Abstract
Plant secondary metabolites contribute significantly to the field of agriculture, medicine, and biofuels. These compounds have been a focal point in plant breeding and metabolic engineering. However information on these compounds is lacking severely especially in non-model plants. Through integrated omics approach, we can now study secondary metabolites in model and non-model plants to determine genes, predict gene function, and provide information on pathways that may regulate its biosynthesis and function. Online resources have provided a means to fast-track our understanding on the mechanism involved in the biosynthesis of secondary metabolites and how these products are regulated by their environment, developmental stages, and species. The information derived may be utilized in metabolic engineering or in elicitation of the mechanisms involved in its production. As secondary metabolites have been implicated in plant defense mechanism, the understanding of the genes, their function, and their pathways will definitely assist in improving plant defenses against biotic and abiotic stresses. Here we provide a brief overview on the databases and resources available to conduct in silico analysis of plant secondary metabolites and future prospects in utilizing the derived information to improve metabolite function and production in crops.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Akiyama K, Chikayama E, Yuasa H et al (2008) PRIMe: a web site that assembles tools for metabolomics and transcriptomics. In Silico Biol 8:339–345. PMID: 19032166
Alagna F (2013) Innovative transcriptomics approaches for large scale identification of genes involved in plant secondary metabolism. J Plant Biochem Physiol 1:e107. https://doi.org/10.4172/2329-9029.1000e107
Alagna F, D’Agostino N, Torchia L et al (2009) Comparative 454 pyrosequencing of transcripts from two olive genotypes during fruit development. BMC Genomics 10:399. https://doi.org/10.1186/1471-2164-10-399
Arnaud B, Elio S, Robert DH (2007) Metabolic engineering of flavonoids in tomato (Solanum lycopersicum): the potential for metabolomics. Metabolomics 3:399–412. https://doi.org/10.1007/s11306-007-0074-2
Baldazzi V, Bertin N, de Jong H et al (2012) Towards multiscale plant models: integrating cellular networks. Trends Plant Sci 17:728–736. https://doi.org/10.1016/j.tplants.2012.06.012
Beckers V, Dersch LM, Lotz K et al (2016) In silico metabolic network analysis of Arabidopsis leaves. BMC Syst Biol 10:102. https://doi.org/10.1186/s12918-016-0347-3
Beltrame L, Bianco L, Fontana P et al (2013) Pathway processor 2.0: a web resource for pathway-based analysis of high-throughput data. Bioinformatics 29:1825–1826. https://doi.org/10.1093/bioinformatics/btt292
Bombarely A, Menda N, Tecle IY et al (2011) The sol genomics network (solgenomics.net): growing tomatoes using Perl. Nucleic Acids Res 39:D1149–D1155. https://doi.org/10.1093/nar/gkq866
Broun P (2004) Transcription factors as tools for metabolic engineering in plants. Curr Opin Plant Biol 7:202–209. https://doi.org/10.1016/j.pbi.2004.01.013
Broun P (2005) Transcriptional control of flavonoid biosynthesis: a complex network of conserved regulators involved in multiple aspects of differentiation in Arabidopsis. Curr Opin Plant Biol. 2005 8:272–279. https://doi.org/10.1016/j.pbi.2005.03.006
Capell T, Christou P (2004) Progress in plant metabolic engineering. Curr Opin Biotechnol 15:148–154. https://doi.org/10.1016/j.copbio.2004.01.009
Carelli M, Biazzi E, Panara F, Tava A et al (2011) Medicago truncatula CYP716A12 is a multifunctional oxidase involved in the biosynthesis of hemolytic saponins. Plant Cell 23:3070–3081. https://doi.org/10.1105/tpc.111.087312
