Experimental biologists are generating data at an unprecedented rate. Thanks to recent advances in DNA sequencing technology, the quantity of genome sequence data stored in online databases more than doubles every year. In addition, millions of gene expression profiles are freely available to download at the click of a button, and this number continues to grow. At the other end of the biological scale, single-cell and single-molecule experimental techniques have enabled the quantitative measurement of biological function at smaller and faster scales, and these datasets are becoming more readily available.
Unfortunately, biological insight has not kept pace with this deluge of data. One reason for the discrepancy is due to computational limitations–the sheer size of the available data makes a global, integrated analysis using current techniques infeasible. Another reason is due to statistical challenges–much of the data is polluted with both technical and biological noise, and identifying the pertinent biological signals in the midst of this noise is difficult.
The goal of our lab is to improve the inference of biological meaning from the wealth of experimental data collected from single cells to whole organisms. To do so, we develop sophisticated statistical and computational tools that enable integrated analyses of noisy, heterogeneous datasets.
Graduate student research positions are available for highly motivated individuals interested in pursuing a Master’s or Ph.D. in computational systems biology. Relevant UofM graduate programs include those in Biological Sciences and Bioinformatics. Please click here for more information.