Advertisement

Inference of Gene Regulatory Network Through Adaptive Dynamic Bayesian Network Modeling

  • Yaqun Wang
  • Scott A. Berceli
  • Marc Garbey
  • Rongling WuEmail author
Chapter
Part of the ICSA Book Series in Statistics book series (ICSABSS)

Abstract

Background: The reconstruction of gene regulatory networks (GRN) using gene expression data can gain new insights into the causality of transcriptional and cellular processes that make a complex living system. Dynamic Bayesian network (DBN) modeling has been increasingly used to reconstruct GRN for the temporal pattern of transcriptional interactions in a time course, but this approach requires expression data measured at even time intervals. In practice, time points at which gene expression is recorded are usually uneven-spaced, determined on the basis of distinct phases of biological processes. We reform DBN modeling to accommodate to any possible irregularity and sparsity of time course microarray data.

Results: The model is implemented with functional clustering that classifies dynamic genes into distinct clusters by adaptively fitting mean expression curves for each cluster, followed by a step of interpolating expression data at missing time points. The model is also equipped with unique power to integrate data from multiple expression experiments. We analyze two data sets of time course gene expression measured for vein bypass grafts in rabbits that receive two distinct treatments, high and low blood flow. The similarity and difference in the structure and organization of genetic networks can be identified under high and low flow, providing new insights into the mechanisms of how genes regulate each other to determine final phenotypic formation. Extensive simulation studies have been conducted to demonstrate the performance and property of the new model.

Conclusions: The results demonstrate that our adaptive Dynamic Bayesian Network model provides an unprecedented tool to elucidate a comprehensive picture of GRN. By analyzing real data sets from a surgical study and through extensive simulation studies, the new model has been well demonstrated for its usefulness and utility.

Keywords

Gene network Dynamic gene expression Gene clustering Gene expression plasticity Gene-environment interaction Transcriptional time lag Dynamic Bayesian network Legendre orthogonal polynomial Mixture model Multivariate normal distribution Bayesian information criterion EM algorithm Missing value interpolation Bilateral vein graft Rabbit model 

Notes

Acknowledgements

This research was supported by National Institute of Health grants 1U10HL098115, 5U01HL119178 and 5UL1TR000127. Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the National Institutes of Health.

