Computational Biology Research Paper

Computational Biology Research Paper-43
However, classical approaches focusing on genotype-phenotype correlation often fail in identifying significant associations between genotype and phenotype.Variation in the DNA sequence can perturb a complex web of interactions among a number of biological molecules, and that change of the system can lead to phenotypic variation.

However, classical approaches focusing on genotype-phenotype correlation often fail in identifying significant associations between genotype and phenotype.Variation in the DNA sequence can perturb a complex web of interactions among a number of biological molecules, and that change of the system can lead to phenotypic variation.

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This ongoing research project is developing and applying new computational tools for discovery of non-coding RNA.Professors Lee, Ruzzo, Seelig, and Tompa, together with their students, collaborate with molecular biologists on a wide range of computational problems related to the analysis of biological sequences.Specific projects include the discovery of regulatory motifs in nucleotide and protein sequences, the inference of regulatory relationships among genes, comparative sequence analysis, the discovery of non-coding RNA, identifying the genetic basis of complex traits, and synthetic biology.We have developed algorithms that search for statistically overrepresented motifs in such a collection of regulatory regions, these motifs being good candidate binding sites.An orthogonal approach deduces binding sites by considering orthologous regulatory regions of a single gene from multiple species.Computational Molecular Biology Overview Molecular Biology has become an information science with close ties to Computer Science.Large databases and sophisticated algorithms have become essential tools for biologists seeking to understand complex biological systems, determine the functions of nucleotide and protein sequences, or reconstruct the course of evolution.The complexity of these cellular mechanisms induced by sequence variations, together with environmental factors, makes it difficult to infer the causal relationship between genotype and phenotype.One way to resolve this challenge is to utilize high-throughput functional information from individuals, such as RNA expression measurements, quantitative protein profile, metabolite levels and so on.Programmed molecular self-assembly will be used for the massively parallel construction of nanoscale devices."Smart drugs" that target drug activity to disease cells and activate in response to specific molecular clues will have minimal side effects and improve therapeutic outcomes.

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