Advertisement

Metabolic Circuit Design Automation by Multi-objective BioCAD

  • Andrea Patané
  • Piero ConcaEmail author
  • Giovanni Carapezza
  • Andrea Santoro
  • Jole Costanza
  • Giuseppe Nicosia
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10122)

Abstract

We present a thorough in silico analysis and optimization of the genome-scale metabolic model of the mycolic acid pathway in M. tuberculosis. We apply and further extend meGDMO to account for finer sensitivity analysis and post-processing analysis, thanks to the combination of statistical evaluation of strains robustness, and clustering analysis to map the phenotype-genotype relationship among Pareto optimal strains. In the first analysis scenario, we find 12 Pareto-optimal single gene set knockout, which completely shut down the pathway, hence critically reducing the pathogenicity of M. tuberculosis; as well as 34 genotypically different strains in which the production of mycolic acid is severely reduced.

Keywords

Metabolic pathways Mycolic acid maximization M. tuberculosis Global sensitivity analysis Robustness analysis Clustering analysis Optimization 

References

  1. 1.
    Hasdemir, D., Hoefsloot, H.C.J., Smilde, A.K.: Validation and selection of ODE based systems biology models: how to arrive at more reliable decisions. BMC Syst. Biol. 9, 1–9 (2015)CrossRefGoogle Scholar
  2. 2.
    Palsson, B.: Systems Biology. Cambridge University Press, Cambridge (2015)CrossRefzbMATHGoogle Scholar
  3. 3.
    Kauffman, K.J., Prakash, P., Edwards, J.S.: Advances in flux balance analysis. Curr. Opin. Biotechnol. 14(5), 491–496 (2003)CrossRefGoogle Scholar
  4. 4.
    Yim, H., Haselbeck, R., Niu, W., Pujol-Baxley, C., Burgard, A., Boldt, J., Khandurina, J., et al.: Metabolic engineering of Escherichia coli for direct production of 1, 4-butanediol. Nat. Chem. Biol. 7(7), 445–452 (2011)CrossRefGoogle Scholar
  5. 5.
    Rockwell, G., Guido, N.J., Church, G.M.: Redirector: designing cell factories by reconstructing the metabolic objective. PLoS Comput. Biol. 9, 1 (2013)CrossRefGoogle Scholar
  6. 6.
    Figueredo, G.P., Siebers, P., Owen, M.R., Reps, J., Aickelin, U.: Comparing stochastic differential equations and agent-based modelling and simulation for early-stage cancer. PloS One 9(4), e95150 (2014)CrossRefGoogle Scholar
  7. 7.
    Hamilton, J.J., Reed, J.L.: Software platforms to facilitate reconstructing genome-scale metabolic networks. Environ. Microbiol. 16(1), 49–59 (2014)CrossRefGoogle Scholar
  8. 8.
    Costanza, J., Carapezza, G., Angione, C., Lió, P., Nicosia, G.: Robust design of microbial strains. Bioinformatics 28(23), 3097–3104 (2012)CrossRefzbMATHGoogle Scholar
  9. 9.
    Patane, A., Santoro, A., Costanza, J., Carapezza, G., Nicosia, G.: Pareto optimal design for synthetic biology. IEEE Trans. Biomed. Circ. Syst. 9(4), 555–571 (2015)CrossRefGoogle Scholar
  10. 10.
    Deb, K.: Multi-objective Optimization Using Evolutionary Algorithms. Wiley, Hoboken (2001)zbMATHGoogle Scholar
  11. 11.
    Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., Tarantola, S.: Global Sensitivity Analysis: The Primer. Wiley, Hoboken (2008)zbMATHGoogle Scholar
  12. 12.
    Morris, M.D.: Factorial sampling plans for preliminary computational experiments. Technometrics 33(2), 161–174 (1991)CrossRefGoogle Scholar
  13. 13.
    Angione, C., Carapezza, G., Costanza, J., Lió, P., Nicosia, G.: Computing with metabolic machines. Turing-100 10, 1–15 (2012)zbMATHGoogle Scholar
  14. 14.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. Turing-100 10, 1–15 (2012)Google Scholar
  15. 15.
    Cutello, V., Narzisi, G., Nicosia, G., Pavone, M.: Clonal selection algorithms: a comparative case study using effective mutation potentials. In: Jacob, C., Pilat, M.L., Bentley, P.J., Timmis, J.I. (eds.) ICARIS 2005. LNCS, vol. 3627, pp. 13–28. Springer, Heidelberg (2005). doi: 10.1007/11536444_2 CrossRefGoogle Scholar
  16. 16.
    Angione, C., Costanza, J., Carapezza, G., Lió, P., Nicosia, G.: Pareto epsilon-dominance and identifiable solutions for BioCAD modelling. In: Proceedings of the 50th Annual Design Automation Conference, pp. 43–51 (2013)Google Scholar
  17. 17.
    Carapezza, G., Umeton, R., Costanza, J., Angione, C., Stracquadanio, G., Papini, A., Liò, P., Nicosia, G.: Efficient behavior of photosynthetic organelles via Pareto optimality, identifiability, and sensitivity analysis. ACS Synth. Biol. 2(5), 274–288 (2013)CrossRefGoogle Scholar
