Institute for Systems Biology

About: Institute for Systems Biology is a nonprofit organization based out in Seattle, Washington, United States. It is known for research contribution in the topics: Population & Proteomics. The organization has 1277 authors who have published 2777 publications receiving 353165 citations.

Computational and Statistical Approaches to Genomics

Abstract: Microarray Image Analysis and Gene Expression Ratio Statistics, Y. Chen, et al Statistical Considerations in the Assessment of cDNA Microarray Data Obtained Using Amplification, J. Wang, et al Sources of Variation in Microarray Experiments, M.K. Kerr, et al Studentizing Microarray Data, K.A. Baggerly, et al Exploratory Clustering of Gene Expression Profiles of Mutated Yeast Strains, M. Oja, et al Selecting Informative Genes for Cancer Classification Using Gene Expression Data, T. Akutsu, S. Miyano Design Issues and Commparison of Methods for Microarray-Based Classification, E.R. Dougherty, S.N. Attoor Analyzing Protein Sequences using Signal Analysis Techniques, K.M. Bloch, G.R. Arce Statistics of the Numbers of Transcripts and Protein Sequences Encoded in the Genome, V.A. Kuznetsov Normalized Maximum Likelihood Models for Boolean Regression Used for Prediction and Classification in Genomics, I. Tabus, et al Inference of Genetic Regulatory Networks via Best-Fit Extensions, I. Shmulevich, et al Regularization and Noise Injection for Improving Genetic Network Models, E. van Someren, et al Parallel Computation and Visualization Tools for Codetermination Analysis of Multivariate Gene-Expression Relations, E.B. Suh, et al Human Glioma Diagnosis from Gene Expression Data, G.N. Fuller, et al Application of DNA Microarray Technology to Clinical Biopsies of Breast Cancer, L. Pusztai, et al Alternative Splicing - Genetic Complexity in Cancer, S.W. Song, et al Single-Nucleotide Polymorphisms, DNA Repair, and Cancer, Q. Wei, et al.

Guidelines for reporting the use of mass spectrometry in proteomics

Abstract: Joeri Borstlap1, Glyn Stacey2, Andreas Kurtz3, Anja Elstner3, Alexander Damaschun1, Begoña Arán4 & Anna Veiga4,5 1CellNet Initiative, Berlin-Brandenburg Center for Regenerative Therapies (BCRT), Charité– Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany. 2The UK Stem Cell Bank, National Institute for Biological Standards and Control, Blanch Lane, South Mimms, Potters Bar, Hertfordshire, EN6 3QG, UK. 3Cell Therapy Group, BerlinBrandenburg Center for Regenerative Therapies (BCRT), Charité–Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany. 4Banc de Linies Cellulars, Centre de Medicina Regenerativa de Barcelona (CMRB), C/Dr. Aiguader 88, 08003-Barcelona, Spain. 5Institut Universitari Dexeus, Passeig de la Bonanova 67, 08017-Barcelona, Spain. e-mail: joeri.borstlap@b-crt.de

Pathway alterations during glioma progression revealed by reverse phase protein lysate arrays

Abstract: The progression of gliomas has been extensively studied at the genomic level using cDNA microarrays. However, systematic examinations at the protein translational and post-translational levels are far more limited. We constructed a glioma protein lysate array from 82 different primary glioma tissues, and surveyed the expression and phosphorylation of 46 different proteins involved in signaling pathways of cell proliferation, cell survival, apoptosis, angiogenesis, and cell invasion. An analysis algorithm was employed to robustly estimate the protein expressions in these samples. When ranked by their discriminating power to separate 37 glioblastomas (high-grade gliomas) from 45 lower-grade gliomas, the following 12 proteins were identified as the most powerful discriminators: IBalpha, EGFRpTyr845, AKTpThr308, phosphatidylinositol 3-kinase (PI3K), BadpSer136, insulin-like growth factor binding protein (IGFBP) 2, IGFBP5, matrix metalloproteinase 9 (MMP9), vascular endothelial growth factor (VEGF), phosphorylated retinoblastoma protein (pRB), Bcl-2, and c-Abl. Clustering analysis showed a close link between PI3K and AKTpThr308, IGFBP5 and IGFBP2, and IBalpha and EGFRpTyr845. Another cluster includes MMP9, Bcl-2, VEGF, and pRB. These clustering patterns may suggest functional relationships, which warrant further investigation. The marked association of phosphorylation of AKT at Thr308, but not Ser473, with glioblastoma suggests a specific event of PI3K pathway activation in glioma progression.

Recent progress toward the use of circulating microRNAs as clinical biomarkers.

Abstract: MicroRNAs (miRNAs) have been shown to be critical mediators of many cellular and developmental processes and have been implicated in different human diseases. Since the observation of extracellular miRNAs present in various biofluids, much attention and excitement have been garnered toward understanding the functional roles of these circulating extracellular miRNAs and establishing their potential use as noninvasive diagnostic biomarkers. Here, we will review the current state of miRNA biomarkers for many human diseases, including their emerging use in toxicological applications, and discuss the current challenges in the field, with an emphasis on technical issues that often hinder discovery-based miRNA biomarker studies.

Optimal flux spaces of genome-scale stoichiometric models are determined by a few subnetworks

Abstract: The metabolism of organisms can be studied with comprehensive stoichiometric models of their metabolic networks. Flux balance analysis (FBA) calculates optimal metabolic performance of stoichiometric models. However, detailed biological interpretation of FBA is limited because, in general, a huge number of flux patterns give rise to the same optimal performance. The complete description of the resulting optimal solution spaces was thus far a computationally intractable problem. Here we present CoPE-FBA: Comprehensive Polyhedra Enumeration Flux Balance Analysis, a computational method that solves this problem. CoPE-FBA indicates that the thousands to millions of optimal flux patterns result from a combinatorial explosion of flux patterns in just a few metabolic sub-networks. The entire optimal solution space can now be compactly described in terms of the topology of these sub-networks. CoPE-FBA simplifies the biological interpretation of stoichiometric models of metabolism, and provides a profound understanding of metabolic flexibility in optimal states.