Born in Mexico City, Mexico. BS in Mathematics from the Autonomous Metropolitan University. MS in Mathematics and Ph.D in Mathematics from Claremont Graduate University. Postdoctoral fellow at the Computational Molecular Biology and Bioinformatics of the University of Southern California 2003-2006.
Research Associate at the National Institute of Genomic Medicine 2006 to date. Adjunct Faculty at the School of Mathematical Sciences - Claremont Graduate University 2003- present. I taught for 3 years while I was a postdoctoral fellow at USC. Then from Mexico I have been teaching from outside the US as part of the distance learning initiative. Fall 2008 will be my 6th year teaching and the 3rd one online.

Long Biography

RESEARCH INTERESTS

• Statistical Applications in Genetics and Molecular Biology
• Computational Statistics
• Microarray data analysis: cDNA microarrays, Affymetrix arrays
- Background correction methods,
- normalization approaches
• Probabilistic modeling of genetic regulatory networks,
• State-Space modeling of time series,
• Computer intensive methods in statistics and probability.

PUBLICATIONS

Rangel, C., Wild, D. L. Falciani, F., Ghahramani, Z., and Gaiba, A. (2001) “Modeling biological responses using gene expression profiling and linear dynamical systems.” Proceedings of the 2nd International Conference on Systems Biology. Madison, WI: OmniPress, pp 248-256.

Dubey, A., Hwang, S., Rangel, C., Rasmussen, C.E., Ghahramani, Z. and Wild, D.L. “Clustering protein sequence and structure space with infinite Gaussian mixture models.” Pacific Symposium in Biocomputing 2004. Ed. R.B. Altman, A.K. Dunker, L. Hunter and T.E. Klein. World Scientific Publishing, Singapore, 399-410 (2004).

Rangel, C., Angus, J., Ghahramani, Z., Lioumi, M., Sotheran, E., A., Gaiba, A.,.Wild, D.L. and Falciani, F. “Modeling T-cell activation using gene expression profiling and state space models.” Bioinformatics (2004), 20(9):1361-1372.

Beal, M.J., Falciani, F., Ghahramani, Z., Rangel C. and Wild, D.L. “A Bayesian approach to reconstructing genetic regulatory networks with hidden factors.” Bioinformatics, 21: 349-356 (2005).

Claremont Graduate University Mathematics Clinic Reports

"Methods and Monte Carlo Algorithms for Geometric Convergence," Okten G., Park Jeho, Rangel C., Claremont Research Institute of Applied Mathematical Sciences (CRIAMS) Technical Report LANL-01001 Chapter 5, Los Alamos National Laboratory, January 2001

"Digital Filter Design," Cumberbatch E. Bhan A., Rangel C.- Claremont Graduate University Mathematics Clinic, Momentum Data Systems, Technical Report, June 2000.

"Enhancement to the Site Availability Model (SAM) for Satellite Navigation System Availability Modeling," Angus J., Lee S., Rangel C. and Mukhopadhyay S. - Claremont Graduate University Mathematics Clinic Reports Hughes / Raytheon Systems Company, Fall 97 - Spring 98

BOOK CHAPTERS

"Inferring Transcriptional Networks using Prior Biological Knowledge and Constrained State Space Models" Wild D., Angus J.E., Beal M., Li J., Rangel C. chapter in Learning and Inference in Computational Systems Biology by Neil D. Lawrence, Mark Girolami, Magnus Rattray and Guido Sanguinetti. The MIT Press Cambridge, Massachusetts London, England © 2009 Massachusetts Institute of Technology

"Modeling genetic regulatory networks using gene expression profiling and state space models," C. Rangel, J. Angus, Z. Ghahramani, and D. Wild, chapter in Applications of Probabilistic Modeling in Medical Informatics and Bioinformatics, D. Husmeier, S. Roberts, and R. Dybowski, editors, Springer Verlag, 2005.

INVITED TALKS

Matilde Representación de Matemáticas Aplicadas – XVI Semana de Matemáticas Aplicadas; Instituto Tecnológico Autónomo de México; Septiembre 2006. “Aplicaciones Matemáticas en la Medicna Genómica”

Seminar for Statistics - ETH Federal Institute of Technology; Zurich, Switzerland; April 21, 2005. “Applicability of Linear Dynamical Systems to Genetic Regulatory Network Inference”

Biomedical Engineering, USC Viterbi School of Engineering, February 28, 2005.
"Using microarray gene expression data to infer genetic regulatory networks: a Linear Dynamical Systems Approach”

Complex Stochastic Systems in Biology and Medicine workshop; Munich, Germany October 7-8, 2004. “Linear Dynamical Systems Modeling of Genetic Regulatory Networks.”

Retreat of the Joint Ph.D. Program in Computational Science Claremont Graduate University and San Diego State University, Temecula CA. November 2002. “Some Computational Aspects of Linear Dynamical Systems in their Use in Modeling Microarray Gene Expression Data,”

Gene Regulatory Network Workshop, Keck Graduate Institute, Claremont CA. June 2002. “Modeling Biological Responses using Gene Expression Profiling and Linear Dynamical Systems.”

History

Member for: 17 weeks 1 day