MINIMUM MUTATION ALGORITHM FOR GAPLESS METABOLIC NETWORK EVOLUTION

Esa Pitkänen, Juho Rousu, Mikko Arvas

Abstract

We present a method for inferring the structure of ancestral metabolic networks directly from the networks of observed species and their phylogenetic tree. Our method aims to minimize the number of mutations on the phylogenetic tree, whilst keeping the ancestral networks structurally feasible, i.e., free of reaction gaps. To this end, we present a parsimony-based method that generates metabolic network phylogenies where the ancestral nodes are required to represent gapless metabolic networks, networks where all reactions are reachable from external substrates. In particular, we introduce the gapless minimum mutation problem: finding phylogenies of gapless metabolic networks when the topology of the phylogenetic tree is given, but the content of ancestral nodes is unknown. The gapless minimum mutation problem is shown to be computationally hard to solve even approximatively. We then propose an efficient dynamic programming based heuristic that combines knowledge on both the metabolic network topology and phylogeny of species. Specifically, the reconstruction of each ancestral network is guided by the heuristic to minimize the total phylogeny cost. We experiment by reconstructing phylogenies generated under a simple random model and derived from KEGG for a number of fungal species.

References

  1. Alon, N., Moshkovitz, D., and Safra, S. (2006). Algorithmic construction of sets for k-restrictions. ACM Trans. Algorithms, 2(2):153-177.
  2. Arvas, M., Kivioja, T., Mitchell, A., Saloheimo, M., Ussery, D., Penttilä, M., and Oliver, S. (2007). Comparison of protein coding gene contents of the fungal phyla Pezizomycotina and Saccharomycotina. BMC Genomics, 8(1):325.
  3. Borenstein, E., Kupiec, M., Feldman, M. W., and Ruppin, E. (2008). Large-scale reconstruction and phylogenetic analysis of metabolic environments. PNAS, 105(38):14482-14487.
  4. Bourque, G. and Sankoff, D. (2004). Improving gene network inference by comparing expression time-series across species, developmental stages or tissues. J Bioinform Comput Biol, 2(4):765-783.
  5. Caetano-Anollés, G., Yafremava, L., Gee, H., CaetanoAnollés, D., Kim, H., and Mittenthal, J. (2009). The origin and evolution of modern metabolism. The International Journal of Biochemistry & Cell Biology, 41(2):285-297.
  6. Clemente, J., Satou, K., and Valiente, G. (2007). Phylogenetic reconstruction from non-genomic data. Bioinformatics, 23(2):e110.
  7. Clemente, J. C., Ikeo, K., Valiente, G., and Gojobori, T. (2009). Optimized ancestral state reconstruction using sankoff parsimony. BMC Bioinformatics, 10(51).
  8. Dandekar, T., Schuster, S., Snel, B., Huynen, M., and Bork, P. (1999). Pathway alignment: application to the comparative analysis of glycolytic enzymes. Biochem J., 343(Pt 1):115-124.
  9. Deacon, J. (2006). Fungal biology. Wiley-Blackwell.
  10. Fitch, W. M. (1971). Toward defining the course of evolution: minimum change for a specific tree topology. Syst. Zool., 20:406-416.
  11. Fitzpatrick, D., Logue, M., Stajich, J., and Butler, G. (2006). A fungal phylogeny based on 42 complete genomes derived from supertree and combined gene analysis. BMC Evolutionary Biology, 6(1):99.
  12. Garey, M. R. and Johnson, D. S. (1979). Computers and Intractability: A Guide to the Theory of NPCompleteness. W. H. Freeman.
  13. Gusfield, D. (1997). Algorithms on Strings, Trees, and Sequences. Cambridge University Press.
  14. Handorf, T., Christian, N., Ebenhöh, O., and Kahn, D. (2008). An environmental perspective on metabolism. Journal of Theoretical Biology, 252(3):530-537.
  15. Jamshidi, N. and Palsson, B. O. (2007). Investigating the metabolic capabilities of Mycobacterium tuberculosis H37Rv using the in silico strain iNJ661 and proposing alternative drug targets. BMC Systems Biology, 1(26).
  16. Kanehisa, M., Araki, M., Goto, S., Hattori, M., Hirakawa, M., Itoh, M., Katayama, T., Kawashima, S., Okuda, S., Tokimatsu, T., and Yamanishi, Y. (2008). Kegg for linking genomes to life and the environment. Nucleic Acids Res., 36:D480-D484.
