet 15(2):107–120.
8. Burgard AP, Vaidyaraman S, Maranas CD (2001) Minimal reaction sets for Escherichia coli metabolism under different growth requirements and uptake environments. Biotechnol Prog 17(5):791–797.
9. Mahadevan R, Schilling C (2003) The effects of alternate optimal solutions in constraint-based genome-scale metabolic models. Metab Eng 5(4):264–276.
10. Mahadevan R, Edwards JS, Doyle FJ (2002) Dynamic flux balance analysis of diauxic growth in Escherichia coli. Biophys J 83(3):1331–1340.
11. Kanehisa M, Furumichi M, Tanabe M, Sato Y, Morishima K (2016) Kegg: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. http://dx.doi.org/10.1093/nar/gkw1092.
12. King ZA, Lu J, Dräger A, Miller P, Federowicz S, Lerman JA, Ebrahim A, Palsson BO, Lewis NE (2016) Bigg models: a platform for integrating, standardizing and sharing genome-scale models. Nucleic Acids Res 44(D1):D515–D522.
13. Caspi R, Billington R, Ferrer L, Foerster H, Fulcher CA, Keseler IM, Kothari A, Krummenacker M, Latendresse M, Mueller LA et al (2016) The metacyc database of metabolic pathways and enzymes and the biocyc collection of pathway/genome databases. Nucleic Acids Res 44(D1):D471–D480.
14. Devoid S, Overbeek R, DeJongh M, Vonstein V, Best AA, Henry C (2013) Automated genome annotation and metabolic model reconstruction in the seed and model seed. In: Systems metabolic engineering: methods and protocols. Humana Press, New York, pp 17–45.
15. Angione C, Pratanwanich N, Lió P (2015) A hybrid of metabolic flux analysis and bayesian factor modeling for multiomic temporal pathway activation. ACS Synth Biol 4(8):880–889.
16. Lewis NE, Hixson KK, Conrad TM, Lerman JA, Charusanti P, Polpitiya AD, Adkins JN, Schramm G, Purvine SO, Lopez-Ferrer D et al (2010) Omic data from evolved E. coli are consistent with computed optimal growth from genome-scale models. Mol Syst Biol 6(1):390.
17. Palsson B (2015) Systems biology: constraint-based reconstruction and analysis. Cambridge University Press, Cambridge. https://books.google.co.uk/books?id=QNBpBgAAQBAJ.
18. Voigt C (2011) Synthetic biology, part b: computer aided design and DNA assembly. Methods in enzymology. Elsevier Science, Amsterdam. https://books.google.co.uk/books?id=9uPvZWiabr4C.
19. Deutscher D, Meilijson I, Schuster S, Ruppin E (2008) Can single knockouts accurately single out gene functions? BMC Syst Biol 2(1):50.
20. Shlomi T, Berkman O, Ruppin E (2005) Regulatory on/off minimization of metabolic flux changes after genetic perturbations. Proc Natl Acad Sci USA 102(21):7695–7700.
21. Megchelenbrink W, Katzir R, Lu X, Ruppin E, Notebaart RA (2015) Synthetic dosage lethality in the human metabolic network is highly predictive of tumor growth and cancer patient survival. Proc Natl Acad Sci 112(39):12217–12222.
22. Conway M, Angione C, Liò P (2016) Iterative multi level calibration of metabolic networks. Curr Bioinforma 11(1):93–105.
23. Costanza J, Carapezza G, Angione C, Lió P, Nicosia G (2012) Robust design of microbial strains. Bioinformatics 28(23):3097–3104.
24. Angione C, Lió P (2015) Predictive analytics of environmental adaptability in multi-omic network models. Sci Rep 5:15147.
25. Angione C, Costanza J, Carapezza G, Lió P, Nicosia G (2015) Multi-target analysis and design of mitochondrial metabolism. PloS One 10(9):e0133825.
26. Xu G (2011) An iterative strategy for bi-objective optimization of metabolic pathways. In: 2011 fourth international joint conference on computational sciences and optimization.
