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Hierarchical parameter estimation of GRN based on topological analysis

Hierarchical parameter estimation of GRN based on topological analysis

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Reverse engineering of gene regulatory network (GRN) is an important and challenging task in systems biology. Existing parameter estimation approaches that compute model parameters with the same importance are usually computationally expensive or infeasible, especially in dealing with complex biological networks.In order to improve the efficiency of computational modeling, the paper applies a hierarchical estimation methodology in computational modeling of GRN based on topological analysis. This paper divides nodes in a network into various priority levels using the graph-based measure and genetic algorithm. The nodes in the first level, that correspond to root strongly connected components(SCC) in the digraph of GRN, are given top priority in parameter estimation. The estimated parameters of vertices in the previous priority level ARE used to infer the parameters for nodes in the next priority level. The proposed hierarchical estimation methodology obtains lower error indexes while consuming less computational resources compared with single estimation methodology. Experimental outcomes with insilico networks and a realistic network show that gene networks are decomposed into no more than four levels, which is consistent with the properties of inherent modularity for GRN. In addition, the proposed hierarchical parameter estimation achieves a balance between computational efficiency and accuracy.

References

    1. 1)
      • 1. Marbach, D., Costello, J.C., Kĺźffner, B., et al: ‘Wisdom of crowds for robust gene network inference’, Nat. Methods, 2012, 9, (8), pp. 796806.
    2. 2)
      • 2. Bansal, M., Gatta, G.D., Bernardo, D.D.: ‘Inference of gene regulatory networks and compound mode of action from time course gene expression profiles’, Bioinformatics, 2006, 22, (7), pp. 815822.
    3. 3)
      • 3. Xing, H.M., Gardner, T.S.: ‘The mode-of-action by network identification algorithm: a network biology approach for molecular target identification’, Nat. Protocols, 2006, 1, (6), pp. 25512554.
    4. 4)
      • 4. Hase, T., Ghosh, S., Kitano, H., et al: ‘Harnessing diversity towards the reconstructing of large scale gene regulatory networks’, PLOS Comput. Biol., 2013, 9, (11), p. e1003361.
    5. 5)
      • 5. Chang, Y.H., Gray, J.W., Tomlin, C.J., et al: ‘Exact reconstruction of gene regulatory networks using compressive sensing’, BMC Bioinf., 2014, 15, (1), pp. 400421.
    6. 6)
      • 6. Ghanbari, M., Lasserre, J., Vingron, M.: ‘Reconstruction of gene networks using prior knowledge’, BMC Syst. Biol., 2015, 9, (1), pp. 8494.
    7. 7)
      • 7. Meyer, P., Cokelaer, T., Chandran, D., et al: ‘Network topology and parameter estimation: from experimental design methods to gene regulatory network kinetics using a community based approach’, BMC Syst. Biol., 2014, 8, (1), pp. 1331.
    8. 8)
      • 8. Xiong, J., Zhou, T.: ‘Structure identification for gene regulatory networks via linearization and robust state estimation’, Automatica, 2014, 50, (11), pp. 27652776.
    9. 9)
      • 9. Zhang, X.J., Zhao, X.M., He, K., et al: ‘Inferring gene regulatory networks from gene expression data by path consistency algorithm based on conditional mutual information’, Bioinformatics, 2012, 28, (1), pp. 98104.
    10. 10)
      • 10. Zhang, X.J., Liu, K.Q., Liu, Z.P., et al: ‘NARROMI: a noise and redundancy reduction technique improves accuracy of gene regulatory network inference’, Bioinformatics, 2013, 29, (1), pp. 106113.
    11. 11)
      • 11. Margolin, A.A., Nemenman, I., Basso, K, et al: ‘ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context’, BMC Bioinf., 2004, 7, (1), pp. 115.
    12. 12)
      • 12. Lachmann, A., Giorgi, F.M., Lopez, G., et al: ‘ARACNe-AP: gene network reverse engineering through adaptive partitioning inference of mutual information’, Bioinformatics, 2016, 32, (14), pp. 22332235.
    13. 13)
      • 13. Cao, J.G., Zhao, H.Y.: ‘Estimating dynamic models for gene regulation networks’, Bioinformatics, 2008, 24, (14), pp. 16191624.
    14. 14)
      • 14. Fan, M., Kuwahara, H., Wang, X., et al: ‘Parameter estimation methods for gene circuit modeling from time-series mRNA data: a comparative study’, Briefings Bioinf., 2015, 16, (6), pp. 987999.
    15. 15)
      • 15. Biswas, S., Acharyya, S.: ‘Parameter estimation of gene regulatory network using honey bee mating optimization’, Int. Conf. Emerg. Appl. Inf. Technol., 2015, 5, (3), pp. 110.
    16. 16)
      • 16. Dahlquist, K.D., Fitzpatrick, B.G., Camacho, E.T., et al: ‘Parameter estimation for gene regulatory networks from microarray data: cold shock response in Saccharomyces cerevisiae’, Bull. Math. Biol., 2015, 77, (8), pp. 14571492.
