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.

The organization and evolution of the dipteran and hymenopteran Down syndrome cell adhesion molecule (Dscam) genes.

Abstract: The Drosophila melanogaster Down syndrome cell adhesion molecule (Dscam) gene encodes an axon guidance receptor and can generate 38,016 different isoforms via the alternative splicing of 95 variable exons. Dscam contains 10 immunoglobulin (Ig), six Fibronectin type III, a transmembrane (TM), and cytoplasmic domains. The different Dscam isoforms vary in the amino acid sequence of three of the Ig domains and the TM domain. Here, we have compared the organization of the Dscam gene from three members of the Drosophila subgenus (D. melanogaster, D. pseudoobscura, and D. virilis), the mosquito Anopheles gambiae, and the honeybee Apis mellifera. Each of these organisms contains numerous alternative exons and can potentially synthesize tens of thousands of isoforms. Interestingly, most of the alternative exons in one species are more similar to one another than to the corresponding alternative exons in the other species. These observations provide strong evidence that many of the alternative exons have arisen by reiterative exon duplication and deletion events. In addition, these findings suggest that the expression of a large Dscam repertoire is more important for the development and function of the insect nervous system than the actual sequence of each isoform.

The Biology/Disease-driven Human Proteome Project (B/D-HPP): Enabling Protein Research for the Life Sciences Community

Abstract: The biology and disease oriented branch of the Human Proteome Project (B/D-HPP) was established by the Human Proteome Organization (HUPO) with the main goal of supporting the broad application of state-of the-art measurements of proteins and proteomes by life scientists studying the molecular mechanisms of biological processes and human disease. This will be accomplished through the generation of research and informational resources that will support the routine and definitive measurement of the process or disease relevant proteins. The B/D-HPP is highly complementary to the C-HPP and will provide datasets and biological characterization useful to the C-HPP teams. In this manuscript we describe the goals, the plans, and the current status of the of the B/D-HPP.

Probabilistic analysis of gene expression measurements from heterogeneous tissues

Abstract: Motivation: Tissue heterogeneity, arising from multiple cell types, is a major confounding factor in experiments that focus on studying cell types, e.g. their expression profiles, in isolation. Although sample heterogeneity can be addressed by manual microdissection, prior to conducting experiments, computational treatment on heterogeneous measurements have become a reliable alternative to perform this microdissection in silico. Favoring computation over manual purification has its advantages, such as time consumption, measuring responses of multiple cell types simultaneously, keeping samples intact of external perturbations and unaltered yield of molecular content. Results: We formalize a probabilistic model, DSection, and show with simulations as well as with real microarray data that DSection attains increased modeling accuracy in terms of (i) estimating cell-type proportions of heterogeneous tissue samples, (ii) estimating replication variance and (iii) identifying differential expression across cell types under various experimental conditions. As our reference we use the corresponding linear regression model, which mirrors the performance of the majority of current non-probabilistic modeling approaches. Availability and Software: All codes are written in Matlab, and are freely available upon request as well as at the project web page http://www.cs.tut.fi/~erkkila2/. Furthermore, a web-application for DSection exists at http://informatics.systemsbiology.net/DSection. Contact:timo.p.erkkila@tut.fi; harri.lahdesmaki@tut.fi

Prebiotic network evolution: six key parameters

Abstract: The origins of life likely required the cooperation among a set of molecular species interacting in a network. If so, then the earliest modes of evolutionary change would have been governed by the manners and mechanisms by which networks change their compositions over time. For molecular events, especially those in a pre-biological setting, these mechanisms have rarely been considered. We are only recently learning to apply the results of mathematical analyses of network dynamics to prebiotic events. Here, we attempt to forge connections between such analyses and the current state of knowledge in prebiotic chemistry. Of the many possible influences that could direct primordial network, six parameters emerge as the most influential when one considers the molecular characteristics of the best candidates for the emergence of biological information: polypeptides, RNA-like polymers, and lipids. These parameters are viable cores, connectivity kinetics, information control, scalability, resource availability, and compartmentalization. These parameters, both individually and jointly, guide the aggregate evolution of collectively autocatalytic sets. We are now in a position to translate these conclusions into a laboratory setting and test empirically the dynamics of prebiotic network evolution.