Oates, Chris J. and Mukherjee, Sach. (2012) Network inference and biological dynamics. Annals of Applied Statistics, Vol.6 (No.3). pp. 1209-1235. ISSN 1932-6157

Preview

PDF WRAP_Oates_0866100-px-131211-paper.pdf - Accepted Version - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader Download (632Kb)

Abstract

Network inference approaches are now widely used in biological applications to probe regulatory relationships between molecular components such as genes or proteins. Many methods have been proposed for this setting, but the connections and differences between their statistical formulations have received less attention. In this paper, we show how a broad class of statistical network inference methods, including a number of existing approaches, can be described in terms of variable selection for the linear model. This reveals some subtle but important differences between the methods, including the treatment of time intervals in discretely observed data. In developing a general formulation, we also explore the relationship between singlecell stochastic dynamics and network inference on averages over cells. This clarifies the link between biochemical networks as they operate at the cellular level and network inference as carried out on data that are averages over populations of cells. We present empirical results, comparing thirty-two network inference methods that are instances of the general formulation we describe, using two published dynamical models. Our investigation sheds light on the applicability and limitations of network inference and provides guidance for practitioners and suggestions for experimental design.

Item Type:	Journal Article
Subjects:	Q Science > QA Mathematics Q Science > QH Natural history > QH301 Biology
Divisions:	Faculty of Science > Centre for Complexity Science Faculty of Science > Statistics
Library of Congress Subject Headings (LCSH):	System analysis, Biological control systems -- Mathematical models
Journal or Publication Title:	Annals of Applied Statistics
Publisher:	Insitute of Mathematical Statistics
ISSN:	1932-6157
Date:	January 2012
Volume:	Vol.6
Number:	No.3
Page Range:	pp. 1209-1235
Identification Number:	10.1214/11-AOAS532
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access
Funder:	Engineering and Physical Sciences Research Council (EPSRC), National Cancer Institute (U.S.) (NCI)
Grant number:	EP/E501311/1 (EPSRC), U54 CA 112970 (NCI)
References:	Aijo, T., Lahdesmaki, H. (2009) Learning gene regulatory networks from gene expression measurements using non-parametric molecular kinetics, Bioinformatics, 25(22), 2937- 44. Altay, G., Emmert- Streib, F. (2010) Revealing differences in gene network inference algorithms on the network level by ensemble methods, Bioinformatics, 26(14), 1738-1744. Babu, M.M., Luscombe, N.M., Aravind, L., et al. (2004) Structure and evolution of transcriptional regulatory networks, Current Opinion in Structural Biology, 14(3), 283-91. Bansal, M., di Bernardo, D. (2007) Inference of gene networks from temporal gene expression profiles, IET Systems Biology, 5, 306-12. Bansal, M., Belcastro, V., Ambesi-Impiombato, A. et al. (2007) How to infer gene networks from expression profiles, Mol. Sys. Bio., 3(78). Beal, M.J., Falciani, F. Ghahramani, Z., et al. (2005) A Bayesian approach to reconstructing genetic regulatory networks with hidden factors, Bioinformatics, 21(3), 349. Bolstad, A., Van Veen, B., Nowak, R. (2011) Causal Network Inference via Group Sparse Regularization, IEEE Trans. Signal Processing, 99. Bonneau, R. (2008) Learning biological networks: from modules to dynamics, Nat. Chem. Bio., 4, 658-64. Burnham, K.P., Anderson, D.R. (2002) Model Selection and Multimodal Inference: A Practical Information-Theoretic Approach, Springer, New York. Camacho, D.M., Collins, J.J. (2009) Systems biology strikes gold, Cell, 137(1), 24- 6. Cantone, I., Marucci, L., Iorio, F., et al. (2009) A yeast synthetic network for in vivo assessment of reverse-engineering and modeling approaches, Cell, 137(1), 172-81. Craciun, G., Pantea, C. (2008) Identifiability of chemical reaction