    17. 17)
      • 17. Kuwahara, H., Fan, M., Wang, S.J., et al: ‘A framework for scalable parameter estimation of gene circuit models using structural information’, Bioinformatics, 2013, 29, (13), pp. 98107.
    18. 18)
      • 18. Faith, J.J., Hayete, B., Thaden, J.T., et al: ‘Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles’, PLOS Biol., 2007, 5, (1), p. e8.
    19. 19)
      • 19. Vignes, M., Vandel, J., Allouche, D., et al: ‘Gene regulatory network reconstruction using Bayesian networks, the Dantzig selector, the Lasso and their meta-analysis’, PLOS One, 2011, 6, (12), p. e29165.
    20. 20)
      • 20. Liu, F., Zhang, S.W., Guo, W.F, et al: ‘Inference of gene regulatory network based on local Bayesian networks’, PLOS Comput. Biol., 2016, 12, (8), p. e1005024.
    21. 21)
      • 21. Diop, S., Fliess, M.: ‘Nonlinear observability, identifiability, and persistent trajectories’. IEEE Conf. Proc. Decision and Control, Brighton, UK, 1991, (1), pp. 714719.
    22. 22)
      • 22. Gui, S., Rice, A.P., Chen, R., et al: ‘A scalable algorithm for structure identification of complex gene regulatory network from temporal expression data’, BMC Bioinf., 2017, 18, (1), pp. 7486.
    23. 23)
      • 23. Mason, O., Verwoerd, M.: ‘Graph theory and networks in biology’, IET Syst. Biol., 2007, 1, (2), pp. 89119.
    24. 24)
      • 24. Chen, G., Larsen, P., Almasri, E., et al: ‘Rank-based edge reconstruction for scale-free genetic regulatory networks’, BMC Bioinf., 2008, 9, (1), p. 75.
    25. 25)
      • 25. Roy, S.: ‘Systems biology beyond degree, hubs and scale-free networks: the case for multiple metrics in complex networks’, Syst. Synth. Biol., 2012, 6, (1–2), p. 31.
    26. 26)
      • 26. Yang, B., Xu, J., Liu, B., et al: ‘Inferring gene regulatory networks with a scale-free property based informative prior’. IEEE Int. Conf. Biomedical Engineering and Informatics, Shenyang, China, 2015, pp. 542547.
    27. 27)
      • 27. Li, L., Alderson, D., Doyle, J.C., et al: ‘Towards a theory of scale-free graphs: definition, properties, and implications’, Internet Math., 2005, 2, (4), pp. 431523.
    28. 28)
      • 28. Barrat, A., Barth Lemy, M., Pastorsatorras, R, et al: ‘The architecture of complex weighted networks’, Proc. Natl. Acad. Sci. USA, 2004, 101, (11), pp. 37473752.
    29. 29)
      • 29. Piraveenan, M., Prokopenko, M., Hossain, L., et al: ‘Percolation centrality: quantifying graph-theoretic impact of nodes during percolation in networks’, PLOS One, 2013, 8, (1), p. e53095.
    30. 30)
      • 30. McLendon, W., Hendrickson, B., Plimption, S.J., et al: ‘Finding strongly connected components in distributed graphs’, J. Parallel Distrib. Comput., 2005, 65, (8), pp. 901910.
    31. 31)
      • 31. Liu, Y.Y., Slotine, J.J., Barabási, A.L.: ‘Observability of complex systems’, Proc. Natl. Acad. Sci., 2013, 110, (7), pp. 24602465.
    32. 32)
      • 32. Travençolo, B.A.N., Costa, L.F.: ‘Accessibility in complex networks’, Phys. Lett. A, 2008, 373, (1), pp. 8995.
    33. 33)
      • 33. Liu, Y.Y., Slotine, J.J., Barabási, A.L.: ‘Controllability of complex networks’, Nature, 2011, 473, (7346), pp. 167173.
    34. 34)
      • 34. Lin, C.T.: ‘Structural controllability’, IEEE Trans. Autom. Control, 1974, 19, (3), pp. 201208.
    35. 35)
      • 35. Dodds, P.S., Muhamad, R., Watts, D.J.: ‘An experimental study of search in global social networks’, Science, 2003, 301, (5634), pp. 827829.
    36. 36)
      • 36. Vu, T.T., Vohradsky, J.: ‘Nonlinear differential equation model for quantification of transcriptional regulation applied to microarray data of Saccharomyces cerevisiae’, Nucleic Acids Res., 2007, 35, (1), pp. 279287.
    37. 37)
      • 37. Ghasemi, O., Lindsey, M.L., Yang, T., et al: ‘Bayesian parameter estimation for nonlinear modelling of biological pathways’, BMC Syst. Biol., 2011, 5, (3), pp. 110.
    38. 38)
      • 38. Eydgahi, H., William, W.C., Muhlich, J., et al: ‘Properties of cell death models calibrated and compared using Bayesian approaches’, Mol. Syst. Biol., 2013, 9, (1), pp. 644660.
    39. 39)
      • 39. Reginska, T.: ‘A regularization parameter in discrete ill-posed problems’, SIAM J. Sci. Comput., 1996, 17, (3), pp. 740749.
    40. 40)
      • 40. Arthur, E.H., Robert, W.K.: ‘Ridge regression: biased estimation for nonorthogonal problems’, Technometrics, 2000, 42, (1), pp. 8086.
    41. 41)
      • 41. Greenfield, A., Madar, A., Ostrer, H., et al: ‘DREAM4: combining genetic and dynamic information to identify biological networks and dynamical models’, PLOS One, 2010, 5, (10), p. e13397.
    42. 42)
      • 42. Schade, B., Jansen, G., Whiteway, M., et al: ‘Cold adaptation in budding yeast’, Mol. Biol. Cell, 2004, 15, (12), pp. 54925502.
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