  18. 18.
    Cutello, V., Narzisi, G., Nicosia, G., Pavone, M.: An immunological algorithm for global numerical optimization. In: Talbi, E.-G., Liardet, P., Collet, P., Lutton, E., Schoenauer, M. (eds.) EA 2005. LNCS, vol. 3871, pp. 284–295. Springer, Heidelberg (2006). doi: 10.1007/11740698_25 CrossRefGoogle Scholar
  19. 19.
    Long, M.R., Ong, W.K., Reed, J.L.: Computational methods in metabolic engineering for strain design. Curr. Opin. Biotechnol. 34, 135–141 (2015)CrossRefGoogle Scholar
  20. 20.
    Marino, S., Hogue, I.B., Ray, C.J., Kirschner, D.E.: A methodology for performing global uncertainty and sensitivity analysis in systems biology. J. Theor. Biol. 254(1), 178–196 (2008)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Kitano, H.: Biological robustness. Nat. Rev. Genet. 5(11), 826–837 (2004)CrossRefGoogle Scholar
  22. 22.
    Takayama, K., Wang, C., Besra, G.S.: Pathway to synthesis and processing of mycolic acids in Mycobacterium tuberculosis. Clin. Microbiol. Rev. 18(1), 81–101 (2005)CrossRefGoogle Scholar
  23. 23.
    Church, G.M., Regis, E.: Regenesis: How Synthetic Biology Will Reinvent Nature and Ourselves. Basic Books, New York (2014)Google Scholar
  24. 24.
    Church, G.M., Elowitz, M.B., Smolke, C.D., Voigt, C.A., Weiss, R.: Realizing the potential of synthetic biology. Nat. Rev. Mol. Cell Biol. 15(3), 289–294 (2014)CrossRefGoogle Scholar
  25. 25.
    Lee, S.K., Chou, H., Ham, T.S., Lee, T.S., Keasling, J.D.: Metabolic engineering of microorganisms for biofuels production: from bugs to synthetic biology to fuels. Curr. Opin. Biotechnol. 19(6), 556–563 (2008)CrossRefGoogle Scholar
  26. 26.
    Andrianantoandro, E., Basu, S., Karig, D.K., Weiss, R.: Synthetic biology: new engineering rules for an emerging discipline. Mol. Syst. Biol. 2(1) (2006)Google Scholar
  27. 27.
    Burgard, A.P., Pharkya, P., Maranas, C.D.: Optknock: a bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnol. Bioeng. 84(6), 647–657 (2003)CrossRefGoogle Scholar
  28. 28.
    Yang, L., Cluett, W.R., Mahadevan, R.: EMILiO: a fast algorithm for genome-scale strain design. Metab. Eng. 13(3), 272–281 (2011)CrossRefGoogle Scholar
  29. 29.
    Lun, D.S., Rockwell, G., Guido, N.J., Baym, M., Kelner, J.A., Berger, B., Galagan, J.E., Church, G.M.: Large-scale identification of genetic design strategies using local search. Mol. Syst. Biol. 5(1), 296 (2009)Google Scholar
  30. 30.
    Orth, J.D., Conrad, T.M., Na, J., Lerman, J.A., Nam, H., Feist, A.M., Palsson, B.: A comprehensive genome-scale reconstruction of Escherichia coli metabolism-2011. Mol. Syst. Biol. 7(1), 535 (2011)CrossRefGoogle Scholar
  31. 31.
    Ester, M., Kriegel, H., Sander, J., Xu, X., et al.: A density-based algorithm for discovering clusters in large spatial databases with noise. Kdd 96(34), 226–231 (1996)Google Scholar
  32. 32.
    Ciccazzo, A., Conca, P., Nicosia, G., Stracquadanio, G.: An advanced clonal selection algorithm with ad-hoc network-based hypermutation operators for synthesis of topology and sizing of analog electrical circuits. In: Bentley, P.J., Lee, D., Jung, S. (eds.) ICARIS 2008. LNCS, vol. 5132, pp. 60–70. Springer, Heidelberg (2008). doi: 10.1007/978-3-540-85072-4_6 CrossRefGoogle Scholar
  33. 33.
    Anile, A.M., Cutello, V., Giuseppe, N., Rascuna, R., Spinella, S.: Comparison among evolutionary algorithms and classical optimization methods for circuit design problems. In: IEEE Congress on Evolutionary Computation, CEC 2005, Edinburgh, UK, 2–5 September 2005, vol. 1, pp. 765–772. IEEE Press (2005)Google Scholar
  34. 34.
    Cutello, V., Narzisi, G., Giuseppe, N., Pavone, M.: Real coded clonal selection algorithm for global numerical optimization using a new inversely proportional hypermutation operator. In: The 21st Annual ACM Symposium on Applied Computing, SAC 2006, Dijon, France, 23–27 April 2006, vol. 2, pp. 950–954. ACM Press (2006)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Andrea Patané
    • 1
  • Piero Conca
    • 1
    • 2
    Email author
  • Giovanni Carapezza
    • 1
  • Andrea Santoro
    • 1
    • 2
    • 3
  • Jole Costanza
    • 3
  • Giuseppe Nicosia
    • 1
  1. 1.Department of Mathematics and Computer ScienceUniversity of CataniaCataniaItaly
  2. 2.Consiglio Nazionale delle Ricerche (CNR)CataniaItaly
  3. 3.Istituto Italiano di Tecnologia (IIT)MilanItaly

Personalised recommendations