Carroll AJ, Badger MR, Harvey Millar A (2010) The MetabolomeExpress project: enabling web-based processing, analysis and transparent dissemination of GC/MS metabolomics datasets. BMC Bioinforma 11:376. https://doi.org/10.1186/1471-2105-11-376
Cusido RM, Onrubia M, Sabater-Jara AB et al (2014) A rational approach to improving the biotechnological production of taxanes in plant cell cultures of Taxus spp. Biotechnol Adv 32(6):1157–1167. https://doi.org/10.1016/j.biotechadv.2014.03.002
D’Agostino N, Traini A, Frusciante L, Chiusano ML (2009) SolEST database: a ‘one-stop shop’ approach to the study of Solanaceae transcriptomes. BMC Plant Biol 9:142. https://doi.org/10.1186/1471-2229-9-142
de Oliveira Dal’Molin CG, Quek LE, Palfreyman RW et al (2010) AraGEM, a genome-scale reconstruction of the primary metabolic network in Arabidopsis. Plant Physiol 152(2):579–589. doi.org/10.1104/pp.109.148817
Dersch LM, Beckers V, Wittmann C (2016) Green pathways: metabolic network analysis of plant systems. Metab Eng 34:1–24. https://doi.org/10.1016/j.ymben.2015.12.001
Do PT, Prudent M, Sulpice R et al (2010) The influence of fruit load on the tomato pericarp metabolome in a Solanum chmielewskii introgression line population. Plant Physiol 154:1128–1142. https://doi.org/10.1104/pp.110.163030
Dudareva N, Pichersky E (2008) Metabolic engineering of plant volatiles. Curr Opin Biotechnol 2008(19):181–189. https://doi.org/10.1016/j.copbio.2008.02.011
Dyer JM, Stymne S, Green AG et al (2008) High-value oils from plants. Plant J 54:640–655. https://doi.org/10.1111/j.1365-313X.2008.03430.x
Enfissi EM, Barneche F, Ahmed I et al (2010) Integrative transcript and metabolite analysis of nutritionally enhanced DE-ETIOLATED1 down-regulated tomato fruit. Plant Cell 22:1190–1215. https://doi.org/10.1105/tpc.110.073866
Ferry-Dumazet H, Gil L, Deborde C et al (2011) MeRy-B: a web knowledgebase for the storage, visualization, analysis and annotation of plant NMR metabolomic profiles. BMC Plant Biol 11:104. https://doi.org/10.1186/1471-2229-11-104
Fiehn O (2001) Combining genomics, metabolome analysis, and biochemical modelling to understand metabolic networks. Comp Funct Genomics 2:155–168. https://doi.org/10.1002/cfg.82
Fiehn O, Weckwerth W (2003) Deciphering metabolic networks. Eur J Biochem 270:579–588. PMID:12581198
Fiehn O, Kopka J, Dormann P et al (2000) Metabolite profiling for plant functional genomics. Nat Biotechnol 18:1157–1161. https://doi.org/10.1038/81137
Field B, Osbourn AE (2008) Metabolic diversification – independent assembly of operon-like gene clusters in different plants. Science 320:543–547. https://doi.org/10.1126/science.1154990
Field B, Fiston-Lavier AS, Kemen A, Geisler K, Quesneville H et al (2011) Formation of plant metabolic gene clusters within dynamic chromosomal regions. Proc Natl Acad Sci U S A 108:16116–16121. https://doi.org/10.1073/pnas.1109273108
Forester SC, Waterhouse AL (2009) Metabolites are key to understanding health effects of wine polyphenolics. J Nutr 139:1824S–1831S. https://doi.org/10.3945/jn.109.107664
Forster J, Famili I, Fu P et al (2003) Genome-scale reconstruction of the Saccharomyces cerevisiae metabolic network. Genome Res 13:244–253. https://doi.org/10.1101/gr.234503
Grafahrend-Belau E, Schreiber F, Koschützki D et al (2009) Flux balance analysis of barley seeds: a computational approach to study systemic properties of central metabolism. Plant Physiol 149(1):585–598. https://doi.org/10.1104/pp.108.129635
