Pervasive System Biology for Active Compound Valorization in Jatropha

  • Nicolas CarelsEmail author
  • Milena Magalhães
  • Carlyle Ribeiro Lima
  • Bir Bahadur
  • Marcio Argollo de Menezes


Physic nut (Jatropha curcas L.) is a tree in the family Euphorbiaceae whose members have been known for the production of important natural compounds for therapeutic applications. Physic nut is also one of the few important plant species whose genome has been fully sequenced under high scrutiny, mostly because it is a potential source of oil, which could contribute to alleviate the worldwide energy crisis. However, for being a new crop, J. curcas is not yet domesticated to the point of being industrially productive, and a long way is being undertaken to improve it by selective breeding. During the last decade, the scientific community has performed a huge effort to aggregate knowledge to this plant species. The challenge around J. curcas constitutes a fertile ground to look for natural compounds that may serve as scaffolds for new drug applications. In this chapter, we review the principal conceptual strategies that may be taken to valorize natural compounds in the genus Jatropha.


Genome and genetic maps Genome-wide association study Metabolic engineering Pathway modeling Selective breeding 



This contribution was supported by fellowships from the Instituto Nacional de Ciência e Tecnologia de Inovação em Doenças de Populações Negligenciadas (#573642/2008-7) to M.M and C.R.L.


  1. Albert R, Jeong H, Barabasi AL (2000) Error and attack tolerance of complex networks. Nature 406(6794):378–382PubMedCrossRefPubMedCentralGoogle Scholar
  2. Albertsen L, Chen Y, Bach LS et al (2011) Diversion of flux toward sesquiterpene production in Saccharomyces cerevisiae by fusion of host and heterologous enzymes. Appl Environ Microbiol 77:1033–1040PubMedCrossRefPubMedCentralGoogle Scholar
  3. Alla H, David R (1998) Continuous and hybrid Petri nets. J Circ Syst Comput 8:159–188CrossRefGoogle Scholar
  4. Alonso H, Bliznyuk AA, Gready JE (2006) Combining docking and molecular dynamic simulations in drug design. Med Res Rev 26(5):531–568PubMedCrossRefPubMedCentralGoogle Scholar
  5. Alves AA, Laviola BG, Formighieri EF et al (2015) Perennial plants for biofuel production: bridging genomics and field research. Biotechnol J 10(4):505–507PubMedCrossRefPubMedCentralGoogle Scholar
  6. Arnone MI, Davidson EH (1997) The hardwiring of development: organization and function of genomic regulatory systems. Development 124:1851–1864PubMedPubMedCentralGoogle Scholar
  7. Aya K, Hobo T, Sato-Izawa K et al (2014) A novel AP2-type transcription factor, SMALL ORGAN SIZE1, controls organ size downstream of an auxin signaling pathway. Plant Cell Physiol 55:897–912PubMedCrossRefPubMedCentralGoogle Scholar
  8. Azevedo Peixoto L, Laviola BG, Alves AA et al (2017) Breeding Jatropha curcas by genomic selection: a pilot assessment of the accuracy of predictive models. PLoS One 12(3):e0173368Google Scholar
  9. Bader GD, Hogue CW (2002) Analyzing yeast protein-protein interaction data obtained from different sources. Nat Biotechnol 20(10):991–997PubMedCrossRefPubMedCentralGoogle Scholar
  10. Baghalian K, Hajirezaei MR, Schreiber F (2014) Plant metabolic modeling: achieving new insight into metabolism and metabolic engineering. Plant Cell 26(10):3847–3866PubMedPubMedCentralCrossRefGoogle Scholar
  11. Bandyopadhyay S, Kelley R, Krogan NJ et al (2008) Functional maps of protein complexes from quantitative genetic interaction data. PLoS Comput Biol 4(4):e1000065PubMedPubMedCentralCrossRefGoogle Scholar
  12. Barabási A-L (2016) Chapter 5: The Barabási-Albert model. In: Barabási A-L (ed) Network science. Cambridge University Press, Cambridge, p 475 Google Scholar
  13. Barabási AL, Oltvai ZN (2004) Network biology: understanding the cell’s functional organization. Nat Rev Genet 5(2):101–113PubMedCrossRefGoogle Scholar
  14. Barenholz U, Davidi D, Reznik E et al (2017) Design principles of autocatalytic cycles constrain enzyme kinetics and force low substrate saturation at flux branch points. eLife 6:e20667PubMedPubMedCentralCrossRefGoogle Scholar
  15. Bar-Joseph Z, Gerber GK, Lee TI et al (2003) Computational discovery of gene modules and regulatory networks. Nat Biotechnol 21(11):1337–1342PubMedCrossRefPubMedCentralGoogle Scholar
  16. Barroso-González J, El Jaber-Vazdekis N, García-Expósito L et al (2009) The lupane-type triterpene 30-oxo-calenduladiol is a CCR5 antagonist with anti-HIV-1 and anti-chemotactic activities. J Biol Chem 284:16609–16620Google Scholar
  17. Bartell JA, Blazier AS, Yen P et al (2017) Reconstruction of the metabolic network of Pseudomonas aeruginosa to interrogate virulence factor synthesis. Nat Commun 8:14631PubMedPubMedCentralCrossRefGoogle Scholar
  18. Barton NH, Etheridge AM, Véber A (2017) The infinitesimal model: definition, derivation, and implications. Theor Popul Biol 118:50–73PubMedCrossRefPubMedCentralGoogle Scholar
  19. Baryshnikova A, Costanzo M, Kim Y et al (2010) Quantitative analysis of fitness and genetic interactions in yeast on a genome-wide scale. Nat Methods 7:1017–1024PubMedPubMedCentralCrossRefGoogle Scholar
  20. Barzel B, Sharma A, Barabási A-L (2013) Chapter 9: Graph theory properties of cellular networks. In: Walhout M, Vidal M, Dekker J (eds) Handbook of system biology concepts and insights. Elsevier, Academic, Waltham, pp 177–193CrossRefGoogle Scholar
  21. Basha SD, Francis G, Makkar HPS et al (2009) A comparative study of biochemical traits and molecular markers for assessment of genetic relationships between Jatropha curcas L. germplasm from different countries. Plant Sci 176:812–823Google Scholar
  22. Beg QK, Vazquez A, Ernst J et al (2007) Intracellular crowding defines the mode and sequence of substrate uptake by Escherichia coli and constrains its metabolic activity. Proc Natl Acad Sci U S A 104(31):12663–12668PubMedPubMedCentralCrossRefGoogle Scholar
  23. Belhaj K, Chaparro-Garcia A, Kamoun S et al (2013) Plant genome editing made easy: targeted mutagenesis in model and crop plants using the CRISPR/Cas system. Plant Methods 9:39PubMedPubMedCentralCrossRefGoogle Scholar
  24. Bellay J, Atluri G, Sing TL et al (2011) Putting genetic interactions in context through a global modular decomposition. Genome Res 21:1375–1387PubMedPubMedCentralCrossRefGoogle Scholar
  25. Beltrao P, Serrano L (2007) Specificity and evolvability in eukaryotic protein interaction networks. PLoS Comput Biol 3(2):e25PubMedPubMedCentralCrossRefGoogle Scholar
  26. Blazeck J, Alper H (2010) Systems metabolic engineering: genome-scale models and beyond. Biotechnol J 5:647–659PubMedPubMedCentralCrossRefGoogle Scholar
  27. Blazeck J, Garg R, Reed B et al (2012) Controlling promoter strength and regulation in Saccharomyces cerevisiae using synthetic hybrid promoters. Biotechnol Bioeng 109:2884–2895PubMedCrossRefGoogle Scholar
  28. Blount BA, Weenink T, Vasylechko S et al (2012) Rational diversification of a promoter providing fine-tuned expression and orthogonal regulation for synthetic biology. PLoS One 7:1–11Google Scholar
  29. Bohm HJ (1992) The computer program LUDI: a new method for the de novo design of enzyme. J Comput Aided Mol Des 6(1):61–78PubMedCrossRefGoogle Scholar
  30. Bolger M, Schwacke R, Gundlach H et al (2017) From plant genomes to phenotypes. J Biotechnol 261:46–52PubMedCrossRefGoogle Scholar
  31. Bordbar A, Monk JM, King ZA et al (2014) Constraint-based models predict metabolic and associated cellular functions. Nat Rev Genet 15:107–120PubMedCrossRefGoogle Scholar
  32. Boutros M, Agaisse H, Perrimon N (2002) Sequential activation of signaling pathways during innate immune responses in Drosophila. Dev Cell 3:711–722PubMedCrossRefGoogle Scholar
  33. Brady SM, Orlando DA, Lee JY et al (2007) A high-resolution root spatiotemporal map reveals dominant expression patterns. Science 318:801–806PubMedCrossRefGoogle Scholar
  34. Bro C, Regenberg B, Förster J et al (2006) In silico aided metabolic engineering of Saccharomyces cerevisiae for improved bioethanol production. Metab Eng 8:102–111Google Scholar
