Computational Biology
Our goal is to develop and apply appropriate
mathematical and statistical methods for increasingly massive amount of data being collected either in images or time series for molecular processes and
cellular systems. The current challenges lie not just in the development of theoretical framework for high-dimensional data or gigabyte-scale computations, but also in the experimental design and quality control of the measurement process to insure sufficient and reliable data for a given task.
In recent years we have been in active development of quality control methodology for multiplexed assays and
high-throughput mass spectrometers.
We are currently also interested in developing quantitative biological measurement methods for multimodal cellular imaging platforms such as statistical background correction and quantiative cell characteristic measurements from microscopic images.
Useful Resources
- Cellular Imaging and Cell Biology
- Software
Related Work and Publications:
- K. M. Mullen, M. Halter, Z.Q. J. Lu and N. Dodder (2009).
R package: cellVolumeDist for Cell Volume Distributions Reveal Cell Growth Rates and Division Times, J. of Theor. Biology. Mar 7, 2009;257(1):124-30.
- M. B. Satterfield, K.Lippa, Z.Q. Lu, M.L. Salit (2008).
Microarray Scanner Performance Over a Five-Week Period as Measured With Cy5 and Cy3 Serial Dilution Slides. Journal of Research of the National Institute of Standards and Technology, Volume 113, Number 3, May-June 2008.
- Z.Q. John Lu (2008).
SVD-based functional ANOVA for measurement evaluation of MALDI-TOF mass spectrometry of polymers, Proceedings of the
36th Symposium on
the Interface, Computing Science and Statistics (2004), Volume 36, Computational Biology and Informatics.
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