Computational systems biology of cellular processes in Arabidopsis thaliana: an overview

  • Pascal Holzheu
  • Ursula KummerEmail author


Systems biology strives for gaining an understanding of biological phenomena by studying the interactions of different parts of a system and integrating the knowledge obtained into the current view of the underlying processes. This is achieved by a tight combination of quantitative experimentation and computational modeling. While there is already a large quantity of systems biology studies describing human, animal and especially microbial cell biological systems, plant biology has been lagging behind for many years. However, in the case of the model plant Arabidopsis thaliana, the steadily increasing amount of information on the levels of its genome, proteome and on a variety of its metabolic and signalling pathways is progressively enabling more researchers to construct models for cellular processes for the plant, which in turn encourages more experimental data to be generated, showing also for plant sciences how fruitful systems biology research can be. In this review, we provide an overview over some of these recent studies which use different systems biological approaches to get a better understanding of the cell biology of A. thaliana. The approaches used in these are genome-scale metabolic modeling, as well as kinetic modeling of metabolic and signalling pathways. Furthermore, we selected several cases to exemplify how the modeling approaches have led to significant advances or new perspectives in the field.


Arabidopsis thaliana Systems biology Genome scale models Kinetic modeling Metabolism Signalling 



This work was financed by the DFG through the SFB 1101 and was supported in part by the Heidelberg Graduate School of Mathematical and Computational Methods for the Sciences (HGS MathComp), founded by DFG Grant GSC 220 in the German Universities Excellence Initiative.