  35. Bulgakov VP, Avramenko TV, Tsitsiashvili GS (2017) Critical analysis of protein signaling networks involved in the regulation of plant secondary metabolism: focus on anthocyanins. Crit Rev Biotechnol 37(6):685–700PubMedCrossRefGoogle Scholar
  36. Bulyk ML, Walhout AJM (2013) Chapter 4: Gene regulatory networks. In: Walhout M, Vidal M, Dekker J (eds) Handbook of system biology concepts and insights. Elsevier, Academic, Waltham, pp 65–88CrossRefGoogle Scholar
  37. Burgueño J, de los Campos G, Weigel K et al (2012) Genomic prediction of breeding values when modeling genotype × environment interaction using pedigree and dense molecular markers. Crop Sci 52:707–719Google Scholar
  38. Campos ARF, de Lima RLS, de Azevedo CAV et al (2016) Physiological attributes of jatropha under different planting densities and nitrogen doses. R Bras Eng Agríc Ambient 20(12):1112–1117Google Scholar
  39. Carels N (2012) Chapter 1: The birth of a new energy crop. In: Carels N, Sujatha M, Bahadur B (eds) Jatropha, challenges for a new energy crop. Volume 1: Farming, economics and biofuel. Springer, New York, pp 3–12CrossRefGoogle Scholar
  40. Carels N (2013) Chapter 14: Towards the domestication of Jatropha: the integration of sciences. In: Bahadur B, Sujatha M, Carels N (eds) Jatropha, challenges for a new energy crop. Volume 2: Genetic improvement and biotechnology. Springer, New York, pp 263–300CrossRefGoogle Scholar
  41. Carels N, Tilli T, Tuszynski JA (2015) A computational strategy to select optimized protein targets for drug development toward the control of cancer diseases. PLoS One 10(1):e0115054PubMedPubMedCentralCrossRefGoogle Scholar
  42. Carvalho CR, Clarindoa WR, Praça MM et al (2008) Genome size, base composition and karyotype of Jatropha curcas L., an important biofuel plant. Plant Sci 174:613–617CrossRefGoogle Scholar
  43. Carvalho RV, Verbeek FJ, Coelho CJ (2018) Bio-modeling using Petri nets: a computational approach. In: Alves Barbosa da Silva F, Carels N, Paes Silva Junior F (eds) Theoretical and applied aspects of systems biology. Springer, Cham, p 203. CrossRefGoogle Scholar
  44. Carvunis AR, Roth FP, Calderwood MA et al (2013) Chapter 3: Interactome networks. In: Walhout M, Vidal M, Dekker J (eds) Handbook of system biology concepts and insights. Elsevier, Academic, Waltham, pp 45–63CrossRefGoogle Scholar
  45. Caspi R, Altman T, Dreher K et al (2012) The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res 40:D742–D753PubMedCrossRefPubMedCentralGoogle Scholar
  46. Caspi R, Dreher K, Karp PD (2013) The challenge of constructing, classifying and representing metabolic pathways. FEMS Microbiol Lett 345(2):85–93PubMedPubMedCentralCrossRefGoogle Scholar
  47. Chae L, Kim T, Nilo-Poyanco R et al (2014) Genomic signatures of specialized metabolism in plants. Science 344(6183):510–513PubMedCrossRefPubMedCentralGoogle Scholar
  48. Chaouiya C (2007) Petri net modelling of biological networks. Brief Bioinform 8:210–219PubMedCrossRefPubMedCentralGoogle Scholar
  49. Chen J, Haverty J, Deng L et al (2013) Identification of a novel endogenous regulatory element in Chinese hamster ovary cells by promoter trap. J Biotechnol 167:255–261PubMedCrossRefPubMedCentralGoogle Scholar
  50. Chen D, Chen M, Altmann T et al (2014) Chapter 11: Bridging genomics and phenomics. In: Chen M, Hofestädt R (eds) Approaches in integrative bioinformatics: towards the virtual cell. Springer, Berlin/Heidelberg, pp 299–333CrossRefGoogle Scholar
  51. Ci D, Song Y, Du Q et al (2015) Variation in genomic methylation in natural populations of Populus simonii is associated with leaf shape and photosynthetic traits. J Exp Bot 67(3):723–737PubMedCrossRefPubMedCentralGoogle Scholar
  52. Ciliberti S, Martin OC, Wagner A (2007) Circuit topology and the evolution of robustness in complex regulatory gene networks. PLoS Comput Biol 3(2):e15PubMedPubMedCentralCrossRefGoogle Scholar
  53. Costa HPS, Cardoso KC, Del Bem LEV et al (2010) Transcriptome analysis of the oil-rich seed of the bioenergy crop Jatropha curcas L. BMC Genomics 11:462Google Scholar
  54. Costanzo M, Baryshnikova A, Bellay J et al (2010) The genetic landscape of a cell. Science 327:425e31CrossRefGoogle Scholar
  55. Cotterell J, Sharpe J (2010) An atlas of gene regulatory networks reveals multiple three-gene mechanisms for interpreting morphogen gradients. Mol Syst Biol 6:425PubMedPubMedCentralCrossRefGoogle Scholar
  56. Crozier A, Jaganath IB, Clifford MN (2009) Dietary phenolics: chemistry, bioavailability and effects on health. Nat Prod Rep 26:1001–1043PubMedCrossRefPubMedCentralGoogle Scholar
  57. Darvasi A, Weinreb A, Minke V et al (1993) Detection marker-QTL linkage and estimating QTL gene effect and map location using a saturated genetic map. Genetics 134:943–951PubMedPubMedCentralGoogle Scholar
  58. Davidson EH (2010) Emerging properties of animal gene regulatory networks. Nature 468:911–920PubMedPubMedCentralCrossRefGoogle Scholar
  59. de Sant’Anna Q, Machado JR, Rodrigues RP et al (2013) Chapter 31: toward the metabolomics of Jatropha curcas. In: Bahadur B, Sujatha M, Carels N (eds) Jatropha, challenges for a new energy crop, Genetic improvement and biotechnology, vol 2. Springer, New York, pp 577–600CrossRefGoogle Scholar
  60. Deaner M, Alper HS (2018) Promoter and terminator discovery and engineering. In: Zhao H, Zeng A-P (eds) Synthetic biology – metabolic engineering, Advances in Biochemical Engineering/Biotechnology, vol 162. Springer, Cham, pp 21–44CrossRefGoogle Scholar
  61. Deikman J, Petracek M, Heard JE (2012) Drought tolerance through biotechnology: improving translation from the laboratory to farmers’ fields. Curr Opin Biotechnol 23:243–250PubMedCrossRefPubMedCentralGoogle Scholar
  62. Devappa RK, Makkar HPS, Becker K (2011) Jatropha diterpenes: a review. J Am Oil Chem Soc 88:301–322CrossRefGoogle Scholar
  63. Dias LAS, Missio RF, Dias DCFS (2012) Antiquity, botany, origin and domestication of Jatropha curcas (Euphorbiaceae), a plant species with potential for biodiesel production. Genet Mol Res 11:2719–2728PubMedCrossRefPubMedCentralGoogle Scholar
  64. Diaz J, Alvarez-Buylla ER (2006) A model of the ethylene signaling pathway and its gene response in Arabidopsis thaliana: pathway cross-talk and noise-filtering properties. Chaos 16:023112PubMedCrossRefPubMedCentralGoogle Scholar
  65. Ding M-Z, Yan H-F, Li L-F et al (2014) Biosynthesis of taxadiene in Saccharomyces cerevisiae: selection of geranylgeranyl diphosphate synthase directed by a computer-aided docking strategy. PLoS One 9(10):e109348PubMedPubMedCentralCrossRefGoogle Scholar
  66. Doebley JF, Gaut BS, Smith BD (2006) The molecular genetics of crop domestication. Cell 127:1309–1321CrossRefGoogle Scholar
  67. Doi A, Fujita S, Matsuno H et al (2004) Constructing biological pathway models with hybrid functional Petri nets. In Silicon Biol 4:271–291Google Scholar
  68. Du L, Gao R, Forster AC (2009) Engineering multigene expression in vitro and in vivo with small terminators for T7 RNA polymerase. Biotechnol Bioeng 104:1189–1196PubMedPubMedCentralCrossRefGoogle Scholar
  69. Du L, Villarreal S, Forster AC (2012) Multigene expression in vivo: supremacy of large versus small terminators for T7 RNA polymerase. Biotechnol Bioeng 109:1043–1050PubMedCrossRefPubMedCentralGoogle Scholar
  70. Dudareva N, Andersson S, Orlova I et al (2005) The nonmevalonate pathway supports both monoterpene and sesquiterpene formation in snapdragon flowers. Proc Natl Acad Sci U S A 102:933–938PubMedPubMedCentralCrossRefGoogle Scholar
  71. Ehrensperger A, Bach S, Lyimo R et al (2014) Beyond biofuels: jatropha’s multiple uses for farmers in East Africa, vol 1. CDE Policy Brief, Bern, pp 1–4. CrossRefGoogle Scholar
  72. Eshed Y, Zamir D (1995) An introgression line population of Lycopersicon pennellii in the cultivated tomato enables the identification and fine mapping of yield-associated QTL. Genetics 141(3):1147–1162PubMedPubMedCentralGoogle Scholar
  73. Espinosa-Soto C, Padilla-Longoria P, Alvarez-Buylla ER (2004) A gene regulatory network model for cell-fate determination during Arabidopsis thaliana flower development that is robust and recovers experimental gene expression profiles. Plant Cell 16:2923–2939PubMedPubMedCentralCrossRefGoogle Scholar
  74. Fehér T, Burland V, Pósfai G (2012) In the fast lane: large-scale bacterial genome engineering. J Biotechnol 160:72–79PubMedCrossRefPubMedCentralGoogle Scholar
