Recent advances in imaging and microscopy techniques have led to a surge in biological data. Different imaging modalities such as computed tomography (CT), magnetic resonance imaging (MRI), confocal microscopy, and serial sectioning microscopy (both optical and electron microscopy) have enabled the observation of morphological characteristics of biological systems and organs at multiple spatial and temporal scales (e.g., neuronal networks, vascular networks, airways, and villi). The different imaging modalities and scales, and the amount of data (easily reaching several terabytes) pose serious computational and mathematical challenges in image analysis and modeling. For example, segmentation, tracing, and reconstruction of densely packed objects in 3D is computationally intensive and error-prone, and inference of nano-scale morphological properties from micro- or macro-scale imaging data require the discovery of invariant statistical properties across scales. This minisymposium will focus on latest advances in multiscale and space-time imaging methods, image processing/analysis algorithms, and modeling frameworks that will enable the interpretation and integration of data from multiple scales and spatial-temporal correlations in biological systems. Also relevant to this symposium are strategies for data and model sharing that will require further abstractions for general multiscale and space-time frameworks for imaging and image analysis.

Notes: (1) "*" marks confirmed speakers. (2) See Session_6_Speakers for the abstracts.

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ABSTRACTS

• Cai:Multi-scale modeling of spatiotemporal dynamics of the primary visual cortex

David Cai

We discuss our large-scale (~1 million neurons) computational modeling of the primary visual cortex (V1). In particular, we describe network mechanisms underlying stochastic, spatiotemporal dynamics associated with spontaneous on-going activity of the V1 and the line-motion illusion --- which is the illusory motion sensation from a static cue of a flashed stationary square quickly followed by a stationary bar. Furthermore, we use a new analysis of coarse-grained event-chains to demonstrate the fine discriminability of orientation of V1.

• Wilson: Mapping and modeling receptor topography during signal transduction

Bridget Wilson+, Ming-yu (Genie) Hsieh*, Shujie Yang+, Diane Lidke+, Janet Oliver+, and Jeremy S. Edwards#

*Department of Electrical and Computer Engineering

#Department of Molecular Genetics and Microbiology

+Department of Pathology

University of New Mexico, NM 87131

The ErbB family of growth factor receptors are widely expressed by cells of epithelial and mesenchymal lineages. EGFR and ErbB2 overexpression can lead to ligand-independent signaling and is linked to carcinogenesis. We use a combination of high resolution microscopy approaches to quantitatively evaluate the topography and behavior of resting and actively signaling ErbB receptors. Immunoelectron microscopy methods capture nanoscale spatial relationships between receptors and their intracellular signaling partners. Live cell imaging approaches, including single particle tracking of quantum dot probes, monitor diffusional properties of receptor monomers and dimers and provide evidence for cytoskeletal corrals. These data provide the foundation for our agent-based modeling efforts. Our stochastic modeling framework incorporates important spatio-temporal aspects of signaling, including protein clustering, protein motion and biochemical reactions within an idealized cellular geometry. We investigate mechanisms of receptor dimerization and activation as functions of time and receptor conformation, density and spatial distribution. For example, we have considered the effects of receptor clustering patterns of ErbB family members on both hetero- and homo-dimerization rates, using immunoelectron microscopy data. Simulation results suggest that partial spatial segregation of ErbB receptors has a profound impact on heterodimerization rates. We propose that membrane spatial organization is a significant contributor to the carcinogenesis process.

• Glass: Optical Imaging of Tissue Culture Models of Cardiac Arrhythmias

L. Glass, A. Shrier, G. Bub, H. Gonzalez, Y. Nagai, M.-Y. Kim, A. Hodge, B. Borek.

McGill University

Cardiac arrhythmias are associated with the abnormal initiation and/or abnormal propagation of the heart rhythm. By carrying out optical imaging of tissue cultures of spontaneously beating heart cells, it is possible to analyze macroscopic patterns of excitation with a goal of relating this to the cellular and subcellular electrophysiological properties of heart cells. In particular we have been able to observe transitions between rhythms in vitro, that model the normal and pathological rhythms in vivo. Optical imaging of tissue culture offers the benefit of providing a controlled enviroment that can be observed over long times, in which it is possible to systematically modify the geometry of the culture, the density of the culture, as well as change the physiology by the addition of pharmacological agents. The experimental work is complemented by theoretical studies of bifurcations and dynamics in mathematical models of excitable, heterogeneous systems. This work points to universal patterns of bifurcations underlying cardiac arrhythmias in humans. This work has been carried out in collaboration with our group at McGill including A. Shrier, G. Bub, H. Gonzalez, Y. Nagai, M.-Y. Kim, A. Hodge, B. Borek.

• Kriete: Multiscale Imaging Reveals Robustness and Capacities of the Human Respiratory System

Andres Kriete (1), Andreas Schmidt (2), S. Zidowitz (3), Daniel C. Haworth(4), Robert F. Kunz (5), Kai Zhao (6), Warren B. Gefter (7)

(1) Biomedical Engineering, Drexel University, (2) Institute of Anatomy, Medical School at the University of Giessen, (3) Center for Medical Image Computing, Mevis Bremen, (4) Department of Aerospace Engineering, Penn State, (5) Applied Research Laboratory, Penn State, (6) Monell Institute, (7) Dept. of Radiology, University of Pennsylvania

System engineered organ representations based on multiscale imaging provide insight into the evolutionary shaped design of the transport networks in terms of modularity, physical optimality and robustness. We have assembled a consortium of investigators to image, model and simulate functional properties of the human respiratory system. MDCT data of nine human lungs and a highly resolved cast, as well as micro-CT and light microscopic imaging was used to investigate dimensions and heterogeneities in geometric branching patterns, gas supply and diffusion capacities. We show that the dimensions of airways in these cases change systematically from area-preserving to area-increasing, thus they reflect multifractal properties. Airways are generally larger than predicted by the Hess-Murray law, providing up to four-fold less resistance as well as increase in robustness against perturbations. We show that resistance in the network topology of airways mediates a mismatch between anat omically demanded and predicted oxygen supply, which offers an explanation for the excess diffusion capacity of human lungs. Robustness and capacities are important cornerstones for the development of solutions to improve diagnostics and intervention planning in aging and disease.