omation mbers - Society for Laboratory Automation and Screening

5:00 pm Wednesday, February 4 High Throughput Screening – Data Analysis and QC Room A2
John Elling
Datect, LLC
2935 Rodeo Park Drive East
Santa Fe, New Mexico 87505
elling@datect.com
Finding and Correcting Systematic Bias in Array Experiments
66
Co-Author(s)
Brian P. Kelley
Whitehead Institute
Before the results of array experiments can be analyzed, the measurements need to be validated, normalized,
and possibly modified and corrected. The Whitehead Institute has shown that frequency analysis can be used to
detect nonrandom spatial correlations in arrays of data that is expected to be random. This technology can be
used to find systematic errors in data from microtiter plates and microarrays that may otherwise be overlooked
when their patterns are obscured by the experimental signal or random noise. When a systematic error occurs
in arrays that may also have legitimate correlated experimental responses, the spatial correlations are overlaid.
Similarly, the effect of multiple systematic errors will be superimposed to create the observed spatial correlation.
We have developed techniques to detect and discriminate multiple systematic biases that occur together in array
data. With this capability, the software can be set up to ignore the acceptable correlations in the data while alerting
the scientist to the appearance of unexpected and potentially problematic correlations. Identifying the source of
a systematic spatial error can be used by scientists to both debug the experiment and to select an approach to
normalizing the bias to correct the error.
8:00 am Thursday, February 5 High Throughput Screening – Automated Design Room A2
Larry DeLucas
University of Alabama at Birmingham
CBSE 206, 1530 3rd Avenue South
Birmingham, Alabama 35294-4400
delucas@cbse.uab.edu
Efficient Protein Crystallization
Co-Author(s)
Terry Bray, Lisa Nagy, Debbie McCombs
University of Alabama at Birmingham
David Hamrick, Larry Cosenza,
Diversified Scientific, Inc.
Alexander Belgoviskiy, Brad Stoops, Arnon Chait
ANALIZA, Inc.
The high throughput production of diffraction-quality crystals remains a major obstacle in structural proteomics,
with success rates rarely exceeding 15% for soluble proteins. The Center for Biophysical Sciences and
Engineering, Diversified Scientific, Inc., and ANALIZA, Inc. present a unique and powerful approach for rapidly
and efficiently determining optimum protein crystallization conditions. This involves the combination of three
key technologies: (1) automated nano-crystallization, (2) incomplete factorial screening, and (3) a specifically
designed neural net crystallization prediction program. This approach demonstrates promise for optimizing protein
crystallization when combined with balanced sampling of crystallization space. This is accomplished using an
incomplete factorial screen and a uniquely designed nano-crystallization system. Every crystallization trial outcome,
including failures, is used to train the neural network. The self-organizing and predictive nature of the neural
network allows for accurate prediction of previously untested crystallization conditions, even in the presence of
noise. If properly trained, the neural network can be used to recognize important patterns of crystallization. This
information allows the neural net to predict non-sampled complete factorial conditions used for optimization. Thus,
it predicts the conditions that produce crystals from the entire “crystallization space” of possible experimental
conditions, based upon results from a much smaller number of actual experiments performed. Therefore, a virtual
screen can be performed using all possible combinations of components and variables. The top 50 scores for the
predictive crystallization conditions are then experimentally prepared to determine/verify optimum crystallization
conditions. Results from a statistically relevant protein sample number will be presented.