  75. Feist AM, Palsson BO (2010) The biomass objective function. Curr Opin Microbiol 13:344–349PubMedPubMedCentralCrossRefGoogle Scholar
  76. Feldmann KA (1991) T-DNA insertion mutagenesis in Arabidopsis: mutational spectrum. Plant J 1:70–82CrossRefGoogle Scholar
  77. Fernando RL, Garrick D (2013) Bayesian methods applied to GWAS. In: Gondro C, van der Werf J, Hayes B (eds) Genome-wide asociation studies and genomic prediction, Methods in Molecular Biology (Methods and Protocols), vol 1019. Humana Press, Totowa, pp 237–274 SpringerGoogle Scholar
  78. Fernie AR, Morgan JA (2013) Analysis of metabolic flux using dynamic labelling and metabolic modelling. Plant Cell Environ 36:1738–1750PubMedCrossRefPubMedCentralGoogle Scholar
  79. Firn RD, Jones CG (2000) The evolution of secondary metabolism – a unifying model. Mol Microbiol 37(5):989–994PubMedCrossRefPubMedCentralGoogle Scholar
  80. Floudas CA, Fung HK, McAllister SR et al (2006) Advances in protein structure prediction and de novo protein design: a review. Chem Eng Sci 61(3):966–988CrossRefGoogle Scholar
  81. Fondi M, Pinatel E, Talà A (2017) Time-resolved transcriptomics and constraint-based modeling identify system-level metabolic features and overexpression targets to increase spiramycin production in Streptomyces ambofaciens. Front Microbiol 8:835PubMedPubMedCentralCrossRefGoogle Scholar
  82. Franco MC, Gomes KA, de Carvalho Filho MM et al (2016) Agrobacterium-mediated transformation of Jatropha curcas leaf explants with a fungal chitinase gene. Afr J Biotechnol 15:2006–2016Google Scholar
  83. Franke L, van Bakel H, Fokkens L et al (2006) Reconstruction of a functional human gene network, with an application for prioritizing positional candidate genes. Am J Hum Genet 78(6):1011–1025Google Scholar
  84. Fraser HB, Plotkin JB (2007) Using protein complexes to predict phenotypic effects of gene mutation. Genome Biol 8(11):R252PubMedPubMedCentralCrossRefGoogle Scholar
  85. Friedman N, Linial M, Nachman I et al (2000) Using Bayesian network to analyze expression data. J Comput Biol 7:601–620PubMedCrossRefGoogle Scholar
  86. Fukuhara S, Muakrong N, Kikuchi S et al (2016) Cytological characterization of an interspecific hybrid in Jatropha and its progeny reveals preferential uniparental chromosome transmission and interspecific translocation. Breed Sci 66:838–844PubMedPubMedCentralCrossRefGoogle Scholar
  87. Galanie S, Thodey K, Trenchard IJ et al (2015) Complete biosynthesis of opioids in yeast. Science 349(6252):1095–1100PubMedPubMedCentralCrossRefGoogle Scholar
  88. Galli V, Guzman F, de Oliveira LF et al (2014) Identifying microRNAs and transcript targets in Jatropha seeds. PLoS One 9:e83727Google Scholar
  89. Gandhi SG, Mahajan V, Bedi YS (2015) Changing trends in biotechnology of secondary metabolism in medicinal and aromatic plants. Planta 241:303–317PubMedCrossRefPubMedCentralGoogle Scholar
  90. Garcia-Ruiz E, HamediRad M, Zhao H (2018) Pathway design, engineering and optimization. Adv Biochem Eng Biotechnol 162:77–116PubMedGoogle Scholar
  91. Garrick D, Fernando RL (2013) Implementing a QTL detection study (GWAS) using genomic prediction methodology. In: Gondro C, van der Werf J, Hayes B (eds) Genome-wide association studies and genomic prediction, Methods in Molecular Biology (Methods and Protocols), vol 1019. Humana Press, Totowa, pp 275–298 SpringerGoogle Scholar
  92. Gavin AC, Bosche M, Krause R et al (2002) Functional organization of the yeast proteome by systematic analysis of protein complexes. Nature 415(6868):141–147PubMedCrossRefPubMedCentralGoogle Scholar
  93. Gavin AC, Aloy P, Grandi P et al (2006) Proteome survey reveals modularity of the yeast cell machinery. Nature 440(7084):631–636PubMedPubMedCentralCrossRefGoogle Scholar
  94. Ge H, Liu Z, Church GM et al (2001) Correlation between transcriptome and interactome mapping data from Saccharomyces cerevisiae. Nat Genet 29(4):482–486PubMedCrossRefGoogle Scholar
  95. Ghosh A, Chikara J, Chaudhary DR et al (2010) Paclobutrazol arrests vegetative growth and unveils unexpressed yield potential of Jatropha curcas. J Plant Growth Regul 29(3):307–315CrossRefGoogle Scholar
  96. Giadrossich F, Cohen D, Schwarz M et al (2016) Modeling bio-engineering traits of Jatropha curcas L. Ecol Eng 89:40–48CrossRefGoogle Scholar
  97. Gibson G (2012) Rare and common variants: twenty arguments. Nat Rev Genet 13(2):135–145PubMedPubMedCentralCrossRefGoogle Scholar
  98. Goel G, Makkar HPS, Francis G et al (2007) Phorbol esters: structure, biological activity, and toxicity in animals. Int J Toxicol 26:279–288PubMedCrossRefGoogle Scholar
  99. Goentoro L, Shoval O, Kirschner MW et al (2009) The incoherent feedforward loop can provide fold-change detection in gene regulation. Mol Cell 36:894–899PubMedPubMedCentralCrossRefGoogle Scholar
  100. Goh KI, Cusick ME, Valle D et al (2007) The human disease network. Proc Natl Acad Sci U S A 104(21):8685–8690PubMedPubMedCentralCrossRefGoogle Scholar
  101. Gomes KA, Almeida TC, Gesteira AS et al (2010) ESTs from seeds to assist the selective breeding of Jatropha curcas L. for oil and active compounds. Genomics Insights 3:29–56PubMedPubMedCentralCrossRefGoogle Scholar
  102. Grafahrend-Belau E, Junker A, Eschenröder A et al (2013) Multiscale metabolic modeling: dynamic flux balance analysis on a whole-plant scale. Plant Physiol 163:637–647PubMedPubMedCentralCrossRefGoogle Scholar
  103. Grover A, Kumari M, Singh S et al (2014) Analysis of Jatropha curcas transcriptome for oil enhancement and genic markers. Physiol Mol Biol Plants 20:139–142PubMedCrossRefPubMedCentralGoogle Scholar
  104. Gupta P, Idris A, Mantri S et al (2012) Discovery and use of single nucleotide polymorphic (SNP) markers in Jatropha curcas L. Mol Breed 30:1325–1335CrossRefGoogle Scholar
  105. Gupta PK, Kulwal KL, Jaiswal V (2014) Association mapping in crop plants: opportunities and challenges. Adv Genet 85:109–147PubMedCrossRefGoogle Scholar
  106. Hannum G, Srivas R, Guénolé A et al (2009) Genome-wide association data reveal a global map of genetic interactions among protein complexes. PLoS Genet 5(12):e1000782PubMedPubMedCentralCrossRefGoogle Scholar
  107. Hao K, Chudin E, Greenawalt D et al (2010) Magnitude of stratification in human populations and impacts on genome wide association studies. PLoS One 5(1):e8695PubMedPubMedCentralCrossRefGoogle Scholar
  108. Harbison CT, Gordon DB, Lee TI et al (2004) Transcriptional regulatory code of a eukaryotic genome. Nature 431:99–104PubMedPubMedCentralCrossRefGoogle Scholar
  109. Hardy S, Robillard PN (2008) Petri net-based method for the analysis of the dynamics of signal propagation in signaling pathways. Bioinformatics 24:209–217PubMedCrossRefGoogle Scholar
  110. Harry-Asobara JL, Samson EO (2014) Comparative study of the phytochemical properties of Jatropha curcas and Azadirachta indica plant extracts. J Poison Med Plants Res 2(2):20–24Google Scholar
  111. He X, Zhang J (2006) Why do hubs tend to be essential in protein networks? PLoS Genet 2:826e34CrossRefGoogle Scholar
  112. Henry CS, Broadbelt LJ, Hatzimanikatis V (2010) Discovery and analysis of novel metabolic pathways for the biosynthesis of industrial chemicals: 3-hydroxypropanoate. Biotechnol Bioeng 106(3):462–473PubMedGoogle Scholar
  113. Herrera JM, Jimenez Martinez CJ, Vera NG (2012) Chapter 17: Use of Jatropha curcas L. (non-toxic variety) as traditional food and generation of new products in Mexico. In: Carels N, Sujatha M, Bahadur B (eds) Jatropha, challenges for a new energy crop. Volume 1: Farming, economics and biofuel. Springer, New York, pp 333–342CrossRefGoogle Scholar
  114. Hirakawa H, Tsuchimoto S, Sakai H et al (2012) Upgraded genomic information of Jatropha curcas L. Plant Biotechnol 129:123–130CrossRefGoogle Scholar
  115. Höltje HD, Folkers G (2008) Small molecules. Molecular modeling. Wiley-VCH Verlag GmbH, New York, pp 9–63CrossRefGoogle Scholar
  116. Hordijk W, Steel M, Dittrich P (2018) Autocatalytic sets and chemical organizations: modeling self-sustaining reaction networks at the origin of life. New J Phys 20:015011CrossRefGoogle Scholar