networks, J. Math. Chem., 44, 244-59. Dargatz, C. (2010) Bayesian Inference for Diffusion Processes with Applications in Life Sciences, PhD Thesis, Munchen. Davidson, E.H. (2001) Gene Regulatory Systems. Development And Evolution, Academic Press, San Diego, 2001. Deltell, A. (2011) Objective Bayes Criteria for Variable Selection, PhD thesis, Universitat de Valencia. Eaton, D., Murphy, K. (2007) Exact Bayesian structure learning from uncertain interventions, Proceedings of 11th Conference on Artificial Intelligence and Statistics (AISTATS- 07). Ellis, B, Wong, W.H. (2008) Learning causal Bayesian network structures from experimental data, JASA, 103(482), 778-89. Elowitz, M.B., Levine, A.J., Siggia, E.D., et al. (2002) Stochastic gene expression in a single cell, Science, 297(5584), 1129-31. Fawcett, T. (2005) An introduction to ROC analysis, Pattern Recognition Letters, 27, 861-874. Friedman, N., Linial, M., Nachman, I., et al. (2000) Using Bayesian networks to analyze expression data, J. Comp. Bio., 7, 601-620. Friedman, J., Hastie, T., Tibshirani, R. (2008) Sparse inverse covariance estimation with the graphical lasso, Biostatistics, 9(3), 432. Friedman, J, Koller, D. (2003) Being Bayesian about network structure. A Bayesian approach to structure discovery in Bayesian networks, Machine Learning, 50(1), 95-125. Hache, H., Lehrach, H., Herwig, R. (2009) Reverse Engineering of Gene Regulatory Networks: A Comparative Study, EURASIP Journal on Bioinformatics and Systems Biol- ogy, 617281. Hans, C. and Dobra, A. and West, M. (2007) Shotgun stochastic search for \large p" regression, Journal of the American Statistical Association, 102(478), 507-516. Hecker, M., Lambeck, S., Toepfer, S., et al. (2009) Gene regulatory network inference: Data integration in dynamic models - A review, Biosystems, 96(1), 86-103. Hill, S., Lu,Y., Molina, J. et al. (2011) Bayesian Inference of Signaling Network Topology in a Single Cancer, In preparation. Hurn, A., Jeisman, J., Lindsay, K. (2007) Seeing the wood for the trees: A critical evaluation of methods to estimate the parameters of stochastic differential equations, Journal of Financial Econometrics, 5(3), 390. Grzegorczyk, M., Husmeier, D. (2010) Improvements in the reconstruction of time-varying gene regulatory networks: dynamic programming and regularization by information sharing among genes, Bioinformatics, 27(5), 693-9. Ideker, T., Lauenburger, D. (2003) Building with a scaffold: emerging strategies for high to low level cellular modelling, Trends in Biotechnology, 21(6), 255-62. Kim, S.Y., Imoto, S., Miyano, S. (2003) Inferring gene networks from time series microarray data using dynamic Bayesian networks, Briefings in Bioinformatics, 4(3), 228- 35. Knowles, D.A., Ghahramani, Z. (2011) Nonparametric Bayesian Sparse Factor Models with Application to Gene Expression Modelling, AOAS, 5(2B), 1534-52. Kolar, M., Song, L., Xing, E.P. (2009) Sparsistent learning of varying-coefficient models with structural changes, NIPS, 22, 100614. Kou, S., Sunney, X., Jun, L. (2005) Bayesian analysis of single-molecule experimental data (with discussion), J. Roy. Statist. Soc., C, 54, 469-506. Lebre, S., Becq, J., Devaux, F. et al. (2010) Statistical inference of the time-varying structure of gene- regulation networks, BMC Systems Biology, 4, 130. Lee, W.P., Tzou, W.S. (2009) Computational methods for discovering gene networks from expression data, Brief. Bioinform., 10(4), 408-423. Li, C-W., Chen, B-S. (2010) Identifying Functional Mechanisms of Gene and Protein Regulatory Networks in Response to a Broader Range of Environmental Stresses, Comp. and Func. Genomics, 408705. Li, Z., Li, P., Krishnan, A. (2011) Large-scale dynamic gene regulatory network inference combining differential equation models with local dynamic Bayesian network analysis, Bioinformatics, 27(19), 2686-91. Marbach, D., Schater, T., Mattiussi, C., et al. (2009) Generating realistic in silico gene networks for performance assessment of reverse engineering methods, Journal of com- putational biology, 16(2), 229-39. Markowetz, F., Spang, R. (2007) Inferring cellular networks - A review, BMC Bioinfor- matics, 8(Suppl. 