Bioimage Informatics draws upon advances in signal processing, optics, probe chemistry, molecular biology and machine learning to provide answers to biological questions from the growing numbers of biological images acquired in digital form. Microscopy is one of the oldest biological methods, and for centuries it has been paired with visual interpretation to learn about biological phenomena. With the advent of sensitive digital cameras and the dramatic increase in computer processing speeds over the past two decades, it has become increasingly common to collect large volumes of biological image data that create a need for sophisticated image processing and analysis. In addition, dramatic advances in machine learning during the same period set the stage for converting imaging from an observational to a computational discipline and allow the direct generation of biological knowledge from images.
Department of Chemistry
Biological research has been propelled by the availability of fluorescent proteins that allow dynamic microscopy of living cells. The repertoire of intrinsically fluorescent proteins is substantially less diverse in form and function than the repertoire of chemically synthesized dye molecules, yet genetic targeting provides such a significant advantage that probes 2-10-fold less bright than typical organic dyes are routinely used in fluorescence imaging. Our work is focused on developing tools that couple the best of the synthetic dyes with the advantages of genetic targeting. These novel probes allow unique investigations of cell-biological and biochemical processes fundamental to our understanding of health and diseases.
Department of Computational & Systems Biology
We investigate the molecular and cellular origins of human epithelial malignancies (e.g., breast cancer, Barrett’s) through computational models. We pursue two interrelated approaches:
- Computational Pathology and Bioimaging We develop algorithms to analyze intratumor phenotypic heterogeneity from in situ fluorescent imaging of tissue sections or tissue microarrays.
- Computational Biophysics We develop models based on anharmonic fluctuations to discern short-lived and rare intermediate conformations that proteins access to fold, bind, and function.
Department of Bioengineering
Departments of Computer Science and Machine Learning
- Data Mining for graphs and streams
- Fractals, self-similarity and power laws
- Indexing and data mining for video, biological and medical databases
- Data base performance evaluation (data placement, workload characterization)
Department of Machine Learning
I’m interested in multi-agent planning, reinforcement learning, decision-theoretic planning, statistical models of difficult data (e.g. maps, video, text), computational learning theory, and game theory.
Departments of Biomedical Engineering and Electrical and Computer Engineering and Center for Bioimage Informatics
The main focus in our lab is on building automated systems for processing and interpretation of biomedical images. To that end, we use both the tools already developed in signal and image processing and machine learning as well as develop new tools specifically tailored to the problem at hand. Many of the techniques we use are based on sophisticated signal-processing concepts, such as multiresolution/wavelets and multiscale processing.
Department of Computational & Systems Biology
Cells process information about their environment using a complex network of molecular circuits. Our research combines principles of systems and synthetic biology to understand how information flows through these circuits and fine-tunes cellular responses.
Lane Center for Computational Biology, Departments of Biological Sciences and Biomedical Engineering, Machine Learning Department, and Center for Bioimage Informatics
- The primary focus of current work in the lab is on automated interpretation of fluorescence microscope images.
Department of Biomedical Engineering, Lane Center for Computational Biolgoy, and Center for Bioimage Informatics
Department of Chemical Engineering and Lane Center for Computational Biology
Professor Sahinidis concentrates on optimization in biology, chemistry, medicine, and engineering.
Director – Drug Discovery Institute; Department of Computational & Systems Biology
My research interests have been rooted in understanding the temporal-spatial dynamics of signaling molecules and proteins in living cells, coupled to defining the mechanisms of fundamental cell functions such as cell division and cell migration. I have always integrated the development of new technologies in fluorescence-based reagents and light microscope imaging in order to improve the ability to define molecular events in cells and tissue models. My interests have evolved from single cell activities to understanding cellular population dynamics, including the biological basis for heterogeneity in response to perturbagens such as drug treatments.
Department of Biomedical Engineering and Lane Center for Computational Biology
My research interests are in computational cell biology, bioimage informatics, molecular cell mechanics, and fluorescence imaging. Current research topics include:
- Experimental and computational analysis of spatiotemporal regulatory mechanisms of axonal cargo transport
- Active control of intracellular transport for targeted intracellular delivery
- Imaging-based experimental and computational analysis of spatial-temporal dynamics of cell signaling and the cytoskeleton
- Molecular cell mechanics of the cytoskeleton
- Advanced experimental and computational fluorescence live imaging