  117. Hui W-K, Wang Y, Chen X-Y et al (2018) Analysis of transcriptional responses of the inflorescence meristems in Jatropha curcas following gibberellin treatment. Int J Mol Sci 19(2):E432PubMedCrossRefGoogle Scholar
  118. Ibarra RU, Edwards JS, Palsson BO (2002) Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth. Nature 420(6912):186–189PubMedCrossRefGoogle Scholar
  119. Ideker T, Krogan NJ (2012) Differential network biology. Mol Syst Biol 8:565PubMedPubMedCentralCrossRefGoogle Scholar
  120. Ihmels J, Friedlander G, Bergmann S et al (2002) Revealing modular organization in the yeast transcriptional network. Nat Genet 31(4):370–377PubMedCrossRefGoogle Scholar
  121. Jansen R, Greenbaum D, Gerstein M (2002) Relating whole-genome expression data with proteine-protein interactions. Genome Res 12(1):37–46PubMedPubMedCentralCrossRefGoogle Scholar
  122. Jansen R, Yu H, Greenbaum D et al (2003) A Bayesian networks approach for predicting protein-protein interactions from genomic data. Science 302(5644):449–453PubMedCrossRefGoogle Scholar
  123. Jiang H, Wu P, Zhang S et al (2012) Global analysis of gene expression profiles in developing physic nut (Jatropha curcas L.) seeds. PLoS One 7(5):e36522PubMedPubMedCentralCrossRefGoogle Scholar
  124. Jin J, Liu J, Wang H et al (2013) PLncDB: plant long non-coding RNA database. Bioinformatics 29(8):1068–1071PubMedPubMedCentralCrossRefGoogle Scholar
  125. Jingura RM, Kamusoko R (2015) A multi-factor evaluation of Jatropha as a feedstock for biofuels: the case of sub-Saharan Africa. Biofuel Res J 7:254–257CrossRefGoogle Scholar
  126. Joshi G, Shukla A, Shukla A (2011) Synergistic response of auxin and ethylene on physiology of Jatropha curcas L. Braz J Plant Physiol 23(1):67–77CrossRefGoogle Scholar
  127. Jothi R, Balaji S, Wuster A et al (2009) Genomic analysis reveals a tight link between transcription factor dynamics and regulatory network architecture. Mol Syst Biol 5:294PubMedPubMedCentralCrossRefGoogle Scholar
  128. Julsing MK, Koulman A, Woerdenbag HJ et al (2006) Combinatorial biosynthesis of medicinal plant secondary metabolites. Biomol Eng 23(6):265–279PubMedCrossRefGoogle Scholar
  129. Juven-Gershon T, Hsu JY, Kadonaga JT (2006) Perspectives on the RNA polymerase II core promoter. Biochem Soc Trans 34:1047–1050PubMedCrossRefGoogle Scholar
  130. Juven-Gershon T, Hsu JY, Theisen JW et al (2008) The RNA polymerase II core promoter – the gateway to transcription. Curr Opin Cell Biol 20:253–259PubMedPubMedCentralCrossRefGoogle Scholar
  131. Kanehisa M, Goto S (2000) KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28(1):27–30PubMedPubMedCentralCrossRefGoogle Scholar
  132. Kanehisa M, Goto S, Furumichi M, Tanabe M, Hirakawa M (2010) KEGG for representation and analysis of molecular networks involving diseases and drugs. Nucleic Acids Res 38:D355–DD60PubMedCrossRefGoogle Scholar
  133. Kapetanovic IM (2008) Computer-aided drug discovery and development (CADDD): in silico-chemico-biological approach. Chem Biol Interact 171(2):165–176PubMedCrossRefGoogle Scholar
  134. Kapitzky L, Beltrao P, Berens TJ et al (2010) Cross-species chemogenomic profiling reveals evolutionarily conserved drug mode of action. Mol Syst Biol 6:451PubMedPubMedCentralCrossRefGoogle Scholar
  135. Katagi A, Sui L, Kamitori K et al (2016) inhibitory effect of isoamericanol A from Jatropha curcas seeds on the growth of MCF-7 human breast cancer cell line by G2/M cellcyclearrest. Heliyon 2(1):e00055PubMedPubMedCentralCrossRefGoogle Scholar
  136. Kelley R, Ideker T (2005) Systematic interpretation of genetic interactions using protein networks. Nat Biotechnol 23:561–566PubMedPubMedCentralCrossRefGoogle Scholar
  137. Khatri S, Saini RV, Chhillar AK (2017) Chapter 3: Molecular farming approach towards bioactive compounds. In: Kalia VC, Saini AK (eds) Metabolic engineering for bioactive compounds. Springer, Singapore, pp 49–72CrossRefGoogle Scholar
  138. Kim M, Sang Yi J, Kim J et al (2014) Reconstruction of a high-quality metabolic model enables the identification of gene overexpression targets for enhanced antibiotic production in Streptomyces coelicolor A3(2). Biotechnol J 9(9):1185–1194PubMedCrossRefGoogle Scholar
  139. King AJ, Montes LR, Clarke JG et al (2013) Linkage mapping in the oilseed crop Jatropha curcas L. reveals a locus controlling the biosynthesis of phorbol esters which cause seed toxicity. Plant Biotechnol J 11:986–996PubMedPubMedCentralCrossRefGoogle Scholar
  140. Knapp SJ, Bridges WC (1990) Using molecular markers to estimate quantitative trait locus parameters: power and genetic variances for unreplicated and replicated progeny. Genetics 126:769–777PubMedPubMedCentralGoogle Scholar
  141. Koch I, Junker BH, Heiner M (2005) Application of Petri net theory for modelling and validation of the sucrose breakdown pathway in the potato tuber. Bioinformatics 21:1219–1226PubMedCrossRefGoogle Scholar
  142. Koch I, Reisig W, Schreiber F (2011) In: Koch I, Reisig W, Schreiber F (eds) Modeling in systems biology: the Petri net approach. Springer, London, p 365CrossRefGoogle Scholar
  143. Korte A, Farlow A (2013) The advantages and limitations of trait analysis with GWAS: a review. Plant Methods 9:29PubMedPubMedCentralCrossRefGoogle Scholar
  144. Krivoruchko A, Siewers V, Nielsen J (2011) Opportunities for yeast metabolic engineering: lessons from synthetic biology. Biotechnol J 6:262–276PubMedCrossRefGoogle Scholar
  145. Krogan NJ, Cagney G, Yu H et al (2006) Global landscape of protein complexes in the yeast Saccharomyces cerevisiae. Nature 440:637–643PubMedPubMedCentralCrossRefGoogle Scholar
  146. Kujur A, Upadhyaya HD, Bajaj D et al (2016) Identification of candidate genes and natural allelic variants for QTLs governing plant height in chickpea. Sci Rep 6:27968PubMedPubMedCentralCrossRefGoogle Scholar
  147. Kulkarni MM, Perrimon N (2013) Chapter 5: Analyzing the structure, function and information flow in signaling networks using quantitative cellular signatures. In: Walhout M, Vidal M, Dekker J (eds) Handbook of system biology concepts and insights. Elsevier, Academic, Waltham, pp 89–113CrossRefGoogle Scholar
  148. Kumar GRK, Bapat VA, Sudhakar Johnson TS (2012) Chapter 24: Phorbol esters and other toxic constituents of Jatropha curcas L. In: Carels N, Sujatha M, Bahadur B (eds) Jatropha, challenges for a new energy crop. Volume 1: Farming, economics and biofuel. Springer, New York, pp 441–462CrossRefGoogle Scholar
  149. Kumar N, Reddy MP, Sujatha M (2013) Chapter 28: Genetic transformation of Jatropha curcas: current status and future prospects. In: Bahadur B, Sujatha M, Carels N (eds) Jatropha, challenges for a new energy crop. Volume 2: Genetic improvement and biotechnology. Springer, New York, pp 535–546CrossRefGoogle Scholar
  150. Kumar V, Singh A, Mithra SV et al (2015) Genome-wide association mapping of salinity tolerance in rice (Oryza sativa). DNA Res 22(2):133–145PubMedPubMedCentralCrossRefGoogle Scholar
  151. Laviola BG, Alves AA, Rosado TB et al (2018) Establishment of new strategies to quantify and increase the variability in the Brazilian Jatropha genotypes. Ind Crop Prod 117:216–223CrossRefGoogle Scholar
  152. Lee TI, Young RA (2000) Transcription of eukaryotic protein-coding genes. Annu Rev Genet 34:77–137PubMedCrossRefGoogle Scholar
  153. Lee TI, Rinaldi NJ, Robert F et al (2002) Transcriptional regulatory networks in Saccharomyces cerevisiae. Science 298:799–804PubMedCrossRefGoogle Scholar
  154. Levy ED, Pereira-Leal JB (2008) Evolution and dynamics of protein interactions and networks. Curr Opin Struct Biol 18(3):349–357PubMedCrossRefGoogle Scholar
  155. Li B, Leal SM (2008) Methods for detecting associations with rare variants for common diseases: application to analysis of sequence data. Am J Hum Genet 83(3):311–321PubMedPubMedCentralCrossRefGoogle Scholar
  156. Li S, Assmann SM, Albert R (2006) Predicting essential components of signal transduction networks: a dynamic model of guard cell abscisic acid signaling. PLoS Biol 4:e312PubMedPubMedCentralCrossRefGoogle Scholar
  157. Li J, Bluemling B, Mol APJ et al (2014) Stagnating Jatropha biofuel development in Southwest China: an institutional approach. Sustainability 6:3192–3212CrossRefGoogle Scholar