27. Sendin J, Exler O, Banga JR (2010) Multi-objective mixed integer strategy for the optimisation of biological networks. IET Syst Biol 4(3):236–248.
28. Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197. http://dx.doi.org/10.1109/4235.996017.
29. Zitzler E, Thiele L (1999) Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Trans Evol 3(4):257–271.
30. Zhang Q, Li H (2007) Moea/d: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans Evol Comput 11(6):712–731.
31. Nagrath D, Avila-Elchiver M, Berthiaume F, Tilles AW, Messac A, Yarmush ML (2010) Soft constraints-based multiobjective framework for flux balance analysis. Metab Eng 12(5):429–445.
32. Kelk SM, Olivier BG, Stougie L, Bruggeman FJ (2012) Optimal flux spaces of genome-scale stoichiometric models are determined by a few subnetworks. Sci Rep 2:580.
33. Maarleveld TR, Wortel MT, Olivier BG, Teusink B, Bruggeman FJ (2015) Interplay between constraints, objectives, and optimality for genome-scale stoichiometric models. PLoS Comput Biol 11(4):e1004166.
34. Oh YG, Lee DY, Lee SY, Park S (2009) Multiobjective flux balancing using the NISE method for metabolic network analysis. Biotechnol Prog 25(4):999–1008.
35. Budinich M, Bourdon J, Larhlimi A, Eveillard D (2017) A multi-objective constraint-based approach for modeling genome-scale microbial ecosystems. PloS One 12(2):e0171744.
36. John PCS, Crowley MF, Bomble YJ (2016) Efficient estimation of the maximum metabolic productivity of batch systems. arXiv preprint. arXiv:161001114.
37. Machado D, Herrgård M (2014) Systematic evaluation of methods for integration of transcriptomic data into constraint-based models of metabolism. PLoS Comput Biol 10(4):e1003580.
38. Covert MW, Schilling CH, Palsson B (2001) Regulation of gene expression in flux balance models of metabolism. J Theor Biol 213(1):73–88.
39. Chandrasekaran S, Price ND (2010) Probabilistic integrative modeling of genome-scale metabolic and regulatory networks in Escherichia coli and Mycobacterium tuberculosis. Proc Natl Acad Sci 107(41):17845–17850.
40. Chandrasekaran S, Price ND (2013) Metabolic constraint-based refinement of transcriptional regulatory networks. PLoS Comput Biol 9(12):e1003370.
41. Rügen M, Bockmayr A, Steuer R (2015) Elucidating temporal resource allocation and diurnal dynamics in phototrophic metabolism using conditional FBA. Sci Rep 5, 15247.
42. Reimers AM, Knoop H, Bockmayr A, Steuer R (2016) Evaluating the stoichiometric and energetic constraints of cyanobacterial diurnal growth. arXiv preprint. arXiv:161006859.
43. Angione C, Conway M, Lió P (2016) Multiplex methods provide effective integration of multi-omic data in genome-scale models. BMC Bioinf 17(4):83.
44. Ebrahim A, Brunk E, Tan J, O’Brien EJ, Kim D, Szubin R, Lerman JA, Lechner A, Sastry A, Bordbar A et al (2016) Multi-omic data integration enables discovery of hidden biological regularities. Nat Commun 71:13091.
45. Segre D, Vitkup D, Church GM (2002) Analysis of optimality in natural and perturbed metabolic networks. Proc Natl Acad Sci 99(23):15112–15117.
46. Raval A, Ray A (2013) Introduction to biological networks. CRC Press, Boca Raton.
47. Machado D, Costa RS, Ferreira EC, Rocha I, Tidor B (2012) Exploring the gap between dynamic and constraint-based models of metabolism. Metab Eng 14(2):112–119.
48. Brochado AR, Andrejev S, Maranas CD, Patil KR (2012) Impact of stoichiometry representation on simulation of genotype-phenotype relationships in metabolic networks. PLoS Comput Biol 8(11):e1002758.
