In biological systems there are lots of communities with many proteins involved to form complexes.
We can use this tool to identify structures embedded in the data., Zhang said. Weve identified the substructures of three different RNA polymerase complexes from noisy data, for instance, which are crucial for gene transcription.
Zhang began his computer science career as a specialist in artificial intelligence, but in recent years he has expanded to bring his skills to computational biology. His main interest and ambition are to use computational means to solve some basic biology problems and problems related to human diseases. For example, his group studied a basic problem of the transcription mechanism of microRNAs, which are small noncoding regulatory RNAs that regulate the development and stress responses of nearly all eukaryotic species that have been studied. Using machine learning techniques, Zhang and his collaborators showed that almost all intergenic microRNA genes in four model species, human, mouse, rice and Arabidopsis, are transcribed by RNA polymerase II, which transcribes protein-coding genes. The results were published in PLoS Computational Biology, 3(3):e37 (2007).
Multidisciplinary research that combines computational approaches with biological data is a hallmark of research themes in Zhang's group. As another example, in a paper published in Genome Biology, 7(6):R49 (2006), Zhang and his Ph.D. student, Guandong Wang, developed an algorithm, called WordSpy, for identifying cis-regulatory elements short DNA sequences that are critical for the regulation of gene expression from a large amount of genome sequences.
Stealth from the ancient Greeks
|Contact: Wexiong Zhang|
Washington University in St. Louis