  158. Li H, Tsuchimoto S, Harada K et al (2017a) Genetic tracing of Jatropha curcas L. from its Mesoamerican origin to the world. Front Plant Sci 8:1539PubMedPubMedCentralCrossRefGoogle Scholar
  159. Li H, Tsuchimoto S, Harada K et al (2017b) Chapter 10: The genome-wide association study. In: Tsuchimoto S (ed) The Jatropha genome, compendium of plant genomes. Springer, New York, pp 159–173CrossRefGoogle Scholar
  160. Liang J, Zhou M, Zhou X et al (2013) JcLEA, a novel LEA-like protein from Jatropha curcas, confers a high level of tolerance to dehydration and salinity in Arabidopsis thaliana. PLoS One 8:e83056PubMedPubMedCentralCrossRefGoogle Scholar
  161. Liu K, Yang Q, Ge Z et al (2012) Simulation of Jatropha curcas L. root in response to water stress based on 3D visualization. Procedia Eng 28:403–408CrossRefGoogle Scholar
  162. Lommen WJM, Schenk E, Bouwmeester HJ et al (2006) Trichome dynamics and artemisinin accumulation during development and senescence of Artemisia annua leaves. Planta Med 72:336–345PubMedCrossRefPubMedCentralGoogle Scholar
  163. Lu C, Jeffries T (2007) Shuffling of promoters for multiple genes to optimize xylose fermentation in an engineered Saccharomyces cerevisiae strain. Appl Environ Microbiol 73:6072–6077PubMedPubMedCentralCrossRefGoogle Scholar
  164. Lucho-Constantino GG, Zaragoza-Martínez F, Ponce-Noyola T et al (2017) Antioxidant responses under jasmonic acid elicitation comprise enhanced production of flavonoids and anthocyanins in Jatropha curcas leaves. Acta Physiol Plant 39:165CrossRefGoogle Scholar
  165. Maghuly F, Jankowicz J, Till B et al (2013) The use of EcoTILLING for the genetic improvement of Jatropha curcas L. In: Bahadur B, Sujatha M, Carels N (eds) Jatropha, challenges for a new energy crop. Volume 2: Genetic improvement and biotechnology. Springer, New York, pp 335–350CrossRefGoogle Scholar
  166. Maranas C, Zomorrodi A (2016) Optimization methods in metabolic networks. Wiley, HobokenCrossRefGoogle Scholar
  167. Marchini J, Howie B, Myers S et al (2007) A new multipoint method for genome-wide association studies via imputation of genotypes. Nat Genet 8:1750–1761Google Scholar
  168. Marinho ACTA, Vasconcelos S, Vasconcelos EV et al (2018) Karyotype and genome size comparative analyses among six species of the oilseed-bearing genus Jatropha (Euphorbiaceae). Genet Mol Biol 41:442–449. CrossRefPubMedPubMedCentralGoogle Scholar
  169. Marques DA, Siqueira WJ, Colombo CA et al (2013) Chapter 23: Breeding and biotechnology of Jatropha curcas. In: Bahadur B, Sujatha M, Carels N (eds) Jatropha, challenges for a new energy crop. Volume 2: Genetic improvement and biotechnology. Springer, New York, pp 457–478CrossRefGoogle Scholar
  170. Mat NHC, Yaakob Z, Ratnam W (2016) Stability of agronomic and yield related traits of Jatropha curcas accessions raised from cuttings. AIP Conf Proc 1784:060041. CrossRefGoogle Scholar
  171. Maurya R, Gupta A, Singh SK et al (2015) Genomic-derived microsatellite markers for diversity analysis in Jatropha curcas. Trees 29(3):1–10CrossRefGoogle Scholar
  172. Mendoza L, Thieffry D, Alvarez-Buylla ER (1999) Genetic control of flower morphogenesis in Arabidopsis thaliana: a logical analysis. Bioinformatics 15:593–606PubMedCrossRefGoogle Scholar
  173. Meuwissen THE, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157(4):1819–1829PubMedPubMedCentralGoogle Scholar
  174. Mmopelwa G, Kgathi DL, Kashe K, Chanda R (2017) Economic sustainability of Jatropha cultivation for biodiesel production: lessons from Southern Africa. J Fundam Renew Energy Appl 7:6Google Scholar
  175. Mochida K, Tran L-SP (2017) Chapter 3: Transcription factors in Jatropha. In: Tsuchimoto S (ed) The Jatropha genome, compendium of plant genomes. Springer, New York, pp 47–60CrossRefGoogle Scholar
  176. Monod J (1942) Recherches sur la croissance des cultures bactériennes. Hermann & cie, ParisGoogle Scholar
  177. Monod J (1947) The phenomenon of enzymatic adaptation – and its bearings on problems of genetics and cellular differentiation. Growth 11:223–289Google Scholar
  178. Monod J (1949) The growth of bacterial cultures. Annu Rev Microbiol 3(1):371–394CrossRefGoogle Scholar
  179. Montes JM, Melchinger A (2016) Domestication and breeding of Jatropha curcas L. Trends Plant Sci 21(12):1045–1057PubMedCrossRefGoogle Scholar
  180. Montes Osorio LR, Torres Salvador AF, Jongschaap RE et al (2014) High level of molecular and phenotypic biodiversity in Jatropha curcas from Central America compared to Africa, Asia and South America. BMC Plant Biol 14:77PubMedPubMedCentralCrossRefGoogle Scholar
  181. Moreno-Risueno MA, Busch W, Benfey P (2010) Omics meet networks – using systems approaches to infer regulatory networks in plants. Curr Opin Plant Biol 13:126–131PubMedCrossRefGoogle Scholar
  182. Morgan JA, Rhodes D (2002) Mathematical modeling of plant metabolic pathways. Metab Eng 4(1):80–89PubMedCrossRefGoogle Scholar
  183. Moumbock AFA, Simoben CV, Wessjohann L et al (2017) Chapter 10: Computational studies and biosynthesis of natural products with promising anticancer properties. In: Badria Farid BA (ed) Natural products and cancer drug discovery. InTech, Rijeka, pp 257–285. CrossRefGoogle Scholar
  184. Mulualem T, Bekeko Z (2016) Advances in quantitative trait loci, mapping and importance of markers assisted selection in plant breeding research. Int J Plant Breed Genet 10:58–68CrossRefGoogle Scholar
  185. Mura I, Csikász-Nagy A (2008) Stochastic Petri net extension of a yeast cell cycle model. J Theor Biol 254:850–860PubMedCrossRefGoogle Scholar
  186. Myronovskyia M, Luzhetskyy A (2016) Native and engineered promoters in natural product discovery. Nat Prod Rep 33:1006–1019. CrossRefGoogle Scholar
  187. Na Chiangmai P, Pootaengon Y, Meetum P et al (2014) Mutation induction in physic nut (Jatropha curcas L.) by colchicine treatments. Silpakorn Univ Sci Tech J 8(2):28–39Google Scholar
  188. Nägele T, Weckwerth W (2012) Mathematical modeling of plant metabolism-from reconstruction to prediction. Metabolites 2(3):553–566PubMedPubMedCentralCrossRefGoogle Scholar
  189. Natarajan P, Parani M (2011) De novo assembly and transcriptome analysis of five major tissues of Jatropha curcas L. using GS FLX titanium platform of 454 pyrosequencing. BMC Genomics 12:191PubMedPubMedCentralCrossRefGoogle Scholar
  190. Natarajan P, Kanagasabapathy D, Gunadayalan G et al (2010) Gene discovery from Jatropha curcas by sequencing of ESTs from normalized and full-length enriched cDNA library from developing seeds. BMC Genomics 11:606PubMedPubMedCentralCrossRefGoogle Scholar
  191. Nayeem A, Sitkoff D, Krystek S (2006) A comparative study of available software for high accuracy homology modeling: from sequence alignments to structural models. Protein Sci 15(4):808–824PubMedPubMedCentralCrossRefGoogle Scholar
  192. Neidhardt FC (1999) Bacterial growth: constant obsession with dn/dt. J Bacteriol 181:7405–7408PubMedPubMedCentralGoogle Scholar
  193. Neidhardt FC, Ingraham JL, Schaechter M (1990) Physiology of the bacterial cell: a molecular approach. Sinauer Associates, Sunderland, p 507Google Scholar
  194. Niu GH, Rodriguez D, Mendoza M et al (2012) Responses of Jatropha curcas to salt and drought stresses. Int J Agron 2012:1–7. CrossRefGoogle Scholar
  195. Nwokocha Blessing A, Agbagwa IO, Okoli BE (2011) Comparative phytochemical screening of Jatropha L. species in the Niger Delta. Res J Phytochem 5:107–114CrossRefGoogle Scholar
  196. O’Brien EJ, Monk JM, Palsson BO (2015) Using genome-scale models to predict biological capabilities. Cell 161(5):971–987PubMedPubMedCentralCrossRefGoogle Scholar
  197. Ohashi-Ito K, Matsukawa M, Fukuda H (2013) An atypical bHLH transcription factor regulates early xylem development downstream of auxin. Plant Cell Physiol 54:398–405PubMedCrossRefPubMedCentralGoogle Scholar
  198. Ohlson T, Wallner B, Elofsson A (2004) Profile–profile methods provide improved fold-recognition: a study of different profile–profile alignment methods. Protein: Struct Funct Bioinforms 57(1):188–197CrossRefGoogle Scholar
  199. Ohtani M, Nakano Y, Usami T, Demura T (2012) Comparative metabolome analysis of seed kernels in phorbol ester-containing and phorbol ester-free accessions of Jatropha curcas L. Plant Biotechnol 29:171–174CrossRefGoogle Scholar
