Complex dynamical biomolecular systems govern virtually all biological processes, on time scales ranging from development to physiology. A paramount problem is to understand the structural and dynamical properties of such systems and their role in cellular function and dysfunction. Our group is developing model inference approaches by integrating the information from multiple types of measurement data in a variety of modeling formalisms, including differential equations, nonlinear discrete dynamical systems, such as probabilistic Boolean networks, dynamic Bayesian networks, and others. Various data sources such as DNA sequences, measurements of gene expression (microarrays), protein expression (protein arrays, mass spectrometry), genome-wide protein-DNA interactions (ChIP-chip), functional annotations, and literature-based relationships, can be combined in statistically principled ways. The inferred models can then be used to predict various aspects of system behavior under environmental or genetic perturbations. In particular, the predictive nature of such models sets the stage for optimal intervention strategies intended to control system behavior, particularly in the context of disease. Publications: M. Heinäniemi, M. Nykter, R. Kramer, A. Wienecke-Baldacchino, L. Sinkkonen, J. X. Zhou, R. Kreisberg, S. A. Kauffman, S. Huang, I. Shmulevich, “Gene-pair expression signatures reveal lineage control,” Nature Methods (April 21), 2013. A. Larjo, I. Shmulevich, H. Lähdesmäki, "Structure learning for Bayesian networks as models of biological networks," Methods in Molecular Biology, Vol. 939, pp. 35-45, 2013. S. A. Ramsey, T. A. Knijnenburg, K. A. Kennedy, D. E. Zak, M. Gilchrist, E. S. Gold, C. D. Johnson, A. Lampano, V. Litvak, G. Navarro, T. Stolyar, A. Aderem, I. Shmulevich, "Genome-wide histone acetylation data improve prediction of mammalian transcription factor binding sites," Bioinformatics, Vol. 26, No. 17, pp. 2071-2075, 2010. V. Litvak, S. Ramsey, A. Rust, D. Zak, K. Kennedy, A. Lampano, M. Nykter, I. Shmulevich, A. Aderem, "Function of C/EBPdelta in a regulatory circuit that discriminates between transient and persistent TLR4-induced signals ," Nature Immunology, Vol. 10, No. 4, pp. 437-43, 2009. M. Nykter, H. Lähdesmäki, A. Rust, V. Thorsson, I.
Shmulevich, "A data integration framework for
prediction of transcription factor targets: a BCL6 case study," Annals
of the New York Academy of Sciences, Vol. 1158, pp. 205-214, 2009. I. Shmulevich, J. D. Aitchison, "Deterministic and stochastic models of genetic regulatory networks," Methods in Enzymology, Vol. 467, pp. 335-356, 2009. A. C. Huang, L. Hu, S.A. Kauffman, W. Zhang, I. Shmulevich, "Using Cell Fate Attractors to Uncover Transcriptional Regulation of HL60 Neutrophil Differentiation," BMC Systems Biology, Vol. 3, No. 20, 2009. W. Liu, H. Lähdesmäki, E. R. Dougherty, I. Shmulevich, "Inference of Boolean Networks using Sensitivity Regularization," EURASIP Journal on Bioinformatics and Systems Biology, Vol. 2008, Article ID 780541, 12 pages, 2008. S. A. Ramsey, S. L. Klemm, D. E. Zak, K. A. Kennedy, V. Thorsson, B. Li, M. Gilchrist, E. Gold, C. D. Johnson, V. Litvak, G. Navarro, J. C. Roach, C. M. Rosenberger, A. G. Rust, N. Yudkovsky, A. Aderem, I. Shmulevich, "Uncovering a macrophage transcriptional program by integrating evidence from motif scanning and expression dynamics," PLoS Computational Biology, Vol. 4, No. 3, e1000021, 2008. H. Lähdesmäki, A. G. Rust, I. Shmulevich, "Probabilistic inference of transcription factor binding from multiple data sources," PLoS ONE, Vol. 3, No. 3, e1820, 2008. H. Lähdesmäki, I. Shmulevich, “Learning the Structure of Dynamic Bayesian Networks from Time Series and Steady State Measurements," Machine Learning, Vol. 71, pp.185-217, 2008. N. D. Price, I. Shmulevich, "Biochemical and statistical network models for systems biology," Current Opinion in Biotechnology, Vol. 18, pp. 365-370, 2007. H. Lähdesmäki, S. Hautaniemi, I. Shmulevich, O. Yli-Harja, "Relationships Between Probabilistic Boolean Networks and Dynamic Bayesian Networks as Models of Gene Regulatory Networks" Signal Processing, Vol. 86, No. 4, pp. 814-834, 2006. M. Brun, E. R. Dougherty, I. Shmulevich, “Steady-State Probabilities for Attractors in Probabilistic Boolean Networks,” Signal Processing, Vol. 85, No. 4, pp. 1993-2013, 2005. R. F. Hashimoto, S. Kim, I. Shmulevich, W. Zhang, M. L. Bittner, E. R. Dougherty, "Growing genetic regulatory networks from seed genes," Bioinformatics, Vol. 20, No. 8, pp. 1241-1247, 2004. I. Shmulevich, I. Gluhovsky, R. Hashimoto, E. R. Dougherty, and W. Zhang, "Steady-State Analysis of Genetic Regulatory Networks Modeled by Probabilistic Boolean Networks," Comparative and Functional Genomics, Vol. 4, No. 6, pp. 601-608, 2003. E. R. Dougherty and I. Shmulevich, "Mappings Between Probabilistic Boolean Networks," Signal Processing, Vol. 83, No. 4, pp. 799-809, 2003. H. Lähdesmäki, I. Shmulevich, and O. Yli-Harja, "On Learning Gene Regulatory Networks Under the Boolean Network Model," Machine Learning, Vol. 52, pp. 147-167, 2003. I. Shmulevich, E.R. Dougherty, and W. Zhang, "From Boolean to probabilistic Boolean networks as models of genetic regulatory networks,'' Proceedings of the IEEE, Vol. 90, No. 11, pp. 1778-1792, 2002. I. Shmulevich, E. R. Dougherty, W. Zhang, "Gene Perturbation and Intervention in Probabilistic Boolean Networks," Bioinformatics, Vol. 18, No. 10, pp. 1319-1331, 2002. I. Shmulevich, E.R. Dougherty, and W. Zhang, "Control of stationary behavior in Probabilistic Boolean Networks by means of structural intervention," Journal of Biological Systems, Vol. 10, No. 4, pp. 431-445, 2002. I. Shmulevich, E. R. Dougherty, S. Kim, W. Zhang, "Probabilistic Boolean Networks: A Rule-based Uncertainty Model for Gene Regulatory Networks," Bioinformatics, Vol. 18, No. 2, pp. 261-274, 2002. Links: |
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