6), S5. McAdams, H.H., Arkin, A. (1997) Stochastic mechanisms in gene expression, PNAS, 94, 814-9. Meinshausen, N., Buhlmann, P. (2006) High-dimensional graphs and variable selection with the lasso, The Annals of Statistics, 34(3), 1436-62. Minty, J.J., Varedi, K.S.M., Nina, L.X. (2009) Network benchmarking: a happy marriage between systems and synthetic biology, Chemistry and Biology, 16(3), 239-41. Morrissey, E.R., Juarez, M.A., Denby, K.J., et al. (2010) On reverse engineering of gene interaction networks using time course data with repeated measurements, Bioinformatics, 26(18), 2305-2312. Mukherjee, S., Speed, T.P. (2008) Network inference using informative priors, PNAS, 105(38), 14313-8. Nam, D., Yoon, S.H., Kim, J.F. (2007) Ensemble learning of genetic networks from timeseries expression data, Bioinformatics, 23(23), 3225-3231. Oates, C.J. and Mukherjee, S. (2011) Supplement to \Biological Dynamics and Network Inference". Oksendal, B. (1998) Stochastic Differential Equations, An Introduction with Applications, 5th ed., Springer. Opgen-Rhein, R., Strimmer, K. (2007) Learning causal networks from systems biology time course data: an effective model selection procedure for the vector autoregressive process, BMC Bioinformatics, 8, (Suppl. 2), S3. Pearl, J. (2009) Causal inference in statistics: An overview, Statistical Surveys, 3, 96-146. Prill, R.J., Marbach, D., Saez- Rodriguez, et al. (2010) Towards a rigorous assessment of systems biology models: the DREAM3 challenges, PloS one, 5, e9202. Paulsson, J. (2005) Models of stochastic gene expression, Physics of Life Reviews, 2(2), 157-75. Rogers, S., Khanin, R., Girolami, M. (2007) Bayesian model-based inference of transcription factor activity, BMC Bioinformatics, 8(S2). Schoeberl, B., Eichler-Jonsson, C., Gilles, E.D., et al. (2002) Computational modeling of the dynamics of the MAP kinase cascade activated by surface and internalized EGF receptors, Nature Biotechnology , 20(4), 370-5. Smith, V.A., Jarvis, E.D., Hartemink, A.J. (2002) Evaluating functional network inference using simulations of complex biological systems, Bioinformatics, 18(S1), S216-S224. Swain, P.S., Elowitz, M.B., Siggia, E.D. (2002) Intrinsic and extrinsic contributions to stochasticity in gene expression, PNAS, 99, 12795-800. Swat, M., Kel, A., Herzel, H. (2004) Bifurcation analysis of the regulatory modules of the mammalian G1/S transition, Bioinformatics, 20(10), 1506-11. Van den Bulcke, T., Van Leemput, K., Naudts, B., et al. (2006) SynTReN: a generator of synthetic gene expression data for design and analysis of structure learning algorithms, BMC Bioinformatics, 7(43). Van Kampen, N.G. (2007) Stochastic Processes in Physics and Chemistry, North Holland, Third Edition. Werhli, A.V., Grzegorczyk, M., Husmeier, D. (2006) Comparative evaluation of reverse engineering gene regulatory networks with relevance networks, graphical gaussian models and bayesian networks, Bioinformatics, 22(20), 2523-31. Wilkinson, D.J. (2006) Stochastic Modelling for Systems Biology, Mathematical and Com- putational Biology, Chapman and Hall/CRC , ISBN: 1-58488-540-8. Wilkinson, D.J. (2009) Stochastic modelling for quantitative description of heterogeneous biological systems, Nature Reviews Genetics, 10(2), 122- 33. Xu, T.R., Vyshemirsky V., Gormand A., et al. (2010) Inferring signaling pathway topologies from multiple perturbation measurements of specific biochemical species, Science Signaling, 3(113), ra20. Yarden, Y., Sliwkowski, M.X. (2001) Untangling the ErbB signalling network, Nature Reviews Molecular Cell Biology, 2, 127-37. Zellner, A. (1986) On Assessing Prior Distributions and Bayesian Regression Analysis With g-Prior Distributions, Bayesian Inference and Decision Techniques - Essays in Honor of Bruno de Finetti, eds. P. K. Goel and A. Zellner, 233-24. Zou, C., Feng, J. (2009) Granger causality vs. dynamic Bayesian network inference: a comparative study, BMC Bioinformatics, 10(12).
URI:	http://wrap.warwick.ac.uk/id/eprint/40487

Request changes to a record

Actions (login required)

View Item