1. A New Oklahoma Bioinformatics Company

2. Microarray and Bioinformatics

3. Bioinformatics Programs Graphics Database Management & Sequence Data Statistical Analysis Ease of Use

4. Bioinformatics Programs

5. The Technology Core technology was developed at the University of Oklahoma. Scientists from microbiology, biochemistry, computer science, molecular biology, mathematics, and bio-informatics took part in the development. Development took over two years of work and experimentation.

6. Company Structure Microarray Data Management, LLC is a joint venture between the company and the University of Oklahoma. The company is the first Bioinformatics joint venture enterered into by the University of Oklahoma. The company has an exclusive license from the University of Oklahoma for commercial development of the technology.

7. Technology The core technology is a stand alone software program bringing together all elements necessary to manage, evaluate, calculate, and graphically represent data from microarray data sets The technology is highly intitutive, and allows for seamless real time interaction with microarray experimental data and existing data bases.

8. Technology (continued) No other existing Bioinformatics software offers the power of the technology in a stand alone package. The technology uses a unique set of mathematical and computer algorithms to enable processing, data management, and visualation power. Initial patents have been filed.

9. Technology Applications Microarrays are Viewed to the future of medical diagnostics and biopharmaceutical therapeutic intervention Microarrays are cutting edge technology, evolving with the expansion of genetic research. Biotechnology companies, can significantly reduce research time leading to drug discovery using microarrays and the companies software saving potentially millions of dollars in costs. Microarrays are the only technology that can be used to study genes and gene fragments and their interaction with other genes and other substances. Microarrays can evaluate up to 50,000 genes or gene sequences one time

10. Technology Applications (cont) Genomics Proteonomics Pharmacogenomics Bioinformatics Oncogenomics Plant Genomics Medical Diagnostic Genomics Medical Therapeutic Genomics and Proteonomics

11. Unique Properties of the Technology Creation and maintenance of custom data bases based on individual gene responses, generated by microarrays. Analysis of sequence information by gene response. Very important in cancer chemotherapy. Currently not available. Expression Analysis using data mining and statistical approaches to understand gene function and expression patterns.

12. The Market for the Technology The current market in 2004 for microarray technologies is estimated to exceed 1 billion dollars. The market for microarray bioinformatics (a subset of the microarray total market) in 2004 is estimated at about 50 million dollars. The bioinformatics market is expected to grow to exceed 500 million dollars as the microarray market expands to 5 billion dollars in 2010.

13. The Market (continued) Dramatic market expansion of cancer therapeutic Micro- arrays to measure and evaluate effectiveness of chemotherapy. Integration of microarrays into genomic diagnostics Significant utilization of microarray technologies in diabetes, Alzheimer's, cardiovascular, stroke, AIDS, Parkinsonism, autism, and anemia Drug Discovery; Bio-pharmaceutical companies will utilize microarrays to discover novel genes and the proteins that are produced and control their expression or repression.

14. Tactical Strategy Continue to pursue additional patents, layering onto the core technology Begin beta site testing of technology for oncogenomic, proteonomic, and pharmacogenomic applications Begin commercial discussions with software, data base management, and hardware companies engaged in selling high end scientific servers; microarrays are a very data intensive technology, and require a large amount of computing power.

15. Summary Our technology is novel and fills an existing void in Bioinformatics software. It is a totally integrated package designed to meet the needs for Microarray data management. The microarray market is a very large and important technology. Most genetic research will utilize microarrays and will need Bioinformatics software. Our technology is enabling and will allow for the rapid expansion of microarray utilization. Our technology will be an important aspect of developing treatments and diagnostics for cancer and other serious and prevalent diseases.

16. Definitions Microarray– An array of DNA or protein samples that can be hybridized with probes to study patterns of gene expression. Bioinformatics– The study of the basic structure of biological information and biological systems. It brings together biological data handling with the analytic theory and practical tools of mathematics and computer science.

17. Microarray Process A typical DNA micro array experiment is as follows: Take a small slide. Divide the slide into a series of square cells to form a rectangular grid. Onto each square cell, stick a tiny amount of liquid that contains DNA corresponding to a gene of known sequence. Different cells will have different genes. Separately prepare a solution that contains a mixture of mRNAs whose sequences are unknown. Add solution a substance that fluoresces when excited by light. Pour the solution onto slide. The mRNA molecules will diffuse over the slide and, wherever they find a matching (i.e. complementary) DNA sequence, such as the one taken from the gene from which the mRNA was transcribed, they will hybridize to each other and the solution will stick to the slide. Without a match the solution will not stick to the slide and can be washed away. Use a laser scanner to detect and measure the florescent signal being emitted at each cell. In a comparative micro array experiment, different slides containing the same set of genes will be exposed to different mRNA samples. By comparing the intensity levels of the florescent signals across the multiple mRNA samples, a scientist will be able to understand how the expression profile of a set of genes differs across the different mRNA samples.