49. Yizhak K, Benyamini T, Liebermeister W, Ruppin E, Shlomi T (2010) Integrating quantitative proteomics and metabolomics with a genome-scale metabolic network model. Bioinformatics 26(12):i255–i260.
50. Monk JM, Koza A, Campodonico MA, Machado D, Seoane JM, Palsson BO, Herrgård MJ, Feist AM (2016) Multi-omics quantification of species variation of Escherichia coli links molecular features with strain phenotypes. Cell Syst 3(3):238–251.
51. Vivek-Ananth R, Samal A (2016) Advances in the integration of transcriptional regulatory information into genome-scale metabolic models. Biosystems 147:1–10.
52. Becker SA, Palsson BØ (2008) Context-specific metabolic networks are consistent with experiments. PLoS Comput Biol 4(5):e1000082.
53. Colijn C, Brandes A, Zucker J, Lun DS, Weiner B, Farhat MR, Cheng TY, Moody DB, Murray M, Galagan JE (2009) Interpreting expression data with metabolic flux models: predicting Mycobacterium tuberculosis mycolic acid production. PLoS Comput Biol 5(8):e1000489.
54. Jerby L, Shlomi T, Ruppin E (2010) Computational reconstruction of tissue-specific metabolic models: application to human liver metabolism. Mol Syst Biol 6(1):401.
55. Vlassis N, Pacheco MP, Sauter T (2014) Fast reconstruction of compact context-specific metabolic network models. PLoS Comput Biol 10(1):e1003424.
56. Wang Y, Eddy JA, Price ND (2012) Reconstruction of genome-scale metabolic models for 126 human tissues using mCADRE. BMC Syst Biol 6(1):153.
57. Imam S, Schäuble S, Brooks AN, Baliga NS, Price ND (2015) Data-driven integration of genome-scale regulatory and metabolic network models. Front Microbiol 6:409.
58. Carrera J, Estrela R, Luo J, Rai N, Tsoukalas A, Tagkopoulos I (2014) An integrative, multi-scale, genome-wide model reveals the phenotypic landscape of Escherichia coli. Mol Syst Biol 10(7):735.
59. Kashaf SS, Angione C, Lió P (2017) Making life difficult for clostridium difficile: augmenting the pathogen’s metabolic model with transcriptomic and codon usage data for better therapeutic target characterization. BMC Syst Biol 11(1):25.
60. Zitzler E, Brockhoff D, Thiele L (2007) The hypervolume indicator revisited: on the design of pareto-compliant indicators via weighted integration. In: International conference on evolutionary multi-criterion optimization. Springer, Berlin, pp 862–876.
61. Ringnér M (2008) What is principal component analysis? Nat Biotechnol 26(3):303.
62. Trefethen LN, Embree M (2005) Spectra and pseudospectra: the behavior of nonnormal matrices and operators. Princeton University Press, Princeton.
63. Newman M (2013) Spectral community detection in sparse networks. arXiv preprint. arXiv:13086494.
64. Schellenberger J, Que R, Fleming RM, Thiele I, Orth JD, Feist AM, Zielinski DC, Bordbar A, Lewis NE, Rahmanian S et al (2011) Quantitative prediction of cellular metabolism with constraint-based models: the cobra toolbox v2. 0. Nat Protoc 6(9):1290–1307.
65. Orth J, Conrad T, Na J, Lerman J, Nam H, Feist A, Palsson B (2011) A comprehensive genome-scale reconstruction of Escherichia coli metabolism. Mol Syst Biol 7(1):535.
66. Faith JJ, Hayete B, Thaden JT, Mogno I, Wierzbowski J, Cottarel G, Kasif S, Collins JJ, Gardner TS (2007) Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles. PLoS Biol 5(1):e8.
67. Hui S, Silverman JM, Chen SS, Erickson DW, Basan M, Wang J, Hwa T, Williamson JR (2015) Quantitative proteomic analysis reveals a simple strategy of global resource allocation in bacteria. Mol Syst Biol 11(2):784.
68. Li C, Li H (2008) Network-constrained regularization and variable selection for analysis of genomic data. Bioinformatics 24(9):1175–1182.