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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 and 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:
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 using computational data integration" (submitted).
H. Lähdesmäki, A. G. Rust, I. Shmulevich, "Probabilistic inference of transcription factor binding from multiple data sources" (submitted).
H. Lähdesmäki, I. Shmulevich, “Learning the Structure of Dynamic Bayesian Networks from Time Series and Steady State Measurements," (submitted).
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:
Probabilistic Boolean Networks
ProbTF
MotifMogul WebServer | Mogul Project
Dizzy: a chemical kinetics stochastic simulation software package written in Java
BioTapestry: network visualization, documentation, and dissemination
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