Visual analysis and dynamical control of phosphoproteomic networks

This paper presents novel graph algorithms and modern control solutions applied to the graph networks resulting from specific experiments to discover disease-related pathways and drug targets in glioma cancer stem cells (GSCs). The theoretical framework applies to many other high-throughput data from experiments relevant to a variety of diseases. In addition to developing novel graph and control networks to predict therapeutic targets, these algorithms will provide biochemists with techniques to identify more metabolic regions and biological pathways for complex diseases, and design and test novel therapeutic solutions.

[1]  H. Hirt,et al.  Protein networking: insights into global functional organization of proteomes , 2008, Proteomics.

[2]  Robert A. Meyers,et al.  Encyclopedia of Complexity and Systems Science , 2009 .

[3]  Peter K. Sorger,et al.  Logic-Based Models for the Analysis of Cell Signaling Networks† , 2010, Biochemistry.

[4]  Alexander E. Kel,et al.  TRANSFAC®: transcriptional regulation, from patterns to profiles , 2003, Nucleic Acids Res..

[5]  Jean-Loup Guillaume,et al.  Fast unfolding of communities in large networks , 2008, 0803.0476.

[6]  K. Strimmer,et al.  Statistical Applications in Genetics and Molecular Biology A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics , 2011 .

[7]  Robert Görke,et al.  An algorithmic walk from static to dynamic graph clustering , 2010 .

[8]  C. Nilsson.  Advances in quantitative phosphoproteomics. , 2012, Analytical chemistry.

[9]  M E J Newman,et al.  Finding and evaluating community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[10]  Howard Colman,et al.  Glycomic and transcriptomic response of GSC11 glioblastoma stem cells to STAT3 phosphorylation inhibition and serum-induced differentiation. , 2010, Journal of proteome research.

[11]  M. Vidal,et al.  Literature-curated protein interaction datasets , 2009, Nature Methods.

[12]  Ulrik Brandes,et al.  On Modularity Clustering , 2008, IEEE Transactions on Knowledge and Data Engineering.

[13]  Wenwu Yu,et al.  Consensus in Directed Networks of Agents With Nonlinear Dynamics , 2011, IEEE Transactions on Automatic Control.

[14]  Sergei Egorov,et al.  Pathway studio - the analysis and navigation of molecular networks , 2003, Bioinform..

[15]  Albert-László Barabási,et al.  Controllability of complex networks , 2011, Nature.

[16]  Magnus Egerstedt,et al.  Graph Theoretic Methods in Multiagent Networks , 2010, Princeton Series in Applied Mathematics.

[17]  Howard Colman,et al.  Quantitative phosphoproteomic analysis of the STAT3/IL-6/HIF1alpha signaling network: an initial study in GSC11 glioblastoma stem cells. , 2010, Journal of proteome research.

[18]  Daizhan Cheng,et al.  State–Space Analysis of Boolean Networks , 2010, IEEE Transactions on Neural Networks.

[19]  Daizhan Cheng,et al.  Model Construction of Boolean Network via Observed Data , 2011, IEEE Transactions on Neural Networks.

[20]  Michael Hecker,et al.  Integrative modeling of transcriptional regulation in response to antirheumatic therapy , 2009, BMC Bioinformatics.