  17. Kreimer, A., Borenstein, E., Gophna, U., and Ruppin, E. (2008). The evolution of modularity in bacterial metabolic networks. Proceedings of the National Academy of Sciences, 105(19):6976.
  18. Lacroix, V., Cottret, L., Thebault, P., and Sagot, M.-F. (2008). An introduction to metabolic networks and their structural analysis. IEEE Transactions on Computational Biology and Bioinformatics, 5(4):594-617.
  19. Mano, A., Tuller, T., Bj, O., and Pinter, R. Y. (2010). Comparative classification of species and the study of pathway evolution based on the alignment of metabolic pathways. BMC Bioinformatics, 11(Suppl 1):S38.
  20. Mithani, A., Preston, G., and Hein, J. (2010). A bayesian approach to the evolution of metabolic networks on a phylogeny. PLoS Computational Biology, 6(8).
  21. Mithani, A., Preston, G. M., and Hein, J. (2009). A stochastic model for the evolution of metabolic networks with neighbor dependence. Bioinformatics, 25(12):1528- 1535.
  22. Palsson, B. (2006). Systems biology: properties of reconstructed networks. Cambridge University Press Cambridge.
  23. Pitkänen, E., Rantanen, A., Rousu, J., and Ukkonen, E. (2005). Finding feasible pathways in metabolic networks. In Advances in Informatics: 10th Panhellenic Conference on Informatics (PCI 2005). Lecture Notes in Computer Science 3746, pages 123-133.
  24. Pitkänen, E., Rantanen, A., Rousu, J., and Ukkonen, E. (2008). A computational method for reconstructing gapless metabolic networks. In Proceedings of the 2nd International Conference on Bioinformatics Research and Development (BIRD'08), volume 13 of Communications in Computer and Information Science. Springer.
  25. Pitkänen, E., Rousu, J., and Ukkonen, E. (2010). Computational methods for metabolic reconstruction. Current Opinion in Biotechnology, 21(1):70-77.
  26. Raman, K. and Chandra, N. (2009). Flux balance analysis of biological systems: applications and challenges. Briefings in Bioinformatics, 10(4):435-449.
  27. Rantanen, A., Rousu, J., Jouhten, P., Zamboni, N., Maaheimo, H., and Ukkonen, E. (2008). An analytic and systematic framework for estimating metabolic flux ratios from 13 C tracer experiments. BMC Bioinformatics, 9(1):266.
  28. Raz, R. and Safra, S. (1997). A sub-constant errorprobability low-degree test, and a sub-constant errorprobability PCP characterization of NP. In Proceedings of the Twenty-Ninth Annual ACM Symposium on Theory of Computing, pages 475-484.
  29. Sankoff, D. (1975). Minimal mutation trees of sequences. SIAM J. Appl, 28:35-42.
  30. Sharan, R. and Ideker, T. (2006). Modeling cellular machinery through biological network comparison. Nature Biotechnology, 24:427-433.
  31. Sigurdsson, M. I., Jamshidi, N., Jonsson, J. J., and Palsson, B. O. (2009). Genome-scale network analysis of imprinted human metabolic genes. Epigenetics, 4(1):43- 46.
  32. Tohsato, Y., Matsuda, H., and Hashimoto, A. (2000). A multiple alignment algorithm for metabolic pathway analysis using enzyme hierarchy. In Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology, pages 376-383.
  33. Tuller, T., Birin, H., Gophna, U., Kupiec, M., and Ruppin, E. (2010). Reconstructing ancestral gene content by coevolution. Genome Res., 20(1):122-132.
  34. Wagner, A. (2009). Evolutionary constraints permeate large metabolic networks. BMC Evolutionary Biology, 9(1):231.
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Paper Citation


in Harvard Style

Pitkänen E., Rousu J. and Arvas M. (2011). MINIMUM MUTATION ALGORITHM FOR GAPLESS METABOLIC NETWORK EVOLUTION . In Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2011) ISBN 978-989-8425-36-2, pages 28-38. DOI: 10.5220/0003132200280038


in Bibtex Style

@conference{bioinformatics11,
author={Esa Pitkänen and Juho Rousu and Mikko Arvas},
title={MINIMUM MUTATION ALGORITHM FOR GAPLESS METABOLIC NETWORK EVOLUTION},
booktitle={Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2011)},
year={2011},
pages={28-38},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003132200280038},
isbn={978-989-8425-36-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2011)
TI - MINIMUM MUTATION ALGORITHM FOR GAPLESS METABOLIC NETWORK EVOLUTION
SN - 978-989-8425-36-2
AU - Pitkänen E.
AU - Rousu J.
AU - Arvas M.
PY - 2011
SP - 28
EP - 38
DO - 10.5220/0003132200280038