  200. One KT, Muakrong N, Phetcharat C et al (2014a) Inheritance of dwarfiness and erect growth habit in progenies of Jatropha curcas × Jatropha integerrima. J Am Soc Hort Sci 139(5):582–586CrossRefGoogle Scholar
  201. One KT, Muakrong N, Tanya P et al (2014) Physicochemical properties of seeds and oil from an F2 population of Jatropha curcas × Jatropha integerrima. Science Asia 40:428–435CrossRefGoogle Scholar
  202. Orth JD, Thiele I, Palsson BO (2010) What is flux balance analysis? Nat Biotechnol 28(3):245–248PubMedPubMedCentralCrossRefGoogle Scholar
  203. Osorio LRM, Salvador AFT, Jongschaap REE et al (2014) High level of molecular and phenotypic biodiversity in Jatropha curcas from Central America compared to Africa, Asia and South America. BMC Plant Biol 14:77CrossRefGoogle Scholar
  204. Ovando-Medina I, Adriano-Anaya L, Vázquez-Ovando A et al (2013) Chapter 12: Genetic diversity of Jatropha curcas in Southern Mexico. In: Bahadur B, Sujatha M, Carels N (eds) Jatropha, challenges for a new energy crop. Volume 2: Genetic improvement and biotechnology. Springer, New York, pp 219–250CrossRefGoogle Scholar
  205. Paddon CJ, Keasling JD (2014) Semi-synthetic artemisinin: a model for the use of synthetic biology in pharmaceutical development. Nat Rev Microbiol 12(5):355–367PubMedCrossRefPubMedCentralGoogle Scholar
  206. Papin JA, Hunter T, Palsson BO et al (2005) Reconstruction of cellular signalling networks and analysis of their properties. Nat Rev Mol Cell Biol 6:99–111PubMedCrossRefPubMedCentralGoogle Scholar
  207. Pearl J (1988) Probabilistic reasoning in intelligent systems. Morgan Kaufmann, San Mateo, p 552Google Scholar
  208. Pecina-Quintero V, Anaya-Lopeza JL, Colmenero AZ et al (2011) Molecular characterisation of Jatropha curcas L. genetic resources from Chiapas, Mexico through AFLP markers. Biomass Bioenergy 35:1897–1905CrossRefGoogle Scholar
  209. Pecina-Quintero V, Anaya-Lopeza JL, Colmenero AZ et al (2014) Genetic structure of Jatropha curcas L. in Mexico and probable center of origin. Biomass Bioenergy 60:147–155CrossRefGoogle Scholar
  210. Pecoul B, Batista C, Stobbaerts E et al (2016) The BENEFIT trial: where do we go from here? PLoS Negl Trop Dis 10(2):e0004343PubMedPubMedCentralCrossRefGoogle Scholar
  211. Peleg M, Rubin D, Altman RB (2005) Using Petri net tools to study properties and dynamics of biological systems. J Am Med Inform Assoc 12:181–199PubMedPubMedCentralCrossRefGoogle Scholar
  212. Pichersky E, Lewinsohn E (2011) Convergent evolution in plant specialized metabolism. Annu Rev Plant Biol 62:549–566PubMedCrossRefGoogle Scholar
  213. Pontiller J, Gross S, Thaisuchat H et al (2008) Identification of CHO endogenous promoter elements based on a genomic library approach. Mol Biotechnol 39:135–139PubMedCrossRefGoogle Scholar
  214. Pontiller J, Maccani A, Baumann M et al (2010) Identification of CHO endogenous gene regulatory elements. Mol Biotechnol 45:235–240PubMedCrossRefGoogle Scholar
  215. Poulter CD, Wiggins PL, Le AT (1981) Farnesylpyrophosphate synthetase. A stepwise mechanism for the 1′-4 condensation reaction. J Am Chem Soc 103:3926–3927CrossRefGoogle Scholar
  216. Pullaiah T, Bahadur B (2013) Chapter 11: Economic and medicinal importance of Jatrophas. In: Bahadur B, Sujatha M, Carels N (eds) Jatropha, challenges for a new energy crop. Volume 2: Genetic improvement and biotechnology. Springer, New York, pp 187–217CrossRefGoogle Scholar
  217. Putranto DH, Tongkra T, Chutteang C et al (2014) Growth and physiological response of Jatropha interspecific hybrid (Jatropha curcas × J. integerrima) under salt stress. Int J Adv Sci Eng Inf Technol 4(2):18–23Google Scholar
  218. Ramachandran N, Hainsworth E, Bhullar B et al (2004) Self-assembling protein microarrays. Science 305(5680):86–90PubMedCrossRefGoogle Scholar
  219. Raposo RS, Souza IG, Veloso ME et al (2014) Development of novel simple sequence repeat markers from a genomic sequence survey database and their application for diversity assessment in Jatropha curcas germplasm from Guatemala. Genet Mol Res 13:6099–6106PubMedCrossRefPubMedCentralGoogle Scholar
  220. Ravasz E, Somera AL, Mongru DA et al (2002) Hierarchical organization of modularity in metabolic networks. Science 297:1551e5CrossRefGoogle Scholar
  221. Ravindranath N, Reddy MR, Mahender G et al (2004) Deoxypreussomerins from Jatropha curcas: are they also plant metabolites? Phytochemistry 65:2387–2390PubMedCrossRefGoogle Scholar
  222. Rhee HS, Pugh BF (2011) Comprehensive genome-wide protein-DNA interactions detected at single-nucleotide resolution. Cell 147:1408–1419PubMedPubMedCentralCrossRefGoogle Scholar
  223. Riechmann JL, Heard J, Martin G et al (2000) Arabidopsis transcription factors: genome-wide comparative analysis among eukaryotes. Science 290:2105–2110PubMedCrossRefGoogle Scholar
  224. Roguev A, Bandyopadhyay S, Zofall M et al (2008) Conservation and rewiring of functional modules revealed by an epistasis map in fission yeast. Science 322(5900):405–410PubMedPubMedCentralCrossRefGoogle Scholar
  225. Rosado TB, Laviola BG, Faria DA et al (2010) Molecular markers reveal limited genetic diversity in a large germplasm collection of the biofuel crop Jatropha curcas L. in Brazil. Crop Sci 50(6):2372–2382CrossRefGoogle Scholar
  226. Rumzhum NN, Sohrab MH, Al-Mansur MA et al (2012) Secondary metabolites from Jatropha podagrica Hook. J Phys Sci 23:29–37Google Scholar
  227. Sabandar CW, Ahmat N, Jaafar FM et al (2013) Medicinal property, phytochemistry and pharmacology of several Jatropha species (Euphorbiaceae): a review. Phytochemistry 85:7–29PubMedCrossRefGoogle Scholar
  228. Sackmann A, Heiner M, Koch I (2006) Application of Petri net based analysis techniques to signal transduction pathways. BMC Bioinformatics 7:482PubMedPubMedCentralCrossRefGoogle Scholar
  229. Sahidin I, Nakazibwe S, Taher M et al (2011) Antiproliferation of curcusone B from Jatropha curcas on human cancer cell lines. Aust J Basic Appl Sci 5(8):47–51Google Scholar
  230. Saka Y, Smith JC (2007) A mechanism for the sharp transition of morphogen gradient interpretation in Xenopus. BMC Dev Biol 7:47PubMedPubMedCentralCrossRefGoogle Scholar
  231. Sakuma S, Salomon B, Komatsuda T (2011) The domestication syndrome genes responsible for the major changes in plant form in the Triticeae crops. Plant Cell Physiol 52:738–749PubMedPubMedCentralCrossRefGoogle Scholar
  232. Salvador-Figueroa M, Magana-Ramos J, Vazquez-Ovando JA et al (2014) Genetic diversity and structure of Jatropha curcas L. in its centre of origin. Plant Genet Resour Char Util 13:9–17. CrossRefGoogle Scholar
  233. Sapeta H, Costa JM, Lourenço T et al (2013) Drought stress response in Jatropha curcas: growth and physiology. Environ Exp Bot 85:76–84CrossRefGoogle Scholar
  234. Sapeta H, Lourenço T, Lorenz S et al (2016) Transcriptomics and physiological analyses reveal co-ordinated alteration of metabolic pathways in Jatropha curcas drought tolerance. J Exp Bot 67(3):845–860PubMedCrossRefPubMedCentralGoogle Scholar
  235. Sastry NSA, Francis CR (2015) GIS based site suitability and potential assessment of Jatropha crop for biofuel production. Int J Emer Eng Res Technol 3(7):232–237Google Scholar
  236. Sato S, Hirakawa H, Isobe S et al (2011) Sequence analysis of the genome of an oil-bearing tree, Jatropha curcas L. DNA Res 18:65–76PubMedCrossRefGoogle Scholar
  237. Sato S, Hirakawa H, Tsuchimoto S et al (2013) Chapter 30: Genome structure of Jatropha curcas L. In: Bahadur B, Sujatha M, Carels N (eds) Jatropha, challenges for a new energy crop. Volume 2: Genetic improvement and biotechnology. Springer, New York, pp 563–576CrossRefGoogle Scholar
  238. Schneider G, Fechner U (2005) Computer-based de novo design of drug-like molecules. Nat Rev Drug Discov 4(8):649–663PubMedCrossRefGoogle Scholar
  239. Seaver SM, Henry CS, Hanson AD (2012) Frontiers in metabolic reconstruction and modeling of plant genomes. J Exp Bot 63(6):2247–2258PubMedCrossRefGoogle Scholar
  240. Servin B, Stephens M (2007) Imputation based analysis of association studies: candidate regions and quantitative traits. PLoS Genet 3(7):e114PubMedPubMedCentralCrossRefGoogle Scholar
