This research is aimed at developing and testing methods that will enable automated, information--?rich studies of the subcellular organization of proteins and other biomolecules. We propose to develop the computational foundation for deciphering the spatiotemporal morphological pathways of different subcellular structures during important subcellular processes automatically from microscopy images. We will leverage our progress in the previous project period, in which we established the Cell Organizer system for building image-?derived models of cell organization, to build new modeling capabilities for cisternal and reticular structures, for capturing the relationships between different cellular components, and for learning how aspects of cell organization change during processes such as the cell cycle. Given the importance of the endoplasmic reticulum in numerous cellular processes (as well as diseases including hematological disorders, cranio--? lenticulo--?sutural dysplasia, chylomicro retention disease, etc.), we expect the modeling approaches we propose to provide important mechanistic clues as to the normal (non-- athological) morphological pathways of these structures and how they may be perturbed in disease. To provide valuable images to drive the development of modeling capabilities, and to simultaneously identify candidate lead compounds for treatment of diseases with ER defects, we will conduct extensive compound screening studies on cells expressing normal and mutant alleles of atlastin (which plays a critical role in ER structure). The computational methods we develop will be integrated into our open source CellOrganizer system (http://CellOrganizer.org). In addition to the applications mentioned above, we anticipate the computational frameworks we develop will be valuable for a wide range of imaging assays including drug screens, RNAi screens, cytology, and pathology.
This research will build a computational framework for learning directly from microscope images the ways in which organelles and other structures are organized in cells and how they change in response to disease. We propose to use the methods to identify compounds that could potentially be used to develop drugs to treat diseases that affect endoplasmic reticulum structure. All computational methods developed will be released through our open source CellOrganizer system, and the tools are expected to be useful for a variety of important applications in health sciences.
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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 |
| Kolouri, Soheil; Tosun, Akif B; Ozolek, John A et al. (2016) A continuous linear optimal transport approach for pattern analysis in image datasets. Pattern Recognit 51:453-462 |
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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 |
| Donovan, Rory M; Tapia, Jose-Juan; Sullivan, Devin P et al. (2016) Unbiased Rare Event Sampling in Spatial Stochastic Systems Biology Models Using a Weighted Ensemble of Trajectories. PLoS Comput Biol 12:e1004611 |
| Rohde, Gustavo K (2015) Computational analysis of cell images. Cytometry A 87:294-5 |
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