References

  1. Akutsu, T., Miyano, S., Kuhara, S.: Inferring qualitative relations in genetic networks and metabolic pathways. Bioinformatics. 16, 727–734 (2000)CrossRefGoogle Scholar
  2. Aluru, S.: Handbook of Computational Molecular Biology. CRC Press, Boca Raton (2005)CrossRefGoogle Scholar
  3. Bansal, M., Della Gatta, G., Di Bernardo, D.: Inference of gene regulatory networks and compound mode of action from time course gene expression profiles. Bioinformatics. 22, 815–822 (2006)CrossRefGoogle Scholar
  4. Barabasi, A.-L., Gulbahce, N., Loscalzo, J.: Network medicine: a network-based approach to human disease. Nat. Rev. Genet. 12, 56–68 (2011)CrossRefGoogle Scholar
  5. Bolouri, H.: Modeling genomic regulatory networks with big data. Trends Genet. 30, 182–191 (2014)CrossRefGoogle Scholar
  6. Brazhnik, P., de la Fuente, A., Mendes, P.: Gene networks: how to put the function in genomics. Trends Biotechnol. 20, 467–472 (2002)CrossRefGoogle Scholar
  7. de Lichtenberg, U., Jensen, L.J., Brunak, S., et al.: Dynamic complex formation during the yeast cell cycle. Science. 307, 724–727 (2005)CrossRefGoogle Scholar
  8. De Smet, I., Lau, S., Mayer, U., et al.: Embryogenesis - the humble beginnings of plant life. Plant J. 61, 959–970 (2010)CrossRefGoogle Scholar
  9. Fernandez, C.M., Goldman, D.R., Jiang, Z., et al.: Impact of shear stress on early vein graft remodeling: a biomechanical analysis. Ann. Biomed. Eng. 32, 1484–1493 (2004)CrossRefGoogle Scholar
  10. Friedman, N., Murphy, K., Russell, S.: Learning the structure of dynamic probabilistic networks. In: Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, pp. 139–147. Morgan Kaufmann Publishers, San Francisco (1998)Google Scholar
  11. Friedman, N., Linial, M., Nachman, I., et al.: Using Bayesian networks to analyze expression data. J. Comput. Biol. 7, 601–620 (2000)CrossRefGoogle Scholar
  12. Gerstein, M.B., Kundaje, A., Hariharan, M., et al.: Architecture of the human regulatory network derived from ENCODE data. Nature. 489, 91–100 (2012)CrossRefGoogle Scholar
  13. Godsey, B.: Improved inference of gene regulatory networks through integrated Bayesian clustering and dynamic modeling of time-course expression data. PLoS One. 8, e68358 (2013)CrossRefGoogle Scholar
  14. Greenfield, A., Madar, A., Ostrer, H., et al.: DREAM4: combining genetic and dynamic information to identify biological networks and dynamical models. PLoS One. 5, e13397 (2010)CrossRefGoogle Scholar
  15. Hecker, M., Lambeck, S., Toepfer, S., et al.: Gene regulatory network inference: data integration in dynamic models–a review. Biosystems. 96, 86–103 (2009)CrossRefGoogle Scholar
  16. Hurley, D., Araki, H., Tamada, Y., et al.: Gene network inference and visualization tools for biologists: application to new human transcriptome datasets. Nucleic Acids Res. 40, 2377–2398 (2012)CrossRefGoogle Scholar
  17. Jiang, Z., Wu, L., Miller, B.L., et al.: A novel vein graft model: adaptation to differential flow environments. Am. J. Phys. Heart Circ. Phys. 286, H240–H245 (2004)Google Scholar
  18. Kim, S.Y., Imoto, S., Miyano, S.: Inferring gene networks from time series microarray data using dynamic Bayesian networks. Brief. Bioinform. 4(3), 228–235 (2003)CrossRefGoogle Scholar
  19. Kim, B.-R., Zhang, L., Berg, A., et al.: A computational approach to the functional clustering of periodic gene-expression profiles. Genetics. 180, 821–834 (2008)CrossRefGoogle Scholar
  20. Kim, B.-R., McMurry, T., Zhao, W., et al.: Wavelet-based functional clustering for patterns of high-dimensional dynamic gene expression. J. Comput. Biol. 17, 1067–1080 (2010)MathSciNetCrossRefGoogle Scholar
  21. Kourou, K., Exarchos, K.P., Papaloukas, C., Fotiadis, D.I.: A Bayesian network-based approach for discovering oral cancer candidate biomarkers. In: Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE, pp. 7663–7666. IEEE, New York (2015)Google Scholar
  22. Li, N., McMurry, T., Berg, A., et al.: Functional clustering of periodic transcriptional profiles through ARMA(p,q). PLoS One. 5(4), e9894 (2010)CrossRefGoogle Scholar
  23. Li, Z., Li, P., Krishnan, A., Liu, J.: Large-scale dynamic gene regulatory network inference combining differential equation models with local dynamic Bayesian network analysis. Bioinformatics. 27, 2686–2691 (2011)CrossRefGoogle Scholar
