Mission

The Laboratory was founded in 2004. In collaboration with experimentalists, we initiate and develop Systems approaches to various problems in molecular and cellular biology. Our goals lie in two areas that define Systems Biology: 1) Develop computational technology to help measure molecular systems, using post-genomic experimental large-scale technology, such as transcriptomics, proteomics, and metabolomics. 2) Develop integrative analyses of multiple large-scale data sets. The specific aims we currently pursue are 1) To develop methods and tools to more effectively analyze proteomic experiments on mass spectrometers ("Mass spec informatics"), and 2) To develop principles, methods, and tools for the integration of large-scale data on genomic, temporal, organiza­tional, and evolutionary scales ("Data integration").

Concept

As of early 2009, the group is a “dry lab” and consists primarily of Ph.D.s trained in the formal sciences with strong interest and experience in molecular biology, and software engineers. We operate in collaborations with local, national and international laboratories.

Representative Projects

BaSysBio: An integrated EU Systems Biology project among 16 different institutions aimed at a high-level understanding of carbon metabolism and defined stress responses of B. subtilis. Our group is responsible for a workpackage in which we explore network modularization techniques and create mass spectrometry informatics approaches to integrate information from a large number of experiments. In particular, we are developing approaches to identify groups of genes of the same function from a mix of large-scale data, and to connect these into a “module” or “pathway model” of global regulation. Other products of this work are novel insights into gene function and regulation, and

Cytoscape: A 5-year, NIH-funded collaboration with 5 other organizations in the United States (academia and industry) to create the standard visualization tool for systems biology. Cytoscape is the most commonly used tool for visualizing and analyzing molecular interaction networks and associated data. Cytoscape is extremely flexible, open-source, and allows a wide range of applications thanks to its plug-in architecture.

Generalized PMF: The comprehensive analysis of complex protein mixtures is an important element for many Systems Biology approaches. The most common experimental approach combines mass spectrometry with protein separation by liquid chromatogra­phy (LC). In an effort to optimally exploit the data generated by this approach, we are developing computational approaches to utilize LC retention time as an additional characteristic (besides accurate masses) for the identification of proteins.

Keywords: Systems Biology, High-Throughput, Transcriptomics, Proteomics, Data integration

Mazurie A, Bonchev D, Schwikowski B, Buck GA (2008) Phylogenetic distances are encoded in networks of interacting pathways. Bioinformatics: Advance Access published September 26, 2008. PMID: 18820265

Prakash A, Piening B, Whiteaker J, Zhang H, Shaffer SA, Martin D, Hohmann L, Cooke K, Olson JM, Hansen S, Flory MR, Lee H, Watts J, Goodlett DR, Aebersold R, Paulovich A, Schwikowski B (2007) Assessing bias in experiment design for large-scale mass spectrometry-based quantitative proteomics. Mol Cell Proteomics 6: 1741-1748 PMID: 17617667

Cline MS, et al. (2007) Integration of biological networks and gene expression data using Cytoscape. Nat Protoc 2(10): 2366-2382. PMID: 17947979

Prakash, A., P. Mallick, J. Whiteaker, H. Zhang, A. Paulovich, M. Flory, H. Lee, R. Aebersold, and B. Schwikowski. (2006). Signal Maps for Mass Spectrometry-based Comparative Proteomics. Mol Cell Proteomics 5:423-432. PMID: 16269421

Reiss, D. J., I. Avila-Campillo, V. Thorsson, B. Schwikowski, and T. Galitski. 2005. Tools enabling the elucidation of molecular pathways active in human disease: application to Hepatitis C virus infection. BMC Bioinformatics 6:154. PMID: 15262809