This research is aimed at developing and testing methods that will enable proteome-wide high-throughput studies of the subcellular distributions of proteins that form networks (such as microtubules), and for identifying proteins whose distributions are related to these. More specifically we will test and further refine methods that we have developed to estimate the average properties (characterize the statistical variation) of the filament distributions for a large number of cells.
Our aim i s to use the power afforded by the availability of data arising from many (possibly thousands) of cells to estimate what is really at the heart of the question for high-throughput studies of proteins of this type: what is the overall average effect of some independent variable (drug or other experimental condition) on the network filament distribution of interest. Preliminary work has established the feasibility of this approach, and we propose to test it extensively with real data for microtubules in a variety of cell types under a variety of conditions that perturb distributions in known ways. The results will establish whether a single microtubule growth model (with changes in parameters) is valid for many cell types (i.e., to remove variation due solely to cell size and shape). We will also extend this method to determine the correlation (affinity) of many unknown proteins to filament networks for several proteome wide studies currently generating such data. The methods will be used to analyze images for thousands of proteins from existing and ongoing proteome-scale studies. The identification solely on the basis of image-based modeling of specific proteins as likely to be microtubule-associated will be tested for selected examples by comparison with information in existing protein databases and literature and by additional experimentation. The successful completion of this study would not only provide important new information about the location of many proteins, but will fill a current void in modeling approaches for proteome-wide studies and facilitate the mechanistic quantification of effects of different drugs, siRNAs or mutations in high throughput screening experiments.
This research is aimed at enabling fundamental understanding of subcellular filament-type protein structure and distribution through generative modeling approaches. The successful completion of this study would enable the development of modeling approaches for similar proteins and subcellular structures and facilitate the mechanistic quantification of effects of different drugs, siRNAs or mutations in high throughput screening experiments.
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|Thorpe, Matthew; Park, Serim; Kolouri, Soheil et al. (2017) A Transportation Lp Distance for Signal Analysis. J Math Imaging Vis 59:187-210|
|Kolouri, Soheil; Park, Serim; Thorpe, Matthew et al. (2017) Optimal Mass Transport: Signal processing and machine-learning applications. IEEE Signal Process Mag 34:43-59|
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|Kolouri, Soheil; Park, Se Rim; Rohde, Gustavo K (2016) The Radon Cumulative Distribution Transform and Its Application to Image Classification. IEEE Trans Image Process 25:920-34|
|Murphy, Robert F (2016) Building cell models and simulations from microscope images. Methods 96:33-39|
|Liu, Chi; Shang, Fei; Ozolek, John A et al. (2016) Detecting and segmenting cell nuclei in two-dimensional microscopy images. J Pathol Inform 7:42|
|Johnson, Gregory R; Buck, Taraz E; Sullivan, Devin P et al. (2015) Joint modeling of cell and nuclear shape variation. Mol Biol Cell 26:4046-56|
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