  241. Shibata D, Sano R, Ara T (2017) Chapter 5: Jatropha metabolomics. In: Tsuchimoto S (ed) The Jatropha genome, compendium of plant genomes. Springer, New York, pp 83–96CrossRefGoogle Scholar
  242. Shou C, Bhardwaj N, Lam HY et al (2011) Measuring the evolutionary rewiring of biological networks. PLoS Comput Biol 7(1):e1001050PubMedPubMedCentralCrossRefGoogle Scholar
  243. Silva EN, Silveira JAG, Rodrigues CRF et al (2015) Physiological adjustment to salt stress in Jatropha curcas is associated with accumulation of salt ions, transport and selectivity of K+, osmotic adjustment and K+/Na+ homeostasis. Plant Biol 17:1023–1029PubMedCrossRefGoogle Scholar
  244. Silva-Junior O, Rosado T, Laviola B et al (2011) Genome-wide SNP discovery from a pooled sample of accessions of the biofuel plant Jatropha curcas based on whole-transcriptome Illumina resequencing. BMC Proc 5:P57PubMedCentralCrossRefPubMedGoogle Scholar
  245. Silveira JAG, Silva EN, Ferreira-Silva SL et al (2013) Chapter 7: Physiological mechanisms involved with salt and drought tolerance in Jatropha curcas plants. In: Carels N, Sujatha M, Bahadur B (eds) Jatropha, challenges for a new energy crop. Volume 1: Farming, economics and biofuel. Springer, New York, pp 125–152Google Scholar
  246. Smolke CD, Thodey C, Trenchard I et al (2014) Benzylisoquinoline alkaloids (bia) producing microbes, and methods of making and using the same. US Patent 20140273109A1Google Scholar
  247. Sorenson D, Gianola D (2002) Likelihood, Bayesian and MCMC methods in quantitative genetics. Springer, New York, p 740 ISBN:0-387-954406CrossRefGoogle Scholar
  248. Soto I, Ellison C, Kenis M et al (2018) Why do farmers abandon jatropha cultivation? The case of Chiapas, Mexico. Energy Sustain Dev 42:77–86CrossRefGoogle Scholar
  249. Sousa FL, Hordijk W, Steel M et al (2015) Autocatalytic sets in E. coli metabolism. J Syst Chem 6:4PubMedPubMedCentralCrossRefGoogle Scholar
  250. Srinivasan SP, Shanthi DS (2017) A seed yield estimation modelling using classification and regression trees (CART) in the biofuel supply chain. J Biomed Imaging Bioeng 1(1):8–12Google Scholar
  251. Stanke M, Steinkamp R, Waack S et al (2004) AUGUSTUS: a web server for gene finding in eukaryotes. Nucleic Acids Res 32(Web Server issue):W309–W312PubMedPubMedCentralCrossRefGoogle Scholar
  252. Struhl K (1984) Genetic properties and chromatin structure of the yeast gal regulatory element: an enhancer-like sequence. Proc Natl Acad Sci U S A 81:7865–7869PubMedPubMedCentralCrossRefGoogle Scholar
  253. Struhl K (1995) Yeast transcriptional regulatory mechanisms. Annu Rev Genet 29:651–674PubMedCrossRefGoogle Scholar
  254. Stuart JM, Segal E, Koller D et al (2003) A gene-coexpression network for global discovery of conserved genetic modules. Science 302(5643):249–255PubMedCrossRefGoogle Scholar
  255. Sujatha M, Bahadur B, Reddy TP (2013) Chapter 21: Interspecific hybridization in the genus Jatropha. In: Bahadur B, Sujatha M, Carels N (eds) Jatropha, challenges for a new energy crop. Volume 2: Genetic improvement and biotechnology. Springer, New York, pp 423–444CrossRefGoogle Scholar
  256. Sun Z, Albert R (2013) Chapter 10: Boolean models of cellular signaling networks. In: Walhout M, Vidal M, Dekker J (eds) Handbook of system biology concepts and insights. Elsevier, Academic, Waltham, pp 197–209CrossRefGoogle Scholar
  257. Sun QB, Li LF, Li Y et al (2008) SSR and AFLP markers reveal low genetic diversity in the biofuel plant Jatropha curcas in China. Crop Sci 48(5):1865–1871CrossRefGoogle Scholar
  258. Sun Y, Wang C, Wang N et al (2017) Manipulation of auxin response factor 19 affects seed size in the woody perennial Jatropha curcas. Sci Rep 7:40844PubMedPubMedCentralCrossRefGoogle Scholar
  259. Sunil N, Kumar V, Varaprasad KS (2013) Chapter 9: Origin, domestication, distribution and diversity of Jatropha curcas L. In: Bahadur B, Sujatha M, Carels N (eds) Jatropha, challenges for a new energy crop. Volume 2: Genetic improvement and biotechnology. Springer, New York, pp 137–152CrossRefGoogle Scholar
  260. Sutthivaiyakit S, Mongkolvisut W, Prabpai S et al (2009) Diterpenes, sesquiterpenes, and a sesquiterpene−coumarin conjugate from Jatropha integerrima. J Nat Prod 72:2024–2027PubMedCrossRefGoogle Scholar
  261. Tarassov K, Messier V, Landry CR et al (2008) An in vivo map of the yeast protein interactome. Science 320:1465–1470PubMedCrossRefGoogle Scholar
  262. Taylor RD, Jewsbury PJ, Essex JW (2002) A review of protein-small molecule docking methods. J Comput Aided Mol Des 16(3):151–166PubMedCrossRefGoogle Scholar
  263. Teichmann SA (2002) The constraints protein-protein interactions place on sequence divergence. J Mol Biol 324(3):399–407PubMedCrossRefGoogle Scholar
  264. The Arabidopsis Genome Initiative (2000) Analysis of the genome sequence of the flowering plant Arabidopsis thaliana. Nature 408:796–815CrossRefGoogle Scholar
  265. Thiele I, Palsson B (2010) A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat Protoc 5:93–121PubMedPubMedCentralCrossRefGoogle Scholar
  266. Thomas R (1973) Boolean formalization of genetic control circuits. J Theor Biol 42(3):563–585PubMedCrossRefGoogle Scholar
  267. Thomas R, Sah NK, Sharma PB (2008) Therapeutic biology of Jatropha curcas: a mini review. Curr Pharm Biotechnol 9:315–324PubMedCrossRefGoogle Scholar
  268. Tilli TM, Carels N, Tuszynski JA et al (2016) Validation of a network-based strategy for the optimization of combinatorial target selection in breast cancer therapy: siRNA knockdown of network targets in MDA-MB-231 cells as an in vitro model for inhibition of tumor development. Oncotarget 7(39):63189–63203PubMedPubMedCentralCrossRefGoogle Scholar
  269. Tjeuw J, Mulia R, Slingerland M et al (2015) Tree or shrub: a functional branch analysis of Jatropha curcas L. Agrofor Syst 89:841–856CrossRefGoogle Scholar
  270. Tong AH, Evangelista M, Parsons AB et al (2001) Systematic genetic analysis with ordered arrays of yeast deletion mutants. Science 294:2364–2368PubMedCrossRefGoogle Scholar
  271. Toosi A, Fernando RL, Dekkers JCM (2010) Genomic selection in admixed and crossbred populations. J Anim Sci 88:32–46PubMedCrossRefGoogle Scholar
  272. Tornow S, Mewes HW (2003) Functional modules by relating protein interaction networks and gene expression. Nucleic Acids Res 31(21):6283–6289PubMedPubMedCentralCrossRefGoogle Scholar
  273. Trabucco A, Achten WMJ, Bowe C et al (2010) Global mapping of Jatropha curcas yield based on response of fitness to present and future climate. Glob Chang Biol Bioenerg 2:139–151Google Scholar
  274. Tsuchimoto S, Cartagena J, Khemkladngoen N et al (2012) Development of transgenic plants in jatropha with drought tolerance. Plant Biotechnol 29:137–143CrossRefGoogle Scholar
  275. Tuch BB, Li H, Johnson AD (2008) Evolution of eukaryotic transcription circuits. Science 319:1797PubMedCrossRefGoogle Scholar
  276. Vaknin Y, Yermiyahu U, Bar-Tal A et al (2018) Global maximization of Jatropha oil production under semi-arid conditions by balancing vegetative growth with reproductive capacity. GCB Bioenergy 10:382–392. CrossRefGoogle Scholar
  277. Valdés-Rodríguez OA, Odilón Sánchez-Sánchez O, Pérez-Vázquez A et al (2013) Jatropha curcas L. root structure and growth in diverse soils. Sci World J 2013:827295. CrossRefGoogle Scholar
  278. Valladares F, Martinez-Ferri E, Balaguer L et al (2000) Low leaf-level response to light and nutrients in Mediterranean evergreen oaks: a conservative resource-use strategy? New Phytol 148:79–91CrossRefGoogle Scholar
  279. Vásquez-Mayorga M, Fuchs EJ, Hernández EJ et al (2017) Molecular characterization and genetic diversity of Jatropha curcas L. in Costa Rica. Peer J 5:e2931. CrossRefPubMedGoogle Scholar
  280. Vazquez A (2017) Overflow metabolism: from yeast to marathon runners. Elsevier Science, LondonGoogle Scholar
  281. Verdonk ML, Cole JC, Hartshorn MJ et al (2003) Improved protein–ligand docking using GOLD. Protein: Struct Funct Bioinform 52(4):609–623CrossRefGoogle Scholar
  282. Vermeirssen V, Deplancke B, Barrasa MI et al (2007) Matrix and Steiner-triple-system smart pooling assays for high-performance transcription regulatory network mapping. Nat Methods 4:659–664PubMedCrossRefPubMedCentralGoogle Scholar
