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In Silico Plant Growth Metabolites

  • Shradha Sharma
  • Pushpa Lohani
Chapter

Abstract

The exponential development in civilization of human race demands for more reliable resources and technology, to sustain and secure the future of the coming generations. With the growing pace of evolution, we need highly efficient technique to scrutinize the data and deduct the possible outcomes. System biology has opened up a totally new era of analysing these complex records of living beings. The in silico on literal terms means “performed on computer or via computer simulation”. The computer models of crops like rice (Oryza sativa), maize (Zea mays), soybean (Glycine max) and cassava (Manihot esculenta) have been made to study and design crops with higher yield and efficiency. This in silico analysis is a combat against the time taking crop breeding technique and error-prone genetic engineering where the result is unpredictable due to intricate interaction between genotype and management. The digital representation of plants in silico (Psi) will examine the possible phenotype of the crop from genotype and environmental interactions. In this chapter, we give a brief on secondary metabolites produced by plants and their potential applications along with the databases which have been made to easily retrieve the required data about them for scientific and academic purposes.

Keywords

Secondary metabolites Phytochemicals Database Simulation Computer models 

References

  1. Breitling R, Ceniceros A, Andris J, Eriko T (2013) Metabolomics for secondary metabolite research. Metabolites 3:1076–1083CrossRefPubMedPubMedCentralGoogle Scholar
  2. Caspi R, Altman T, Billington R, Dreher K, Foerster H, Fulcher CA, Holland TA, Keseler IM, Kothari A, Kub A (2013) The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acid Res 42:D459–D471CrossRefPubMedGoogle Scholar
  3. Davis, Vasanthi (2011) Bioinformation 5(8):361–364CrossRefPubMedPubMedCentralGoogle Scholar
  4. Eckardt NA (2012) In silico plant biology comes of age. Plant Cell 24:3857–3858CrossRefPubMedPubMedCentralGoogle Scholar
  5. Gandhi SG, Mahajan V, Bedi YS (2015) Changing trends in biotechnology of secondary metabolism in medicinal and aromatic plants. Planta 214:303–317CrossRefGoogle Scholar
  6. Grafahrend-Belau E, Weise S, Koschützki D, Scholz U, Junker BJ, Schreiber F (2008) MetaCrop: a detailed database of crop plant metabolism. Nucleic Acid Res 36:D954–D958CrossRefPubMedGoogle Scholar
  7. Hatti et al (2014) Bioinformation 10(5):314–315CrossRefPubMedPubMedCentralGoogle Scholar
  8. Hussain MS, Fareed S, Ansari S, Rahman MA, Ahmad IZ, Saeed M (2012) Current approaches towards production of secondary plant metabolites. J Pharm Bioallied Sci 4(1):10–20CrossRefPubMedPubMedCentralGoogle Scholar
  9. Ichikawa N, Sasagawa M, Yamamoto M, Komaki H, Yoshida Y, Yamazaki S, Fujita N (2013) DoBISCUIT: a database of secondary metabolite biosynthetic gene clusters. Nucleic Acid Res 41:D408–D414CrossRefPubMedGoogle Scholar
  10. Johnson SR, Lange BM (2015) Open access metabolomics databases for natural product research: present capabilities and future potential. Front Bioeng Biotechnol 3:22CrossRefPubMedPubMedCentralGoogle Scholar
  11. Karp PD, Riley M, Paley SM, Toole AP (2002) The MetaCyc database. Nucleic Acid Res 30(1):59–61CrossRefPubMedGoogle Scholar
  12. Kennedy DO, Wightman (2011) Herbal extracts and phytochemicals: plant secondary metabolites and the enhancement of human brain function. Adv Nutr 2:32–50CrossRefPubMedPubMedCentralGoogle Scholar
  13. Lu N, Bernardo EL, Tippayadarapanich C, Takagaki M, Kagawa N, Yamori W (2017) Growth and accumulation of secondary metabolites in Perilla as affected by photosynthetic photon flux density and electrical conductivity of the nutrient solutionGoogle Scholar
  14. Marshall-Colon A, Long SP, Allen DK, Allen G, Beard DA, Benes B, von Caemmerer S, Christensen AJ, Cox DJ, Hart JC, Hirst PM, Kannan K, Katz DS, Lynch JP, Millar AJ, Panneerselvam B, Price ND, Prusinkiewicz P, Raila D, Shekar RG, Shrivastava S, Shukla D, Srinivasan V, Stitt M, Turk MJ, Voit EO, Wang Y, Yin X, Zhu X-G (2017) Crops in silico: generating virtual crops using an integrative and multi-scale modeling platform. Front Plant Sci 8:786CrossRefPubMedPubMedCentralGoogle Scholar
  15. Nakamura Y, Afendi FM, Parvin AK, Ono N, Tanaka K, Morita AH, Sato T, Sugiura T, Altaf-Ul-Amin M, Kanaya S (2014) Plant Cell Physiol 55(1):e7. (1–9)CrossRefPubMedGoogle Scholar
  16. Vaishnav P, Demain AL (2010) Unexpected application of secondary metabolites. Biotechnol Adv 29:223–229CrossRefPubMedGoogle Scholar
  17. Weber T, Kim HU (2016) The secondary metabolite bioinformatics portal: computational tools to facilitate synthetic biology of secondary metabolite production. Synth Syst Biotechnol 1:69–79CrossRefPubMedPubMedCentralGoogle Scholar
  18. Xue Y, He Q (2015) Cyanobacteria as cell factories to produce plant secondary metabolites. Front Bioeng Biotechnol 3:57CrossRefPubMedPubMedCentralGoogle Scholar
  19. Zhu XG, Lynch JP, LeBauer DS, Millar AJ, Stitt M, Long SP (2016) Plants in silico: why, why now and what?—An integrative platform for plant systems biology research. Plant Cell Environ 39:1049–1057CrossRefPubMedGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Shradha Sharma
    • 1
  • Pushpa Lohani
    • 1
  1. 1.Department of Molecular Biology and Genetic Engineering, College of Basic Science and HumanitiesGB Pant University of Agriculture & TechnologyPantnagarIndia

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