Grant D, Nelson RT, Cannon SB et al (2010) SoyBase, the USDA-ARS soybean genetics and genomics database. Nucleic Acids Res 38:D843–D846. https://doi.org/10.1093/nar/gkp798
Grieneisen VA, Scheres B, Hogeweg P et al (2012) Morphogengineering roots: comparing mechanisms of morphogen gradient formation. BMC Syst Biol 6:37. https://doi.org/10.1186/1752-0509-6-37
Grimsrud PA, Swaney DL, Wenger CD et al (2010a) Phosphoproteomics for the masses. ACS Chem Biol 5:105–119. 10.1021/cb900277e. https://doi.org/10.1021/cb900277e
Grimsrud PA, den Os D, Wenger CD et al (2010b) Large-scale phosphoprotein analysis in Medicago truncatula roots provides insight into in vivo kinase activity in legumes. Plant Physiol 152:19–28. 10.1104/pp.109.149625. https://doi.org/10.1104/pp.109.149625
Hamada K, Hongo K, Suwabe K et al (2011) OryzaExpress: an integrated database of gene expression networks and omics annotations in rice. Plant Cell Physiol 52:220–229. https://doi.org/10.1093/pcp/pcq195
Hammami R, Ben-Hamida J, Vergoten G et al (2009) Phytamp: a database dedicated to antimicrobial plant peptides. Nucl Acids Res 37:D963–D968. https://doi.org/10.1093/nar/gkn655
Hartmann T (2007) From waste products to ecochemicals: fifty years research of plant secondary metabolism. Phytochemistry 68:2831–2846. https://doi.org/10.1016/j.phytochem.2007.09.017
Helmy M, Tomita M, Ishihama Y (2011) OryzaPG-DB: rice proteome database based on shotgun proteogenomics. BMC Plant Biol 11:63. https://doi.org/10.1186/1471-2229-11-63
Hirai MY, Klein M, Fujikawa Y et al (2005) Elucidation of gene-to-gene and metabolite-to-gene networks in Arabidopsis by integration of metabolomics and transcriptomics. J Biol Chem 280:25590–25595. https://doi.org/10.1074/jbc.M502332200
Hostettman K, Terreaux C (2000) Search for new lead compounds from higher plants. Chimia 54:652–657. ISSN 0009-4293
Hwang KS, Kim HU, Charusanti P et al (2014) Systems biology and biotechnology of Streptomyces species for the production of secondary metabolites. Biotechnol Adv 32(2):255–268. https://doi.org/10.1016/j.biotechadv.2013.10.008
Iijima Y, Nakamura Y, Ogata Y et al (2008) Metabolite annotations based on the integration of mass spectral information. Plant J 54:949–962. https://doi.org/10.1111/j.1365-313X.2008.03434.x
Jansen JJ, Hoefsloot HCJ, van der Greef J et al (2005) Multilevel component analysis of time-resolved metabolic fingerprinting data. Anal Chim Acta 530:173–183. https://doi.org/10.1016/j.aca.2004.09.074
Joung JG, Corbett AM, Fellman SM et al (2009) Plant MetGenMAP: an integrative analysis system for plant systems biology. Plant Physiol 151:1758–1768. https://doi.org/10.1104/pp.109.145169
Junker BH (2014) Flux analysis in plant metabolic networks: increasing throughput and coverage. Curr Opin Biotechnol 26:183–188. https://doi.org/10.1016/j.copbio.2014.01.016
Kawaura K, Mochida K, Yamazaki Y et al (2006) Transcriptome analysis of salinity stress responses in common wheat using a 22k oligo-DNA microarray. Funct Integr Genomics 6:132–142. https://doi.org/10.1007/s10142-005-0010-3
Kim HJ, Baek KH, Lee SW et al (2008) Pepper EST database: comprehensive in silico tool for analyzing the chili pepper (Capsicum annuum) transcriptome. BMC Plant Biol 8:101. https://doi.org/10.1186/1471-2229-8-101
Kim B, Park H, Na D, Lee SY (2014) Metabolic engineering of Escherichia coli for the production of phenol from glucose. Biotechnol J 9(5):621–629. https://doi.org/10.1002/biot.201300263
Kizer L, Pitera DJ, Pfleger BF et al (2008) Application of functional genomics to pathway optimization for increased isoprenoid production. Appl Environ Microbiol 74:3229–3241. https://doi.org/10.1128/AEM.02750-07
