Dr. Yves Brun

Systems Biology/Microbiology Faculty Search

Department of Biology

Indiana University, Jordan Hall 142, 1001 E. 3rd St

Bloomington, IN 47405-7005

Re: Dr. Chen-Shan Chin

Dear Dr. Brun:

I am writing to strongly recommend Dr. Chen-Shan Chin for a faculty position. Chen-Shan is currently a postdoctoral fellow in my lab. He was trained in theoretical physics/statistical mechanics, with a Ph.D. in theoretical condensed matter physics from the University of Washington in Seattle. He joined my lab in the fall of 2002 with strong physics credentials. As a graduate student, he published several influential papers on the theory of surface growth. In the last three years, he has made a successful transition from theoretical physics to quantitative biology: he has gained substantial knowledge of biology, has worked on a broad range of biological problems, and learned how to do experiments.

I have always been impressed by Chen-Shan’s multiple talents. Being trained in theoretical physics, he is insightful and fluent at mathematical modeling. He is also an expert on computer systems, software development, networking, etc. This combined theoretical and computational skill is crucial for his success in several computational projects that I will describe in more detail. In the last several months, he has learned how to do yeast experiments. And he did not just “learn” how to do experiments, instead he has developed new ways of doing experiments! Utilizing his theoretical and computational skills, he has developed a unique approach to study the dynamics of gene regulation quantitatively at the pathway/network level. In doing that, he had to solve several challenging instrumentation problems and I was quite impressed by how quickly he managed to do it. This project is currently in progress and to a large extent will define his future directions, so I will describe it first.

Chen-Shan’s project on the quantitative dynamics of gene regulation was originally motivated by his theoretical modeling. The goal is to understand the functional significance and the design principles of the complex network architectures, by analyzing the relationship between the network architecture and the quantitative features (such as timing, amplitude, noise, etc) of the cellular response to environment. To tackle the problem, Chen-Shan combined theoretical modeling with large-scale quantitative measurements. On the theoretical side, Chen-Shan started with careful bioinformatics analysis to build local transcription networks responsible for regulating specific biological processes (such as sporulation or amino acid starvation response). He then used a statistical mechanics model to infer the dynamics of the transcription factors and the nature of the interaction between the transcription factors based on the circuit diagram and gene expression profiles. On the experimental side, in collaboration with the DeRisi and Weissman labs, Chen-Shan has developed a prototype robotic system which automatically delivers samples of cells in a set of chemostat reactors to a flow cytometer. Previously flow cytometer measurements for protein dynamics required manual sample loading and push-button operations for data collection. Chen-Shan hacked the computer code to intercept the data traffic between the flow cytometer and the computer, and made both the sample delivery and data acquisition automatic. Currently he can monitor 6 genes tagged by EGFP simultaneously and sample tens of thousand cells once every minute over a time course of several hours. Although this sounds quite technical, the advance in the technology allows him to monitor protein abundance in single cells with high temporal resolution, which is essential for quantitative modeling. His preliminary data on the leucine synthetic pathway in response to amino acid starvation is quite encouraging and has revealed some unexpected features of the response of the cell population. Although currently still exploratory, with these tools in hand, I expect that Chen-Shan will have some novel findings and new insights in the next few months.

Chen-Shan is so far the most productive postdoc that I have worked with. He is creative, hardworking, and independent. Before he endeavored to work in experiments at the bench, he was already quite accomplished in computational biology. Shortly after he arrived, he independently developed a collaboration with a NASA scientist to study protein-protein interaction networks and soon published a paper in Bioinformatics. He has made significant contributions to our work on a comparative analysis of gene expression profiles across species, and was a co-author of a Nature Genetics paper we published last year. In collaboration with Ira Herskowitz’s lab, Chen-Shan developed a novel method to identify the regulatory targets of a transcription factor with high sensitivity and specificity, by combining biochemical analysis with computational genomics. He did all the computational work and is a co-first author of a paper currently in press with BMC bioinformatics.

Another accomplishment of Chen-Shan was a comparative genome analysis of yeast promoters. Together with another postdoc Jeff Chuang (currently an assistant professor at Boston College), Chen-Shan measured the regulatory complexity of the yeast genome by a genome-wide phylogenetic analysis of yeast promoters using five closely related species. He made a number of interesting discoveries. First, he carefully measured local neutral mutation rate and found, surprisingly, that the rate is uniform in the yeast genome, in sharp contrast to the situation in the human genome where the mutation rate is known to vary along chromosomes. Having established that the mutation rate is uniform, he developed a nice mathematical approach based on the frequency of conserved blocks of different lengths to separate functionally conserved and neutral sequences in the yeast promoters, at a resolution of a few base pairs. This allowed him to calculate the amount of conserved promoter sequences upstream of every gene and to estimate the complexity of gene regulation at the genomic scale. This work was published this year in Genome Research.

To summarize, I believe Chen-Shan is a unique candidate for a quantitative/systems biology position. A theoretical physicist by training, he has made a successful transition to biology and has become quite accomplished in the computational biology area. In a short period of time, he has learned how to do yeast experiments and has developed unique experimental approaches to large scale, high resolution, quantitative measurements. His combined talents in theory, computation, and wet bench experiments put him in a unique position to tackle systems biology problems. Beside research, Chen-Shan is also a nice person, very collaborative and easy to get along with. I recommend him highly.

Sincerely yours,

Hao Li

Associate Professor