Integration Between Experimental and Computational Biology for Studying Protein Function
Giselle M. Knudsen
At PSB 2005, we discussed the need for more efficient integration between computational and
experimental biochemistry approaches to solving molecular and systems biology questions. In
this tutorial we will discuss how bioinformatics can be coupled with experiment in an
iterative approach. This is accomplished through the development of balanced, testable
hypotheses based on reliable experimental data, and with testing in relevant experimental systems.
Department of Medicinal Chemistry and Molecular Pharmacology, Purdue University
Reza A. Ghiladi
Department of Pharmaceutical Chemistry, University of California San Francisco
D. Rey Banatao
Department of Chemistry and Biochemistry, University of California Los Angeles
It is extremely important to understand the nature of experimental parameters, and how
they affect data quality. These parameters include the time regime of the chemical or
biological phenomenon, and the sensitivity of any given technique for detecting a specific
event,Ê as well as the measurable rates that can be observed by this technique. Biology is
dynamic, and we must consider what window of our dynamic system is being observed. Within
this context we will discuss several modern technologies including the "omics" technologies
like proteomics, genomics, metabolomics, etc., genetic methods like yeast-two-hybrid, chemical
biology methods such as cross-linking, or the use of other chemical affinity probes, and
spectrophotometric methods including fluorescence, FRET, UV-Visible kinetics assays, microscopy,
as well as some classical structural methods such as NMR and X-ray crystallography.
With each new experimental method, we need to consider system dynamics along with studies of
the individual components of these systems. These dynamic and component arguments will then
serve as our "believability" guidelines for interpretation of complex datasets. Questions
that should be asked are: How was the biological system sampled, and how representative of
the system is it? How do outliers in the dataset relate to the true positives or negatives?
How can experimental intuition be encoded by a computational model? With these computational and
experimental parameters, we will be able to address many diverse subjects, from proteomic/genomic
analysis, structure-function interpretation, cell biology and visualization of signaling pathways,
to drug pharmacology.