  283. Vishwakarma NP, Jadeja VJ (2013) Identification of miRNA encoded by Jatropha curcas from EST and GSS. Plant Signal Behav 8:e23152PubMedPubMedCentralCrossRefGoogle Scholar
  284. Vyas VK, Ukawala RD, Ghate M et al (2012) Homology modeling a fast tool for drug discovery: current perspectives. Indian J Pharm Sci 74(1):1–17PubMedPubMedCentralCrossRefGoogle Scholar
  285. Wagner A (2013) Chapter 13: Genotype networks and evolutionary innovations in biological systems. In: Walhout M, Vidal M, Dekker J (eds) Handbook of system biology concepts and insights. Elsevier, Academic, Waltham, pp 251–264CrossRefGoogle Scholar
  286. Wang PI, Marcotte EM (2010) It’s the machine that matters: predicting gene function and phenotype from protein networks. J Proteome 73(11):2277–2289CrossRefGoogle Scholar
  287. Wang R, Gao Y, Lai L (2000) LigBuilder: a multi-purpose program for structure-based drug design. Mol Model Ann 6(7):498–516CrossRefGoogle Scholar
  288. Wang CM, Liu P, Yi C et al (2011) A first generation microsatellite- and SNP-based linkage map of Jatropha. PLoS One 6:e23632PubMedPubMedCentralCrossRefGoogle Scholar
  289. Wang CM, Liu P, Sun F et al (2012) Isolation and identification of miRNAs in Jatropha curcas. Int J Biol Sci 8:418–429PubMedPubMedCentralCrossRefGoogle Scholar
  290. Wang L, Dash S, Ng CY et al (2017) A review of computational tools for design and reconstruction of metabolic pathways. Synth Syst Biotechnol 2:243–252PubMedPubMedCentralCrossRefGoogle Scholar
  291. Warra AA (2012) Cosmetic potentials of physic nut (Jatropha curcas Linn.) seed oil: a review. Am J Sci Ind Res 3(6):358–366Google Scholar
  292. Weber T, Kim HU (2016) The secondary metabolite bioinformatics portal: computational tools to facilitate synthetic biology of secondary metabolite production. Synth Syst Biotechnol 1(2):69–79PubMedPubMedCentralCrossRefGoogle Scholar
  293. Wilkinson B, Micklefield J (2007) Mining and engineering natural-product biosynthetic pathways. Nat Chem Biol 3:379–386PubMedCrossRefGoogle Scholar
  294. Williamson JR (2008) Cooperativity in macromolecular assembly. Nat Chem Biol 4(8):458–465PubMedCrossRefGoogle Scholar
  295. Winter K, Holtum JAM (2015) Cryptic crassulacean acid metabolism (CAM) in Jatropha curcas. Funct Plant Biol. CSIRO. 42:711–717. CrossRefGoogle Scholar
  296. Winzer T, Gazda V, He Z et al (2012) A Papaver somniferum 10-gene cluster for synthesis of the anticancer alkaloid noscapine. Science 336:1704–1708PubMedCrossRefGoogle Scholar
  297. Wray GA, Hahn MW, Abouheif E et al (2003) The evolution of transcriptional regulation in eukaryotes. Mol Biol Evol 20(9):1377–1419PubMedCrossRefGoogle Scholar
  298. Wu P, Zhou C, Cheng S et al (2015) Integrated genome sequence and linkage map of physic nut (Jatropha curcas L.), a biodiesel plant. Plant J 81:810–821PubMedCrossRefGoogle Scholar
  299. Xia Z, Zhang S, Wen M et al (2018) Construction of an ultrahigh density genetic linkage map for Jatropha curcas L. and identification of QTL for fruit yield. Biotechnol Biofuels 11:3PubMedPubMedCentralCrossRefGoogle Scholar
  300. Xiang Z (2006) Advances in homology protein structure modeling. Curr Protein Pept Sci 7(3):217–227PubMedPubMedCentralCrossRefGoogle Scholar
  301. Yang CY, Deng X, Fang Z et al (2010) Selection of high-oil-yield seed sources of Jatropha curcas L. for biodiesel production. Biofuels 1:705–717CrossRefGoogle Scholar
  302. Yang J, Lee SH, Goddard ME et al (2013a) Chapter 9: Genome-wide complex trait analysis (GCTA): methods, data analyses, and interpretations. In: Gondro C, van der Werf J, Hayes B (eds) Genome-wide association studies and genomic prediction. Humana Press, Springer, Totowa, pp 215–236Google Scholar
  303. Yang YF, Liu JQ, Li XY et al (2013b) New terpenoids from the roots of Jatropha curcas. Chin Sci Bull 58:1115–1119CrossRefGoogle Scholar
  304. Yazaki J, Galli M, Kim AY et al (2016) Mapping transcription factor interactome networks using HaloTag protein arrays. Proc Natl Acad Sci U S A 113(29):E4238–E4247PubMedPubMedCentralCrossRefGoogle Scholar
  305. Ye J, Hong Y, Qu J et al (2013) Chapter 29: Improvement of J. curcas oil by genetic transformation. In: Bahadur B, Sujatha M, Carels N (eds) Jatropha, challenges for a new energy crop. Volume 2: Genetic improvement and biotechnology. Springer, New York, pp 547–562CrossRefGoogle Scholar
  306. Ye J, Liu P, Zhu CS et al (2014) Identification of candidate genes JcARF19 and JcIAA9 associated with seed size traits in Jatropha. Funct Integr Genomics 14(4):757–766PubMedCrossRefPubMedCentralGoogle Scholar
  307. Ye J, Wang C, Yue G (2017) Chapter 2: Linkage mapping and QTL analysis. In: Tsuchimoto S (ed) The Jatropha genome, compendium of plant genomes. Springer, New York, pp 21–44CrossRefGoogle Scholar
  308. Yi C, Reddy C, Varghese K et al (2014) A new Jatropha curcas variety (JO S2) with improved seed productivity. Sustainability 6:4355–4368. CrossRefGoogle Scholar
  309. Yilmaz LS, Walhout AJM (2017) Metabolic network modeling with model organisms. Curr Opin Chem Biol 36:32–39PubMedPubMedCentralCrossRefGoogle Scholar
  310. Yu H, Braun P, Yildirim MA et al (2008) High-quality binary protein interaction map of the yeast interactome network. Science 322(5898):104–110PubMedPubMedCentralCrossRefGoogle Scholar
  311. Yue GH, Sun F, Liu P (2013) Status of molecular breeding for improving Jatropha curcas and biodiesel. Renew Sust Energ Rev 26:332–343CrossRefGoogle Scholar
  312. Zaragoza-Martínez F, Lucho-Constantino GG, Ponce-Noyola T et al (2016) Jasmonic acid stimulates the oxidative responses and triterpene production in Jatropha curcas cell suspension cultures through mevalonate as biosynthetic precursor. Plant Cell Tissue Organ Cult 127:47–56CrossRefGoogle Scholar
  313. Zhang C, Zhang L, Zhang S et al (2015a) Global analysis of gene expression profiles in physic nut (Jatropha curcas L.) seedlings exposed to drought stress. BMC Plant Biol 15:17PubMedPubMedCentralCrossRefGoogle Scholar
  314. Zhang J, Zhao J, Xu Y et al (2015b) Genome-wide association mapping for tomato volatiles positively contributing to tomato flavor. Front Plant Sci 6:1042PubMedPubMedCentralGoogle Scholar
  315. Zhang A, Wang H, Beyene Y et al (2017) Effect of trait heritability, training population size and marker density on genomic prediction accuracy estimation in 22 bi-parental tropical maize populations. Front Plant Sci 8:1916PubMedPubMedCentralCrossRefGoogle Scholar
  316. Zhao N, Wang G, Norris A et al (2013) Studying plant secondary metabolism in the age of genomics. Crit Rev Plant Sci 32(6):369–382CrossRefGoogle Scholar
  317. Zhong S (2008) Integrating QTL analysis into plant breeding practice using Bayesian statistics. Retrospective thesis and dissertations 15868.
  318. Zhu Q, Ge D, Maia JM et al (2011) A genome-wide comparison of the functional properties of rare and common genetic variants in humans. Am J Hum Genet 88(4):458–468PubMedPubMedCentralCrossRefGoogle Scholar
  319. Zhuang X, Chappell J (2015) Building terpene production platforms in yeast. Biotechnol Bioeng 112:1854–1864PubMedCrossRefGoogle Scholar
  320. Zinman GE, Zhong S, Bar-Joseph Z (2011) Biological interaction networks are conserved at the module level. BMC Syst Biol 5:134PubMedPubMedCentralCrossRefGoogle Scholar
  321. Zulak KG, Cornish A, Daskalchuk TE et al (2005) Gene transcript and metabolite profiling of elicitor-induced opium poppy cell cultures reveals the coordinate regulation of primary and secondary metabolism. Planta 225:1085–1106CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Nicolas Carels
    • 1
    • 2
    Email author
  • Milena Magalhães
    • 1
    • 2
  • Carlyle Ribeiro Lima
    • 1
    • 2
  • Bir Bahadur
    • 3
  • Marcio Argollo de Menezes
    • 4
    • 5
  1. 1.Laboratório de Modelagem de Sistemas Biológicos, Centro de Desenvolvimento Tecnológico em SaúdeFundação Oswaldo CruzRio de JaneiroBrazil
  2. 2.Instituto Nacional de Ciência e Tecnologia de Inovação em Doenças de Populações Negligenciadas, INCT-DPNRio de JaneiroBrazil
  3. 3.Department of BotanyKakatiya UniversityWarangalIndia
  4. 4.Instituto de Física, Universidade Federal FluminenseRio de JaneiroBrazil
  5. 5.Instituto Nacional de Ciência e Tecnologia de Sistemas Complexos, INCT-SCRio de JaneiroBrazil

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