Lee TH, Kim YK, Pham TT et al (2009) RiceArrayNet: a database for correlating gene expression from transcriptome profiling, and its application to the analysis of coexpressed genes in rice. Plant Physiol 151:16–33. https://doi.org/10.1104/pp.109.139030
Li GL, Kollner TG, Yin Y et al (2012) Nonseed plant Selaginella moellendorffii has both seed plant and microbial types of terpene synthases. Proc Natl Acad Sci U S A 109:14711–14715. https://doi.org/10.1073/pnas.1204300109
Libault M, Farmer A, Joshi T, Takahashi K, Langley RJ, Franklin LD et al (2010) An integrated transcriptome atlas of the crop model Glycine max, and its use in comparative analyses in plants. Plant J 63:86–99. https://doi.org/10.1111/j.1365-313X.2010.04222.x
Lichtenthaler HK (2000) Non-mevalonate isoprenoid biosynthesis: enzymes, genes and inhibitors. Biochem Soc Trans 28:785–789. PMID:11171208
Lindon JC, Nicholson JK, Holmes E (2007) The handbook of metabonomics and metabolomics. Elsevier, Amsterdam. ISBN:9780080468006
Lobell DB, Schlenker W, Costa-Roberts J (2011) Climate trends and global crop production since 1980. Science 333:616–620. https://doi.org/10.1126/science.1204531
Lotz K, Hartmann A, Grafahrend-Belau E, Schreiber F, Junker BH (2014) Elementary flux modes, flux balance analysis, and their application to plant metabolism. Methods Mol Biol 1083:231–252. https://doi.org/10.1007/978-1-62703-661-0_14
Manners JM (2007) Hidden weapons of microbial destruction in plant genomes. Genome Biol 8:225. https://doi.org/10.1186/gb-2007-8-9-225
Medina I, Carbonell J, Pulido L et al (2010) Babelomics: an integrative platform for the analysis of transcriptomics, proteomics and genomic data with advanced functional profiling. Nucleic Acids Res 38:210–213. https://doi.org/10.1093/nar/gkq388
Mercke P, Kappers IF, Verstappen FWA et al (2004) Combined transcript and metabolite analysis reveals genes involved in spider mite induced volatile formation in cucumber plants. Plant Physiol 135:2012–2024. https://doi.org/10.1104/pp.104.048116
Metlen KL, Aschehoug ET, Callaway RM (2009) Plant behavioural ecology: dynamic plasticity in secondary metabolites. Plant Cell Environ 32:641–653. https://doi.org/10.1111/j.1365-3040.2008.01910.x
Metzker ML (2010) Sequencing technologies—the next generation. Nat Rev Genet 11:31–46. https://doi.org/10.1038/nrg2626
Michael TP, Jackson S (2013) The first 50 plant genomes. Plant Genome 6. https://doi.org/10.3835/plantgenome2013.03.0001in
Mochida K, Shinozaki K (2011) Advances in omics and bioinformatics tools for systems analyses of plant functions. Plant Cell Physiol 52(12):2017–2038. https://doi.org/10.1093/pcp/pcr153
Mochida K, Yamazaki Y, Ogihara Y (2003) Discrimination of homoeologous gene expression in hexaploid wheat by SNP analysis of contigs grouped from a large number of expressed sequence tags. Mol Gen Genomics 270:371–377. https://doi.org/10.1007/s00438-003-0939-7
Mochida K, Kawaura K, Shimosaka E et al (2006) Tissue expression map of a large number of expressed sequence tags and its application to in silico screening of stress response genes in common wheat. Mol Gen Genomics 276:304–312. https://doi.org/10.1007/s00438-006-0120-1
Mochida K, Saisho D, Yoshida T et al (2008) TriMEDB: a database to integrate transcribed markers and facilitate genetic studies of the tribe Triticeae. BMC Plant Biol 8:72. https://doi.org/10.1186/1471-2229-8-72
Mounet F, Moing A, Garcia V et al (2009) Gene and metabolite regulatory network analysis of early developing fruit tissues highlights new candidate genes for the control of tomato fruit composition and development. Plant Physiol 149:1505–1528. https://doi.org/10.1104/pp.108.133967