  24. Luan, Y., Li, H.: Clustering of time-course gene expression data using a mixed-effects model with B-splines. Bioinformatics. 19(4), 474–482 (2003)CrossRefGoogle Scholar
  25. Martin, S., Zhang, Z., Martino, A., et al.: Boolean dynamics of genetic regulatory networks inferred from microarray time series data. Bioinformatics. 23, 866–874 (2007)CrossRefGoogle Scholar
  26. Murphy, K., Mian, S.: Modelling gene expression data using dynamic Bayesian networks. Technical Report, Computer Science Division, University of California, Berkeley (1999)Google Scholar
  27. Ogami, K., Yamaguchi, R., Imoto, S., et al.: Computational gene network analysis reveals TNF-induced angiogenesis. BMC Syst. Biol. 6(Suppl 2), S12 (2012)CrossRefGoogle Scholar
  28. Ortiz-Gutiérrez, E., García-Cruz, K., Azpeitia, E., Castillo, A., de la Paz Sánchez, M., Álvarez-Buylla, E.R.: A dynamic gene regulatory network model that recovers the cyclic behavior of Arabidopsis thaliana cell cycle. PLoS Comput. Biol. 11(9), e1004486 (2015)CrossRefGoogle Scholar
  29. Quint, M., Drost, H.G., Gabel, A., et al.: A transcriptomic hourglass in plant embryogenesis. Nature. 490, 98–101 (2012)CrossRefGoogle Scholar
  30. Rustici, G., Mata, J., Kivinen, K., et al.: Periodic gene expression program of the fission yeast cell cycle. Nat. Genet. 36, 809–817 (2004)CrossRefGoogle Scholar
  31. Song, J.J., Lee, H.J., Morris, J.S., Kang, S.: Clustering of time-course gene expression data using functional data analysis. Comput. Biol. Chem. 31(4), 265–274 (2007)CrossRefGoogle Scholar
  32. Wang, Y., Xu, M., Wang, Z., et al.: How to cluster gene expression dynamics in response to environmental signals. Brief. Bioinform. 13, 162–174 (2011)CrossRefGoogle Scholar
  33. Wang, J., Chen, B., Wang, Y., et al.: Reconstructing regulatory networks from the dynamic plasticity of gene expression by mutual information. Nucleic Acids Res. 41, e97 (2013)CrossRefGoogle Scholar
  34. Wessels, L.F., van Someren, E.P., Reinders, M.J., et al.: A comparison of genetic network models. Pac. Symp. Biocomput. 6, 508–519 (2001)Google Scholar
  35. Xiang, D., Venglat, P., Tibiche, C., et al.: Genome-wide analysis reveals gene expression and metabolic network dynamics during embryo development in Arabidopsis. Plant Physiol. 156, 346–356 (2011)CrossRefGoogle Scholar
  36. Yosef, N., Shalek, A.K., Gaublomme, J.T., et al.: Dynamic regulatory network controlling TH17 cell differentiation. Nature. 496, 461–468 (2013)CrossRefGoogle Scholar
  37. Yu, J., Smith, V.A., Wang, P.P., et al.: Advances to Bayesian network inference for generating causal networks from observational biological data. Bioinformatics. 20, 3594–3603 (2004)CrossRefGoogle Scholar
  38. Zhang, J.: Epistatic clustering: a model-based approach for identifying links between clusters. J. Am. Stat. Assoc. 108, 1366–1384 (2013)MathSciNetCrossRefGoogle Scholar
  39. Zhang, X., Liu, K., Liu, Z.-P., et al.: NARROMI: a noise and redundancy reduction technique improves accuracy of gene regulatory network inference. Bioinformatics. 29, 106–113 (2013)CrossRefGoogle Scholar
  40. Zhang, X., Zhao, J., Hao, J.K., et al.: Conditional mutual inclusive information enables accurate quantification of associations in gene regulatory networks. Nucleic Acids Res. 43(5), e31 (2015)CrossRefGoogle Scholar
  41. Zhu, H., Rao, R.S.P., Zeng, T., et al.: Reconstructing dynamic gene regulatory networks from sample-based transcriptional data. Nucleic Acids Res. 40, 10657–10667 (2012)CrossRefGoogle Scholar
  42. Zou, M., Conzen, S.D.: A new dynamic bayesian network (dbn) approach for identifying gene regulatory networks from time course microarray data. Bioinformatics. 21, 71–79 (2005)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Yaqun Wang
    • 1
    • 2
  • Scott A. Berceli
    • 3
  • Marc Garbey
    • 4
  • Rongling Wu
    • 5
    Email author
  1. 1.Department of StatisticsThe Pennsylvania State UniversityUniversity ParkUSA
  2. 2.Department of BiostatisticsRutgers, The State University of New JerseyNew BrunswickUSA
  3. 3.Department of SurgeryUniversity of FloridaGainesvilleUSA
  4. 4.Department of Computer ScienceUniversity of HoustonHoustonUSA
  5. 5.Department of Public Health SciencesThe Pennsylvania State UniversityHersheyUSA

Personalised recommendations