Compliances with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. 1.
    Kitano H (2002) Systems biology: a brief overview. Science 295(5560):1662–1664PubMedCrossRefGoogle Scholar
  2. 2.
    Bruggeman FJ, Westerhoff HV (2007) The nature of systems biology. Trends Microbiol 15(1):45–50PubMedCrossRefGoogle Scholar
  3. 3.
    Heazlewood JL (2011) The green proteome: challenges in plant proteomics. Front Plant Sci 2:6PubMedPubMedCentralCrossRefGoogle Scholar
  4. 4.
    Van Norman JM, Benfey PN (2009) Arabidopsis thaliana as a model organism in systems biology. Wiley Interdiscip Rev Syst Biol Med 1(3):372–379PubMedPubMedCentralCrossRefGoogle Scholar
  5. 5.
    Tsesmetzis N, Couchman M, Higgins J, Smith A, Doonan JH, Seifert GJ, Schmidt EE, Vastrik I, Birney E, Wu G et al (2008) Arabidopsis reactome: a foundation knowledgebase for plant systems biology. Plant Cell 20(6):1426–1436PubMedPubMedCentralCrossRefGoogle Scholar
  6. 6.
    Laibach F (1943) Arabidopsis thaliana (l.) heynh. als objekt für genetische und entwicklungsphysiologische untersuchungen. Bot Arch 44:439–455Google Scholar
  7. 7.
    Reinholz, E (1945) Auslösung von Röntgen-Mutationen bei Arabidopsis thaliana L. Heynh. und ihre Bedeutung für die Pflanzenzüchtung und Evolutionstheorie: Nebst Zusammenfassg. PhD thesis, Verlag nicht ermittelbarGoogle Scholar
  8. 8.
    Rédei GP (1975) Arabidopsis as a genetic tool. Annu Rev Genet 9(1):111–127PubMedCrossRefGoogle Scholar
  9. 9.
    Steinitz-Sears LM (1963) Chromosome studies in Arabidopsis thaliana. Genetics 48(4):483PubMedPubMedCentralGoogle Scholar
  10. 10.
    Pruitt RE, Meyerowitz EM (1986) Characterization of the genome of Arabidopsis thaliana. J Mol Biol 187(2):169–183PubMedCrossRefGoogle Scholar
  11. 11.
    Initiative AG et al (2000) Analysis of the genome sequence of the flowering plant Arabidopsis thaliana. Nature 408(6814):796CrossRefGoogle Scholar
  12. 12.
    Collins FS, Morgan M, Patrinos A (2003) The human genome project: lessons from large-scale biology. Science 300(5617):286–290PubMedCrossRefGoogle Scholar
  13. 13.
    Hübner K, Sahle S, Kummer U (2011) Applications and trends in systems biology in biochemistry. FEBS J 278(16):2767–2857PubMedCrossRefGoogle Scholar
  14. 14.
    Ashyraliyev M, Fomekong-Nanfack Y, Kaandorp JA, Blom JG (2009) Systems biology: parameter estimation for biochemical models. FEBS J 276(4):886–902PubMedCrossRefGoogle Scholar
  15. 15.
    Angeli D, Ferrell JE, Sontag ED (2004) Detection of multistability, bifurcations, and hysteresis in a large class of biological positive-feedback systems. Proc Natl Acad Sci 101(7):1822–1827PubMedCrossRefGoogle Scholar
  16. 16.
    Fell DA (1992) Metabolic control analysis: a survey of its theoretical and experimental development. Biochem J 286(Pt 2):313PubMedPubMedCentralCrossRefGoogle Scholar
  17. 17.
    Jablonsky J, Bauwe H, Wolkenhauer O (2011) Modeling the calvin-benson cycle. BMC Syst Biol 5(1):185PubMedPubMedCentralCrossRefGoogle Scholar
  18. 18.
    Rios-Estepa R, Lange BM (2007) Experimental and mathematical approaches to modeling plant metabolic networks. Phytochemistry 68(16–18):2351–2374PubMedCrossRefGoogle Scholar
  19. 19.
    Matuszyńska A, Heidari S, Jahns P, Ebenhoeh O (2016) A mathematical model of non-photochemical quenching to study short-term light memory in plants. Biochim Biophys Acta (BBA) Bioenerget 1857(12):1860–1869CrossRefGoogle Scholar
  20. 20.
    Henkel S, Nägele T, Hörmiller I, Sauter T, Sawodny O, Ederer M, Heyer AG (2011) A systems biology approach to analyse leaf carbohydrate metabolism in Arabidopsis thaliana. EURASIP J Bioinf Syst Biol 2011(1):2CrossRefGoogle Scholar
  21. 21.
    Giovanelli J, Mudd SH, Datko AH (1980) Sulfur amino acids in plants. In: Miflin BJ (ed) Amino acids and derivatives. Academic Press, New York, pp 453–505CrossRefGoogle Scholar
  22. 22.
    Eaton SV (1951) Effects of sulfur deficiency on growth and metabolism of tomato. Bot Gaz 112(3):300–307CrossRefGoogle Scholar
  23. 23.
    Feldman-Salit A, Veith N, Wirtz M, Hell R, Kummer U (2019) Distribution of control in the sulfur assimilation in Arabidopsis thaliana depends on environmental conditions. New PhytolGoogle Scholar
  24. 24.
    Kobayashi T, Nozoye T, Nishizawa NK (2019) Iron transport and its regulation in plants. Free Radical Biol Med 133:11–20CrossRefGoogle Scholar
  25. 25.
    Koryachko A, Matthiadis A, Haque S, Muhammad D, Ducoste JJ, Tuck JM, Long TA, Williams CM (2019) Dynamic modelling of the iron deficiency modulated transcriptome response in Arabidopsis thaliana roots. In: Silico Plants, 1(1):diz005Google Scholar
  26. 26.
    Nägele T, Heyer AG (2013) Approximating subcellular organisation of carbohydrate metabolism during cold acclimation in different natural accessions of Arabidopsis thaliana. New Phytol 198(3):777–787PubMedCrossRefGoogle Scholar
  27. 27.
