Schedule, Registration & Abstracts
9:35 - 10:05 am : Xiaoping Hu, Department of Bioengineering, UCR
10:05 - 10:40 am : Amit K. Roy Chowdhury, Electrical and Computer Engineering, UCR
10:40 - 11:00 am : Coffee Break
11:00 - 11:15 am : Presentations by Postdoctoral Associates
11:15 am - 12:15 pm : Plenary Talk, Scott Fraser, Department of Molecular and Computational Biology, Department of Biomedical Engineering, Department of Stem Cell Biology and Regenerative Medicine,University of Southern California
12:15 - 1:30 pm : Lunch and informal discussions
There is no registration fee but participants are encouraged to register here: Registration Form
1 Department of Mathematics, University of California, Riverside
2 Center for Quantitative Modeling in Biology, University of California, Riverside
3 Department of Botany and Plant Sciences, Center for Plant Cell Biology (CEPCEB),
University of California, Riverside
4 Institute of Integrative Genome Biology, University of California, Riverside
Data-driven multiscale modeling of the role of signaling in the maintenance of transcription factordistribution in stem cell homeostasis
The regulation and interpretation of transcription factor levels is critical in spatiotemporal regulation of gene expression in development biology. However, concentration-dependent transcriptional regulation, and the spatial regulation of transcription factor levels are poorly studied in plants. WUSCHEL, a stem cell-promoting homeodomain transcription factor was found to activate and repress transcription at lower and higher levels respectively. The differential accumulation of WUSCHEL in adjacent cells is critical for spatial regulation on the level of CLAVATA3, a negative regulator of WUSCHEL transcription, to establish the overall gradient. Experiments show that subcellular partitioning and protein destabilization control the WUSCHEL protein level and spatial distribution. Meanwhile the destabilization of WUSCHEL also depends on the protein concentration which in turn is influenced by intracellular processes. However, the roles of extrinsic spatial cues in maintaining differential accumulation of WUSCHEL are not well understood. Wedevelop a 3D cell-based mathematical model which integrates sub-cellular partition with cellular concentration across the spatial domain to analyze the regulation of WUS. By using this model, we investigate the machinery of the maintenance of WUS gradient within the tissue. We also developed a hybrid ODE mathematical model of stochastic binding and unbinding of WUS to cis-elements regulating CLV3 expression, connected with deterministic dynamics that accounts for WUS protein dynamics to understand the concentration dependent manner mechanistically. By using the hybrid model, we can explore hypotheses regarding the nature of co-operative interactions among cis-elements, the influence of WUS complex stoichiometry (monomer versus dimer binding) on the transcriptional switching behavior and the CLV3 signaling feedback on the regulation of WUS protein levels, which is critical for stem cell homeostasis.
Xiaoping P. Hu, Chair and Professor, Department of Bioengineering, University of California, Riverside
Some Recent Advances in Neuroimaging
With the advent of functional MRI almost 30 years ago and the advances made with diffusion tensor imaging, MRI is now a widely used tool for examining the function and the structural connectivity of the brain. In addition, MRI can also be used to visualize brain dynamics, iron and melanin. In this talk, I will start with a brief overview of neuroimaging with MRI and follow with more detailed descriptions on our recent work in brain dynamics, iron and melanin, along with our application of the latter two to Parkinson’s disease.
Amit Roy-Chowdhury, Chair and Professor, Electrical and Computer Engineering, University of California, Riverside
Visual Learning with Limited Supervision by Exploiting Context
It is well known that relationships between data points (i.e., context) in structured data can be exploited to obtain better recognition performance. In our recent work, we have explored a different, but related, problem: how can these inter-relationships be used to efficiently learn and continuously update a recognition model, with minimal human labeling effort. Towards this goal, we have proposed an active learning framework to select an optimal subset of data points for manual labeling by exploiting the relationships between them, which will be the focus of the first part of the talk. We will show how information theoretic measures, using ideas of entropy, mutual information and typicality, can be used to identify the optimal subsets. In the second part, we will link these broad ideas of learning with limited supervision with biological applications like cell tracking, where large amounts of labeled data are often not available.
Scott E. Fraser, Provost Professor, Department of Molecular and Computational Biology, Department of Biomedical Engineering, Dept of Stem Cell Biology and Regenerative Medicine, University of Southern California, Los Angeles
Adding Dimensions to Intravital and Multimodal Microscopy
The challenge of modern biology is to draw upon the growing body of high-throughput molecular data to better understand the key events underlying a biological process. This wealth of data presents the challenge of integrating a working knowledge of how these molecular components, often present at vanishingly small concentrations, generate reliable patterns of cell migration and cell differentiation. In typical cell biology approaches, cultures of isolated cells have been used reveal mechanism. What is needed to understand development is to carry out studies on cells in their normal context interacting with other cells and signals.
Imaging techniques are challenged by major tradeoffs between spatial resolution, temporal resolution, and the limited photon budget. We are advancing this tradeoff by constructing two-photon light-sheet microscopes, combining the deep penetration of two-photon microscopy and the speed of light sheet microscopy, permitting 4D cell and molecular imaging with sufficient speed and resolution to generate unambiguous tracing of cells and signals in intact systems. To increase the 5th Dimensions, we are refining a new generation of multispectral image analysis tools that exceed the performance of our previous work on Linear Unmixing by orders of magnitude in speed, error propagation and accuracy. These new analysis tools permit rapid and unambiguous analyses of multiplex-labeled specimens. Finally, to move to faster volumetric imaging, we have combined light field and light sheet approaches, offering the signal to noise needed to image thousands of neurons with high fidelity.
Multispectral imaging offers the chance of asking multiple questions of the same embodied cells. Multiplex analyses permit the variance and the “noise” in a system to be exploited by asking about the analytes that co-vary with a selected gene product. These multi-dimensional imaging tools permit key events to be followed in intact systems as they take place, and allow us to use variance as an experimental tool rather than feeling its effects as a limitation.