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Pal Research Group:
A recent article on IEEE Signal Processing Magazine provides a concise overview of our research on robustness in genetic regulatory network modeling and control (pdf).
Genomic Signal Processing and Control of Cancerous Cells
Individualized cancer treatment that targets the specific malfunctioning genetic pathway of the patient holds promise to drastically increase the success rate of cancer therapy. Our goal is to build predictive models of the genetic regulatory networks and control them to achieve a desirable state.
Some specific research topics:
- Target Inhibition Map approach to inference of cancer pathways
- Studying the effect of coarse-scale modeling
Representative Publication: PSB 2012: 351-62, PMID 22174290
Download Software : (Developed by Noah Berlow)
GUI for generation of Probabilistic Target Inhibition Maps from targeted drug perturbation data
Collaborative Effort with Dr. Charles Keller at Oregon Health & Science University, Keller Lab
Recent collaboration with Dr. Keller resulted in a Cancer Cell publication Vol. 19 (2), Pg. 177-91, 2011
Representative Publications: IEEE TSP, Vol. 58, Pg. 3341 - 3351, No.6, 2010 . IEEE SPM Vol. 29, No. 1, Pg. 66-76, 2012
- Inference of Gene Regulatory Networks from limited time series measurements
- Quantification of the design errors
- Optimal control policy design
- Robustness of the intervention strategies in presence of noise
- Integration of data from diverse technologies measuring gene, protein and metabolite expressions into the modeling framework
- Validation using canine tumors, human cancer cell lines and mice models
Pattern Analysis of fMRI data
A primary goal of the analysis of fMRI data is to locate areas of brain activation that can differentiate various cognitive and sensory tasks. We are using advanced multivariate tools for differentiating cognitive tasks based on their BOLD activation patterns.
Funding 