Nakagami H, Sugiyama N, Mochida K et al (2010) Large-scale comparative phosphoproteomics identifies conserved phosphorylation sites in plants. Plant Physiol 153:1161–1174. https://doi.org/10.1104/pp.110.157347
Nakagami H, Sugiyama N, Ishihama Y et al (2011) Shotguns in the front line: phosphoproteomics in plants. Plant Cell Physiol 53(1):118–124. https://doi.org/10.1093/pcp/pcr148
Naoumkina MA, Modolo LV, Huhman DV et al (2010) Genomic and coexpression analyses predict multiple genes involved in triterpene saponin biosynthesis in Medicago truncatula. Plant Cell 22:850–866. https://doi.org/10.1105/tpc.109.073270
Nautrup-Pedersen G, Dam S, Laursen BS et al (2010) Proteome analysis of pod and seed development in the model legume Lotus japonicus. J Proteome Res 9:5715–5726. https://doi.org/10.1021/pr100511u
Nawrot R, Barylski J, Nowicki G et al (2014) Plant antimicrobial peptides. Folia Microbiol (Praha) 59(3):181–196. https://doi.org/10.1007/s12223-013-0280-4
Ogihara Y, Mochida K, Nemoto Y et al (2003) Correlated clustering and virtual display of gene expression patterns in the wheat life cycle by large-scale statistical analyses of expressed sequence tags. Plant J 33:1001–1011. PMID:12631325
Oksman-Caldentey KM, Saito K (2005) Integrating genomics and metabolomics for engineering plant metabolic pathways. Curr Opin Biotechnol 16:174–179. https://doi.org/10.1016/j.copbio.2005.02.007
Pan D, Sun N, Cheung KH et al (2003) PathMAPA: a tool for displaying gene expression and performing statistical tests on metabolic pathways at multiple levels for Arabidopsis. BMC Bioinforma 4:56. https://doi.org/10.1186/1471-2105-4-56
Paterson AH, Bowers JE, Bruggmann R et al (2009) The Sorghum bicolor genome and the diversification of grasses. Nature 457:551–556. https://doi.org/10.1038/nature07723
Pereira DM, Valentao P, Correia-da-Silva G et al (2012) Plant secondary metabolites in cancer chemotherapy: where are we? Curr Pharm Biotechnol 13:632–650. PMID:22122478
Perry J, Brachmann A, Welham T et al (2009) TILLING in Lotus japonicus identified large allelic series for symbiosis genes and revealed a bias in functionally defective ethyl methanesulfonate alleles toward glycine replacements. Plant Physiol 151:1281–1291. https://doi.org/10.1104/pp.109.142190
Pestana-Calsa MC, Ribeiro IL, Calsa T Jr (2010) Bioinformatics-coupled molecular approaches for unravelling potential antimicrobial peptides coding genes in Brazilian native and crop plant species. Curr Protein Pept Sci 11:199–209. PMID:20088767
Poblete-Castro I, Binger D, Rodrigues A et al (2013) In-silico-driven metabolic engineering of Pseudomonas putida for enhanced production of poly-hydroxyalkanoates. Metab Eng 15:113–123. https://doi.org/10.1016/j.ymben.2012.10.004
Rischer H, Oresic M, Seppanen-Laakso T et al (2006) Gene-to-metabolite networks for terpenoid indole alkaloid biosynthesis in Catharanthus roseus cells. Proc Natl Acad Sci U S A 103:5614–5619. https://doi.org/10.1073/pnas.0601027103
Roberts SC (2007) Production and engineering of terpenoids in plant cell culture. Nat Chem Biol 3:387–395. https://doi.org/10.1038/nchembio.2007.8
Rohmer M (1999) The discovery of a mevalonate-independent pathway for isoprenoid biosynthesis in bacteria, algae and higher plants. Nat Prod Rep 16:565–574. PMID:10584331
Saha R, Suthers PF, Maranas CD (2011) Zea mays iRS1563: a comprehensive genome-scale metabolic reconstruction of maize metabolism. PLoS One 6(7):e21784. https://doi.org/10.1371/journal.pone.0021784
Saito K, Dixon RA, Willmitzer L (2006) In: Saito K, Dixon RA, Willmitzer L (eds) Plant metabolomics. Springer-Verlag, Berlin, pp 111–113. https://doi.org/10.1007/3-540-29782-0
