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Funding Update : Jul 29, 2011

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NSF Bioinformatics Grants Awarded June 30 — July 27, 2011

Novel Methodology for Leveraging Metabolic Simulation to Improve Regulatory Reconstruction
Start Date: Sept. 1, 2011
Expires: Aug. 31, 2012
Awarded Amount to Date: $98,928
Principal Investigators: Justin Domke, Anna Haake
Sponsor: Rochester Institute of Technology

Supports the development of computational methods to better reconstruct gene regulatory networks using metabolic simulation.


Network Learning for Integrative Cancer Genomics
Start Date: Sept. 1, 2011
Expires: Aug. 31, 2014
Awarded Amount to Date: $500,000
Principal Investigators: Rui Kuang
Sponsor: University of Minnesota, Twin Cities

The funds will support the development of network-based machine-learning theoretical frameworks and methods that can model underlying biological mechanisms in cancer. The investigators plan to formulate graph kernels and subgraph mining algorithms that can integrate various types of cancer genome aberrations to improve cancer outcome predictions. They also aim to discover cancer-causative genome aberration patterns and to create semi-supervised matrix factorization methods with Laplacian constraints for predicting novel cancer phenotype and gene associations for identifying potential drug targets. The tools and methods will be used to identify pathways driving chemoresistance in ovarian cancer treatment.


A Large-Scale Data Mining Framework for Genome-Wide Mapping of Multi-Modal Phenotypic Biomarkers and Outcome Prediction
Start Date: Aug. 1, 2011
Expires: July 31, 2014
Awarded Amount to Date: $299,904
Principal Investigators: Heng Huang, Fillia Makedon
Sponsor: University of Texas, Arlington

According to the abstract, the funds will support the development of multi-view learning methods to detect task-relevant phenotypic biomarkers from large-scale heterogeneous imaging and other biomarker data; implement sparse multi-task regression models to find the genetic basis of phenotypic biomarkers at multiple levels; and robust classification methods via structural sparsity for outcome prediction using integrated genotypic and phenotypic data.


Algorithmic Tools for Spatial Positioning Studies in the Cell Nucleus
Start Date: Aug. 1, 2011
Expires: July 31, 2014
Awarded Amount to Date: $499,968
Principal Investigators: Jinhui Xu, Ronald Berezney
Sponsor: State University of New York, Buffalo

The investigators plan to use computational geometry techniques and optimization methods to develop algorithmic tools to study the topology of chromosome territory; chromosome associations and spatial positioning; and the topological structures of associated chromosome territories. These tools can be used to study and compare the difference of various cancer and normal cells in topological structures and associations of chromosome territories and genes.


Mathematics of Synthetic Gene Networks
Start Date: Aug. 1, 2011
Expires: July 31, 2013
Awarded Amount to Date: $331,315
Principal Investigators: Xiao Wang, Yixin Shi
Sponsor: Arizona State University

Supports the expansion of a mathematical framework used in modeling and analyzing synthetic gene networks. The approach uses a combination of mathematical, engineering, and biological techniques to construct, monitor, and analyze novel engineered gene networks.


Nonlinear Matrix and Tensor Completion with Applications in Systems Biology Start Date: July 1, 2011
Expires: June 30, 2014
Awarded Amount to Date: $478,005
Principal Investigators: Olgica Milenkovic, Pierre Moulin, Ely Kerman
Sponsor: University of Illinois, Urbana-Champaign

The funds will support the development of a new algorithmic and analytic paradigm, dubbed low-rank matrix and tensor completion, for predicting which experiments are likely to provide the most informative outcomes of biological tests, based only on a small sample of incomplete and noisy measurements. Specifically, it involves the development of novel tensor and nonlinear completion methods for inferring protein-protein interaction networks and designing synthetic lethality experiments. The method can be used in experiments to discover cellular functions and organization, or to estimate the efficiency of drug treatments in large-scale systems.


Accurate, Biochemically-Relevant, and Robust Scoring Functions for Protein-Ligand Binding Affinity Prediction
Start Date: July 1, 2011
Expires: June 30, 2014
Awarded Amount to Date: $326,000
Principal Investigators: Nihar Mahapatra
Sponsor: Michigan State University

Supports the development of discrete optimization algorithms that facilitate the design of high-throughput single and multi scoring function (SF) methods with provable optimality for a protein-ligand complex dataset; determination of biochemically-relevant SFs through novel biochemical rule filters that constrain the selected protein-ligand complex features; prediction of robustness through a multi-SF approach that reduces the variance in accuracy associated with relying on only a single SF; and flexibility in accuracy-throughput tradeoff provided through a new integrated dynamic multi-SF approach.


Next-Generation Algorithms for Stochastic Spatial Simulation of Cell Polarization Start Date: July 1, 2011
Expires: Sept. 30, 2012
Awarded Amount to Date: $248,681
Principal Investigators: Tau-Mu Yi
Sponsor: University of California, Santa Barbara

These funds will support the development of an algorithm for modeling, simulating, and analyzing spatial stochastic dynamics arising in cell polarity in the yeast pheromone response system.


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