User:Nuri Purswani/Network/References

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  • F. Alche-Buc, M. Quach, N. Brunel. Estimating parameters and hidden variables in non-linear state-space models based on ODEs for biological network inference. Vol 23 23:3209-3216 (2007)
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  • Beal M.J., Li, J. Ghahramani Z. and Wild, D.L. Reconstructing Transcriptional Networks using Gene Expression Profiling and Bayesian State Space Models. In Introduction to Systems Biology (Ed: Choi, S.), Humana Press, Totowa (2007), pp 217-241
  • Beal, M.J.,Angus, J., Li, J., Rangel, C., Wild, D.L. Inferring Transcriptional Networks Using Prior Biological Knowledge and Constrained State-Space Models. In Lawrence, N.D., Girolami, M., Rattray, M. and Sanguinetti, G. (Eds.), Learning and Inference in Computational Systems Biology, MIT Press, Cambridge, (2010), pp 117-152.
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  • Y. Yuan, G.-B. Stan, Sean Warnick, J. Gonçalves. Minimal dynamical structure realisations with application to network reconstruction from data, Proceedings of the 48th IEEE Conference on Decision and Control (CDC 2009), Shangai, China, 16-18 December, 2009
  • J. Goncalves and S. Warnick. Systems Theoretic Approaches to Network Reconstruction, Control Theory and Systems Biology, MIT Press, Eds. B. Ingalls and P. Iglesias, 2009.
  • J. Goncalves and S. Warnick. Necessary and Sufficient Conditions for Dynamical Structure Reconstruction of LTI Networks, IEEE Transactions on Automatic Control, vol.53 (7), August 2008.
  • J. Gonçalves, G.-B. Stan, S. Warnick. Dynamical structure analysis of sparsity and minimality heuristics for reconstruction of biochemical networks, R. Howes, L. Eccleston, Proceedings of the 47th IEEE Conference on Decision and Control (CDC 2008), Cancun, Mexico, 9-11 December, 2008.
  • C. Koh, F.X. Wu, G. Selvaraj, A. Kusalik. Using a State-Space Model and Location Analysis to Infer Time-Delayed Regulatory Networks. EURASIP Journal on Bioinformatics and Systems Biology. Article ID 484601 (2009)
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  • A.A. Markov. "Extension of the limit theorems of probability theory to a sum of variables connected in a chain". reprinted in Appendix B of: R. Howard. Dynamic Probabilistic Systems, volume 1: Markov Chains. John Wiley and Sons, 1971
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  • Porter et. al. Chemotaxis in Rhodobacter sphaeroides Requires an Atypical Histidine Protein Kinase* Received for publication, August 3, 2004, and in revised form, September 20, 2004 Published, JBC Papers in Press, October 12, 2004, DOI 10.1074/jbc.M408855200
  • C.Rangel, J. Angus, Z. Ghahramani, and D. Wild. "Modeling biological responses using gene expression profiling and state space models," Probabilistic Modelling in Medical Informatics and Bioinformatics, D. Husmeier, S. Roberts, and R. Dybowski, editors, Springer Verlag, 2005.
  • C. Rangel, J. Angus, F. Falciani, Z Ghahramani, M. Lioumi, and D. Wild. "Modeling T-cell activation using gene expression profiling and state space models," Bioinformatics. 2004 Jun 12; 20(9):1362-72. Epub 2004 Feb.12.
  • E. Sontag, A. Kiyatkin, B. Kholodenko, ”Inferring dynamic architecture of cellular networks using time series of gene expression, protein and metabolite data,” IEEE Bioinformatics, Vol. 20, Issue 12, pp. 1877- 1886, 2004.
  • E.D. Sontag, M. Andrec, B.N. Kholodenko, R.M. Levy, and. Inference of signaling and gene regulatory networks by steady-state perturbation experiments: structure and accuracy. J. Theoret. Biol., 232(3):427-441, 2005.
  • M. Secrier*, T. Toni*, M.P.H. Stumpf, The ABC of reverse engineering of biological signalling systems, Mol.Biosyst. 5:1925-1935 (2009) (website)
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  • T. Toni, D. Welch, N. Strelkowa, A. Ipsen, M.P.H. Stumpf, Approximate Bayesian Computation scheme for parameter inference and model selection in dynamical systems, J.Roy. Soc. Interface 6, 187-202 (2009).
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  • Fang-Xiang Wu, WJ Zhang, and Anthony J Kusalik. Determination of the minimum number of microarray experiments for discovery of gene expression patterns. BMC Bioinformatics 2006, 7(Suppl 4):S13doi:10.1186/1471-2105-7-S4-S13
  • Zak DE, Gonye GE, Schwaber JS, Doyle FJ, 3rd. Importance of input perturbations and stochastic gene exprsesion in the reverse engineering of genetic regulatory networks: insights from an identifiability analysis of an in silico network. Genome Res (2003);13:2396-2405

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