1. Abstract Our research mainly applies Maximum Likelihood Method (MLE), Dynamic Programming, and Neighbor Joining Method in an attempt of shortening the biomedical research and development time of antibody. Through sequence comparison, we accelerate to locate effective section of monoclonal virus sequence. By doing so, scientists could improve the probability of antibody preparation and reduce blind tests. Meanwhile, by analyzing relationship of found flu virus through genetic sequence comparison, we could design vaccine of unfamiliar virus. Our assumption is proved by the successful result of influenza virus sequence computation. Therefore, our method could be applied for accelerating the locating of best biological sequence in antibody preparation. Introduction Constant mutation of viruses result in new combination of genetic sequence. This is how a new virus is born. 1 According to “Coevolution Hypothesis,” viruses may have evolved from complex molecules of protein and nucleic acid at the same time as cells first appeared on Earth and would have been dependent on cellular life for billions of years. 2,3 Viruses might be produced through one or more mechanism in different time periods 4. To date, molecular biotechnology is an effective way to search for the origin of virus. 3 This technology requires DNA or RNA samples of ancient viruses. However, the oldest virus sample in the laboratory is only around 100 years old. 2,3 For analyzing massive genetic or protein sequences, scientists mostly utilize computers to sort out DNA sequences of virus and host in order to clarify evolution of different viruses. However, there is not much discussion regarding how to utilize computers in biochemical experiments. Rapidly and correctly locate a monoclonal section of bio sequence is a completely ran dom but essential step of vaccine research and development. 5 This project is to test the feasibility of our method through genetic sequence of Influenza A virus. With current bio computation methods, we wish to reduce the random section of antibody preparation. We attempt to complete the search of monoclonal sequence with the least experiments, so that we could further improve the R&D process of antibody preparation. Figure 1. Conventional R&D process of antibody 5 Figure 2. R&D process through methods proposed in our research

2. 1. The result of calculating the similarity distance matrix of mRNA of 14 influenza viruses are as Figure 4 and 5. Evolutional similarity between H7N3 and H5N3 is greater than that among H7N3, H7N1 and H7N2. When initiating new medicine for H7N3, if we apply for the known result of H5N3 vaccine as possible section of H7N3 vaccine section, the R&D process of new vaccine will be accelerated. 2. The MLE probability model analysis also revealed that H7N9 influenza virus has a closer relationship to H7N7. Anticipated with the result of rootless tree, antibody of H7N7 could be used for accelerated development of H7N9 antibody; during emergency, when no any other treatment or alternative method can be performed, the treatment for H7N7 could be used as an alternative treatment. Although we don’t have any existing H7N7 antibody, medical personnel may also look downward to locate a closer biological sequence option. In this case, H7N2 may be used to replace H7N7, then we may conduct a all sequence analysis with Needleman-Wunch Dynamic Programming as the initial sequence of R&D of new medicine. With this procedure, we may find the monoclonal and variability sections similar to H7N9 and H7N3 vaccines. Conclusion 1. We proposed an improved process of R&D and preparation of antibody (Figure 2) 2. Through the sequence comparison calculations, we speed up to identify viral sequences specific section to reduce the blind test experiment. 3.By influenza A subtype viruses are known gene sequence analyze the evolution of the relationship between unknown influenza A subtype virus to design unknown influenza A subtype virus vaccine. References 1. Leppard, Keith, D. Nigel, Easton, Andrew, Introduction to Modern Virology. Blackwell Publishing Limited, 2007. 2. Shors, Teri. Understanding Viruses., Jones and Bartlett Publishers., 2008. 3. Y. Liu, D. C. Nickle, D. Shriner, et al., Molecular clock-like evolution of human immunodeficiency virus type 1, Virology. 10;329(1):101–8, 2004. 4. Dimmock, N. J., Easton, J. Andrew, Leppard, Keith, Introduction to Modern Virology sixth edition, Blackwell Publishing, 2007. 5. Juang RH, Wu YJ. Proteomics and monoclonal antibody applications. Proteomics, Retrieved from the National Taiwan University College of Medicine Biochemistry and Institute of meristem, http://juang.bst.ntu.edu.tw/Protein/ proteomics&mab.htm. 2003. Figure 5. Evolution tree of influenza virus genetic sequence from MLE-rootless tree Figure 4. Evolution tree of influenza virus genetic sequence from MLE-Phylogenetic tree. Acknowledgments We wish to express their gratitude to Prof. Chou Kuan-Chi and Dr. Wang Shun-Te for critical discussion of the experimental protocols. This work was supported by grants from the National Science Council, Taiwan (NSC 101-2514-S-796-001).