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Identification of Sensitive Enzymes in the Photosynthetic Carbon Metabolism

  • Renato Umeton
  • Giovanni Stracquadanio
  • Alessio Papini
  • Jole Costanza
  • Pietro Liò
  • Giuseppe Nicosia
Conference paper
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 736)

Abstract

Understanding and optimizing the CO2 fixation process would allow human beings to address better current energy and biotechnology issues. We focused on modeling the C3 photosynthetic Carbon metabolism pathway with the aim of identifying the minimal set of enzymes whose biotechnological alteration could allow a functional re-engineering of the pathway. To achieve this result we merged in a single powerful pipe-line Sensitivity Analysis (SA), Single- (SO) and Multi-Objective Optimization (MO), and Robustness Analysis (RA). By using our recently developed multipurpose optimization algorithms (PAO and PMO2) here we extend our work exploring a large combinatorial solution space and most importantly, here we present an important reduction of the problem search space. From the initial number of 23 enzymes we have identified 11 enzymes whose targeting in the C3 photosynthetic Carbon metabolism would provide about 90% of the overall functional optimization. Both in terms of maximal CO2 Uptake and minimal Nitrogen consumption, these 11 sensitive enzymes are confirmed to play a key role. Finally we present a RA to confirm our findings.

Keywords

Calvin Cycle Pareto Frontier Robustness Analysis Chloroplast Stroma Sensitive Enzyme 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Zhu XG, de Sturler E, Long SP (2007) Optimizing the distribution of resources between enzymes of carbon metabolism can dramatically increase photosynthetic rate: A numerical simulation using an evolutionary algorithm. Plant Physiol 145:513–526PubMedCrossRefGoogle Scholar
  2. 2.
    Stracquadanio G, Umeton R, Papini A, Liò P, Nicosia, G (2010) Analysis and optimization of C3 photosynthetic carbon metabolism. In: Rigoutsos I, Floudas CA (eds) Proc BIBE 2010, 10th IEEE Int Conf Bioinformatics and Bioengineering, May 31–June 3, 2010, Philadelphia, PA, USA, IEEE Computer Society, pp 44–51CrossRefGoogle Scholar
  3. 3.
    Papini A, Nicosia G, Stracquadanio G, Lio P, Umeton R (2010) Key Enzymes for the optimization of CO2 uptake and nitrogen consumption in the C3 photosynthetic carbon metabolism. J Biotechnol 150:525–526CrossRefGoogle Scholar
  4. 4.
    Farquhar G, Caemmerer S, Berry J (1980) A biochemical model of photosynthetic CO 2 assimilation in leaves of C3 species. Planta 149(1):78–90CrossRefGoogle Scholar
  5. 5.
    Wullschleger S (1993) Biochemical limitations to carbon assimilation in C3 plants: a retrospective analysis. J Exp Bot 44:907–920CrossRefGoogle Scholar
  6. 6.
    Wingler A, Lea P, Quick W, Leegood R (2000) Photorespiration: metabolic pathways and their role in stress protection. Philos Trans Royal Soc London. Ser B: Biol Sci 355(1402):1517Google Scholar
  7. 7.
    Heber U, Bligny R, Streb P, Douce R (1996) Photorespiration is essential for the protection of the photosynthetic apparatus of C3 plants against photoinactivation under sunlight. Bot Acta 109:307–315Google Scholar
  8. 8.
    Morris M (1991) Factorial sampling plans for preliminary computational experiments. Technometrics 33(2):161–174CrossRefGoogle Scholar
  9. 9.
    Saltelli A, Tarantola S, Campolongo F (2004) Sensitivity analysis in practice: a guide to assessing scientific models. John Wiley & Sons Inc.Google Scholar
  10. 10.
    Rosvall M, Bergstrom C (2007) An information-theoretic framework for resolving community structure in complex networks. Proc Natl Acad Sci 104(18):7327PubMedCrossRefGoogle Scholar
  11. 11.
    Storn R, Price K (1997) Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11(4):341–359CrossRefGoogle Scholar
  12. 12.
    Umeton R, Stracquadanio G, Sorathiya A, Papini A, Liò P, Nicosia G (2011) Design of robust metabolic pathways. In: Proc 48th design automation conference, DAC 2011, San Diego, CA, USA, June 5–9, 2011, ACM, pp 747–752Google Scholar
  13. 13.
    Stracquadanio G, Nicosia G (2011) Computational energy-based redesign of robust proteins. Comput Chem Eng 35(3):464–473CrossRefGoogle Scholar
  14. 14.
    Hooke R, Jeeves TA (1961) “Direct search” solution of numerical and statistical problems. J ACM 8(2):212–229CrossRefGoogle Scholar
  15. 15.
    Huyer W, Neumaier A (1999) Global optimization by multilevel coordinate search. J Global Optim 14(4):331–355CrossRefGoogle Scholar
  16. 16.
    Vaz A, Vicente L (2007) A particle swarm pattern search method for bound constrained global optimization. J Global Optim 39(2):197–219CrossRefGoogle Scholar
  17. 17.
    Audet C, Dennis JE (2007) Mesh adaptive direct search algorithms for constrained optimization. SIAM J Optim 17(1):188–217CrossRefGoogle Scholar
  18. 18.
    Hansen N, Ostermeier A (2001) Completely derandomized self-adaptation in evolution strategies. Evol Comput 9(2):159–195PubMedCrossRefGoogle Scholar
  19. 19.
    Jones DR, Perttunen CD, Stuckman BE (1993) Lipschitzian optimization without the Lipschitz constant. J Optim Theor Appl 79(1):157–181CrossRefGoogle Scholar
  20. 20.
    Lewis R, Torczon V (1999) Pattern search algorithms for bound constrained minimization. SIAM J Optim 9(4):1082–1099CrossRefGoogle Scholar
  21. 21.
    Gilmore P, Kelley CT (1995) An implicit filtering algorithm for optimization of functions with many local minima. SIAM J Optim 5(2):269–285CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Renato Umeton
    • 1
  • Giovanni Stracquadanio
    • 2
  • Alessio Papini
    • 3
  • Jole Costanza
    • 4
  • Pietro Liò
    • 5
  • Giuseppe Nicosia
    • 4
  1. 1.Massachusetts Institute of TechnologyCambridgeUSA
  2. 2.The Johns Hopkins UniversityBaltimoreUSA
  3. 3.University of FlorenceFirenzeItaly
  4. 4.University of CataniaCataniaItaly
  5. 5.University of CambridgeCambridgeUK

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