    Hartmann T (2004) Plant-derived secondary metabolites as defensive chemicals in herbivorous insects: a case study in chemical ecology. Planta 219(1):1–4PubMedCrossRefGoogle Scholar
  28. 28.
    Wink M (1988) Plant breeding: importance of plant secondary metabolites for protection against pathogens and herbivores. Theor Appl Genet 75(2):225–233CrossRefGoogle Scholar
  29. 29.
    Verpoorte R (1998) Exploration of nature’s chemodiversity: the role of secondary metabolites as leads in drug development. Drug Discov Today 3(5):232–238CrossRefGoogle Scholar
  30. 30.
    Knoke B, Textor S, Gershenzon J, Schuster S (2009) Mathematical modelling of aliphatic glucosinolate chain length distribution in Arabidopsis thaliana leaves. Phytochem Rev 8(1):39CrossRefGoogle Scholar
  31. 31.
    Olsen KM, Slimestad R, Lea US, Brede C, LØVDAL TROND, Ruoff P, Verheul M, Lillo C (2009) Temperature and nitrogen effects on regulators and products of the flavonoid pathway: experimental and kinetic model studies. Plant Cell Environ 32(3):286–299PubMedCrossRefGoogle Scholar
  32. 32.
    Santner A, Estelle M (2009) Recent advances and emerging trends in plant hormone signalling. Nature 459(7250):1071PubMedCrossRefGoogle Scholar
  33. 33.
    Aldridge BB, Burke JM, Lauffenburger DA, Sorger PK (2006) Physicochemical modelling of cell signalling pathways. Nat Cell Biol 8(11):1195PubMedCrossRefGoogle Scholar
  34. 34.
    Aloni R, Aloni E, Langhans M, Ullrich CI (2006) Role of cytokinin and auxin in shaping root architecture: regulating vascular differentiation, lateral root initiation, root apical dominance and root gravitropism. Ann Bot 97(5):883–893PubMedPubMedCentralCrossRefGoogle Scholar
  35. 35.
    Moubayidin L, Di Mambro R, Sabatini S (2009) Cytokinin-auxin crosstalk. Trends Plant Sci 14(10):557–562PubMedCrossRefGoogle Scholar
  36. 36.
    Muraro D, Byrne H, King J, Voß U, Kieber J, Bennett M (2011) The influence of cytokinin-auxin cross-regulation on cell-fate determination in Arabidopsis thaliana root development. J Theor Biol 283(1):152–167PubMedCrossRefGoogle Scholar
  37. 37.
    Vernoux T, Brunoud G, Farcot E, Morin V, Van den Daele H, Legrand J, Oliva M, Das P, Larrieu A, Wells D et al (2011) The auxin signalling network translates dynamic input into robust patterning at the shoot apex. Mol Syst Biol 7(1):508PubMedPubMedCentralCrossRefGoogle Scholar
  38. 38.
    Pokhilko A, Mas P, Millar AJ (2013) Modelling the widespread effects of toc1 signalling on the plant circadian clock and its outputs. BMC Syst Biol 7(1):23PubMedPubMedCentralCrossRefGoogle Scholar
  39. 39.
    Yilmaz LS, Walhout AJM (2017) Metabolic network modeling with model organisms. Curr Opin Chem Biol 36:32–39PubMedPubMedCentralCrossRefGoogle Scholar
  40. 40.
    Benedict MN, Mundy MB, Henry CS, Chia N, Price ND (2014) Likelihood-based gene annotations for gap filling and quality assessment in genome-scale metabolic models. PLoS Comput Biol 10(10):e1003882PubMedPubMedCentralCrossRefGoogle Scholar
  41. 41.
    Orth JD, Thiele I, Palsson BØ (2010) What is flux balance analysis? Nat Biotechnol 28(3):245PubMedPubMedCentralCrossRefGoogle Scholar
  42. 42.
    Poolman MG, Miguet L, Sweetlove LJ, Fell DA (2009) A genome-scale metabolic model of Arabidopsis and some of its properties. Plant Physiol 151(3):1570–1581PubMedPubMedCentralCrossRefGoogle Scholar
  43. 43.
    de Oliveira DCG, Quek Lake-Ee P, Robin William B, Stevens M, Nielsen LK (2010) Aragem, a genome-scale reconstruction of the primary metabolic network in Arabidopsis. Plant Physiol 152(2):579–589CrossRefGoogle Scholar
  44. 44.
    Williams TCR, Poolman MG, Howden AJM, Schwarzlander M, Fell DA, Ratcliffe RG, Sweetlove LJ (2010) A genome-scale metabolic model accurately predicts fluxes in central carbon metabolism under stress conditions. Plant Physiol 154(1):311–323PubMedPubMedCentralCrossRefGoogle Scholar
  45. 45.
    Cheung CYM, Poolman MG, Fell DA, Ratcliffe RG, Sweetlove LJ (2014) A diel flux balance model captures interactions between light and dark metabolism during day-night cycles in c3 and crassulacean acid metabolism leaves. Plant Physiol 165(2):917–929PubMedPubMedCentralCrossRefGoogle Scholar
  46. 46.
    Shaw R, Cheung CY (2018) A dynamic multi-tissue flux balance model captures carbon and nitrogen metabolism and optimal resource partitioning during arabidopsis growth. Front Plant Sci 9:884PubMedPubMedCentralCrossRefGoogle Scholar
  47. 47.
    Gianchandani EP, Chavali AK, Papin JA (2010) The application of flux balance analysis in systems biology. Wiley Interdiscip Rev Syst Biol Med 2(3):372–382PubMedCrossRefGoogle Scholar
  48. 48.
    Mintz-Oron S, Meir S, Malitsky S, Ruppin E, Aharoni A, Shlomi T (2012) Reconstruction of arabidopsis metabolic network models accounting for subcellular compartmentalization and tissue-specificity. Proc Natl Acad Sci 109(1):339–344PubMedCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.INF 267 (Bioquant)Heidelberg UniversityHeidelbergGermany

Personalised recommendations