Saito K, Hirai MY, Yonekura-Sakakibara K (2008) Decoding genes with coexpression networks and metabolomics – ‘Majority report by precogs’. Trends Plant Sci 13:36–43. https://doi.org/10.1016/j.tplants.2007.10.006
Sakurai N, Ara T, Ogata Y et al (2011) KaPPA-View4: a metabolic pathway database for representation and analysis of correlation networks of gene co-expression and metabolite co-accumulation and omics data. Nucleic Acids Res 39:D677–D684. https://doi.org/10.1093/nar/gkq989
Sato K, Nankaku N, Takeda K (2009) A high-density transcript linkage map of barley derived from a single population. Heredity 103:110–117. https://doi.org/10.1038/hdy.2009.57
Schatz MC, Witkowski J, McCombie WR (2012) Current challenges in de novo plant genome sequencing and assembly. Genome Biol 13:243. https://doi.org/10.1186/gb4015
Schilmiller A, Shi F, Kim J et al (2010) Mass spectrometry screening reveals widespread diversity in trichome specialized metabolites of tomato chromosomal substitution lines. Plant J 62:391–403. https://doi.org/10.1111/j.1365-313X.2010.04154.x
Schnable PS, Ware D, Fulton RS et al (2009) The B73 maize genome: complexity, diversity, and dynamics. Science 326:1112–1115. https://doi.org/10.1126/science.1178534
Schoof H, Ernst R, Nazarov V et al (2004) MIPS Arabidopsis thaliana database (MAtDB): an integrated biological knowledge resource for plant genomics. Nucleic Acids Res 32:D373–D376. https://doi.org/10.1093/nar/gkh068
Shachar-Hill Y (2013) Metabolic network flux analysis for engineering plant systems. Curr Opin Biotechnol 24(2):247–255. https://doi.org/10.1016/j.copbio.2013.01.004
Silverstein KAT, Graham MA, Paape TD et al (2005) Genome organization of more than 300 defensin-like genes in Arabidopsis. Plant Physiol 138:600–610. doi.org/10.1104/pp.105.060079
Silverstein KAT, Moskal WA Jr, Wu HC et al (2007) Small cysteine-rich peptides resembling antimicrobial peptides have been underpredicted in plants. Plant J 51:262–280. https://doi.org/10.1111/j.1365-313X.2007.03136.x
Somerville C, Youngs H, Taylor C, Davis SC, Long SP (2010) Feedstocks for lignocellulosic biofuels. Science 329:790–792. https://doi.org/10.1126/science.1189268
Sweetlove LJ, Last RL, Fernie AR (2003) Predictive metabolic engineering: a goal for systems biology. Plant Physiol 132(2):420–425
Terras FR, Eggermont K, Kovaleva V et al (1995) Small cysteine-rich antifungal proteins from radish: their role in host defense. Plant Cell 7:573–588. doi.org/10.1105/tpc.7.5.573
Thimm O, Blasing O, Gibon Y et al (2004) MAPMAN: a user-driven tool to display genomics data sets onto diagrams of metabolic pathways and other biological processes. Plant J 37:914–939. PMID:14996223
Tieman D, Zeigler M, Schmelz E et al (2010) Functional analysis of a tomato salicylic acid methyl transferase and its role in synthesis of the flavor volatile methyl salicylate. Plant J 62:113–123. https://doi.org/10.1111/j.1365-313X.2010.04128.x
Tohge T, Nishiyama Y, Hirai MY et al (2005) Functional genomics by integrated analysis of metabolome and transcriptome of Arabidopsis plants over-expressing an MYB transcription factor. Plant J 42:218–235. https://doi.org/10.1111/j.1365-313X.2005.02371.x
Tokimatsu T, Sakurai N, Suzuki H et al (2005) KaPPAview: a web-based analysis tool for integration of transcript and metabolite data on plant metabolic pathway maps. Plant Physiol 138:1289–1300. https://doi.org/10.1104/pp.105.060525
Trethewey RN (2001) Gene discovery via metabolic profiling. Curr Opin Biotechnol 12:135–138. PMID:11287226
