Justin Kinney
CSHL/QB Fellow
Ph.D., Princeton University, 2008
Sequence-function relationships; biophysics; deep sequencing; machine learning; transcriptional regulation; DNA replication
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My research combines theory, computation, and experiment in an effort to better understand the relationship between sequence and function in molecular biology. The use of microarray and ultra-high-throughput sequencing technologies plays a central role in this work. Using these technologies, one can assay the activities of tens of thousands to millions of different biological sequences in simple, small-scale experiments. The resulting data can then be used infer precise quantitative modes for how biological sequence dictates function.
Importantly, one does not need a detailed understanding of experimental noise in order to reliably fit quantitative models of sequence-function relationships to such data. This was shown by Kinney et al. (2007) in the context of using ChIP-chip and protein binding microarray data to infer models for the DNA sequence-specificities of transcription factors. The same principle can be used to fit models to any data set comprising a list of sequences and corresponding measurements for whatever biological activity one is interested in.
In Kinney et al. (2010), a combination of fluorescence-activated cell sorting and 454 pyrosequencing was used to measure the transcriptional activities resulting from hundreds of thousands of slightly mutated versions of the Escherichia coli lac promoter. Fitting biophysically-inspired models not only allowed the sequence specificities of regulatory proteins to be determined in their native functional context, it also allowed the interaction energy between a DNA-bound transcription factor and a DNA-bound RNA polymerase holoenzyme to be measured in living cells. This approach — measuring the in vivo activity of a large number of slightly mutated regulatory sequences — provides a new way of interrogating the transcriptional regulatory code, and can likely be applied to a wide range of regulatory sequences in a variety of single-celled organisms as well as in cell culture.
Selected Publications
Kinney JB, Murugan A, Callan CG, Cox EC (2010) Using deep sequencing to characterize the biophysical mechanism of a transcriptional regulatory sequence. Proc Natl Acad Sci USA. 107:9158-9163
Mustonen V, Kinney JB, Callan CG, Lässig M (2008) Energy-dependent fitness: a quantitative model for the evolution of yeast transcription factor binding sites. Proc Natl Acad Sci USA. 105:12376-12381
Kinney JB, Tkacik G, Callan CG (2007) Precise physical models of protein-DNA interaction from high-throughput data. Proc Natl Acad Sci USA.104:501-506.