Microscopic imaging is essential for understanding the organization of the intricate structures of biological samples. However, the large size of microscopic image datasets (gigabytes to terabytes) poses new challenges for quantitatively characterizing features from such images. We have developed a set of algorithms handling different issues in analyzing such images including registration, segmentation, visualization and quantification. One of the applications is to model breast tumor microenvironment.

The tumor microenvironment (TME) is a significant contributor to the progression of cancer. TME in breast cancer consists of a multitude of cell types such as endothelial cells, fibroblasts, and immune cells. To design a realistic computational model for cancer, we need to model the both the intracellular reactions and cell-cell interactions in the TME. Our goal is to develop a "geographical information system" of the breast TME by integrating spatial information of cells (from microscopic imaging) with molecular information (e.g., gene expression and ChIP-seq) using a systems biology approach. While we will discuss a wide spectrum of algorithms involved in this work, we will focus on the microscopic image segmentation problem using two-point correlation function and two hybrid linear model fitting algorithms.

2:30-3:00 The Origin Of Slow Hydration Dynamics And Breakdown Of Linear Response
Tanping Li, Singer and Zhong labs

Proteins are the working horse of the cellular machinery. They are responsible for diverse functions ranging from molecular motors to signaling. The Broad recognition of their involvement in all cellular processes has led to efforts to predict their functions from sequences, and if available, from their structures. A practical way to predict protein function is through identification of the binding partners. Since the vast majority of protein chores in living cells are mediated by protein-protein interactions, if the function of at least one of the components with which the protein interacts is identified, it is expected to facilitate its functional and pathway assignment. Through the network of protein-protein interactions, we can map cellular pathways and their intricate cross-connectivity. Since two protein partners cannot simultaneously bind at the same (or overlapping) site, discovery of the ways in which proteins associate should assist in inferring their dynamic regulation. Identification of protein-protein interactions is at the heart of functional genomics. The talk will describe our recent work in this direction.