Ulrich-Merzenich G, Zeitler H, Jobst D et al (2007) Application of the “omic-” technologies in phytomedicine. Phytomedicine 14:70–82. https://doi.org/10.1016/j.phymed.2006.11.011
Urbanczyk-Wochniak E, Baxter C, Kolbe A et al (2005) Profiling of diurnal patterns of metabolite and transcript abundance in potato (Solanum tuberosum) leaves. Planta 221:891–903. https://doi.org/10.1007/s00425-005-1483-y
Vernoux T, Brunoud G, Farcot E et al (2011) The auxin signalling network translates dynamic input into robust patterning at the shoot apex. Mol Syst Biol 7:508. https://doi.org/10.1038/msb.2011.39
Verpoorte R, Memelink J (2002) Engineering secondary metabolite production in plants. Curr Opin Biotechnol 13:181–187. PMID:11950573
Walpole J, Papin JA, Peirce SM (2013) Multiscale computational models of complex biological systems. Annu Rev Biomed Eng 15(1):137–154. https://doi.org/10.1146/annurev-bioeng-071811-150104
Wang Y, Joshi T, Zhang XS et al (2006) Inferring gene regulatory networks from multiple microarray datasets. Bioinformatics 22:2413–2420. doi.org/10.1093/bioinformatics/btl396
Weckwerth W (2008) Integration of metabolomics and proteomics in molecular plant physiology – coping with the complexity by data-dimensionality reduction. Physiol Plant 132:176–189. https://doi.org/10.1111/j.1399-3054.2007.01011.x
Wu SQ, Chappell J (2008) Metabolic engineering of natural products in plants; tools of the trade and challenges for the future. Curr Opin Biotechnol 19:145–152. https://doi.org/10.1016/j.copbio.2008.02.007
Wurtele ES, Li J, Diao L et al (2003) MetNet: software to build and model the biogenetic lattice of Arabidopsis. Comp Funct Genomics 4:239–245. https://doi.org/10.1002/cfg.285
Xia J, Mandal R, Sinelnikov IV et al (2012) MetaboAnalyst 2.0 – a comprehensive server for metabolomic data analysis. Nucleic Acids Res 40:W127–W133. https://doi.org/10.1093/nar/gks374
Xu X, Pan S, Cheng S et al (2011) Genome sequence and analysis of the tuber crop potato. Nature 475:189–195. https://doi.org/10.1038/nature10158
Yan L, Kerr PS (2002) Genetically engineered crops: their potential use for improvement of human nutrition. Nutr Rev 60(5 Pt 1):135–141. PMID:12030276
Yang D, Du X, Yang Z et al (2014) Transcriptomics, proteomics, and metabolomics to reveal mechanisms underlying plant secondary metabolism. Eng Life Sci 00:1–11. https://doi.org/10.1002/elsc.201300075
Young ND, Udvardi M (2009) Translating Medicago truncatula genomics to crop legumes. Curr Opin Plant Biol 12:193–201. https://doi.org/10.1016/j.pbi.2008.11.005
Zhang H, Sreenivasulu N, Weschke W et al (2004) Large-scale analysis of the barley transcriptome based on expressed sequence tags. Plant J 40:276–290. https://doi.org/10.1111/j.1365-313X.2004.02209.x
Zhang P, Foerster H, Tissier CP et al (2005) MetaCyc and AraCyc. Metabolic pathway databases for plant research. Plant Physiol 138:27–37. https://doi.org/10.1104/pp.105.060376
Zhao J (2007) Nutraceuticals, nutritional therapy, phytonutrients, and phytotherapy for improvement of human health: a perspective on plant biotechnology application. Recent Pat Biotechnol 1:75–97. PMID:19075834
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Nadarajah, K.K. (2018). In Silico Identification of Plant-Derived Secondary Metabolites in Defense. In: Choudhary, D., Kumar, M., Prasad, R., Kumar, V. (eds) In Silico Approach for Sustainable Agriculture. Springer, Singapore. https://doi.org/10.1007/978-981-13-0347-0_16
Download citation
DOI: https://doi.org/10.1007/978-981-13-0347-0_16
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-0346-3
Online ISBN: 978-981-13-0347-0
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)