Experimental data on gene transcriptional regulation are distributed throughout many and various databases and datasets. Currently, no resource exists that would automatically integrate these data and provide a one-stop shop experience for the user seeking for retrieving experimental and predicted information on transcription factors and gene regulatory regions — information essential for deciphering and modeling gene regulatory networks. Read More
Several structural genomics groups from NIH sponsored Protein Structure Initiative (PSI), including the Joint Center for Structural Genomics (JCSG) from UCSD and Burnham Institute for Medical Research has compiled a large protein structure dataset, which was constructed very carefully and selectively; that is, the dataset contains only experimentally determined structures of proteins from one specific organism, the hyperthermophilic bacterium Thermotoga maritima, and those of close homologs from mesophilic bacteria. In contrast to the conclusions of previous studies, the analyses show that oligomerization order, hydrogen bonds, and secondary structure play minor roles in adaptation to hyperthermophily in bacteria. On the other hand, the data exhibit very significant increases in the density of salt-bridges and in compactness for proteins from T. maritima. The latter effect can be measured by contact order or solvent accessibility, and network analysis shows a specific increase in highly connected residues in this thermophile. These features account for changes in 96% of the protein pairs studied. The results provide a clear picture of protein thermostability in one species, and a framework for future studies of thermal adaptation.
Adam Godzik’s lab at the Burnaham Institute of Medical Research is currently constructing a “big picture” of metabolic processes in Thermatoga and in othe bacterial genomes and assimilating protein architectural information flowing from Structural Genomics (SG) efforts. protein architectural information flowing from SG has not been assimilated into mainstream research as rapidly and as widely as that generated by traditional structural biology. We believe the reason for this unanticipated situation is that, unlike traditional structural biology, structure determination at SG centers is inevitably not always – nor even routinely – accompanied by a local stream of connected, synergistic biochemical and biological research. Consequently, the vast majority of protein structures determined by SG centers lack these complementary details and are not described in high impact, peer-reviewed manuscripts, the principal way by which scientists communicate. Instead, the end result of the work of a SG center is usually a set of coordinates deposited in the PDB, information that is not readily assimilated by a typical biologist and opportunities are likely often missed since the scientific application is not recognized. As a result, data from structural genomics is only very slowly absorbed into the wider research stream, largely as correlated experimental data arises.
Gene regulatory networks provide insight into the mechanisms of differential gene expression at a system level. However, the methods for inference, functional analysis and visualization of regulatory modules and networks require the user to collect heterogeneous data from many sources using numerous bioinformatics tools. This makes the analysis expensive and time-consuming. In this work, the BiologicalNetworks application -the data integration and network visualization environment – was extended with tools for inference and analysis of gene regulatory modules and networks. The backend database of the application integrates public data on gene expression, pathways, transcription factor binding sites, gene and protein sequences, and functional annotations. Thus, all data essential for the analysis can be mined publicly. In addition, the user’s data can either be integrated in the database and become public, or kept private within the application. The capabilities to analyze multiple gene expression experiments are also provided. The generated network, regulatory modules and binding sites can be visualized and further analyzed within this same application.
The developed tools were applied to the OCT4 regulatory network in embryonic stem cells.
Driving Projects are research projects that are selected for their scientific merit in answering important biological questions and that represent a broad range of research endeavors, advancing their disciplines. Driving Projects stimulate the BiologicalNetworks project to improve its technologies and provide feedback on our work. How to become a Driving Project.
List of current and past Driving Projects (ordered by activity):
1. Transcriptional regulation database, (UCSD)
2. Brain Development study, (UCSD and Institute Pasteur / College de France, France)
3. Whole Genome Metabolic Reconstruction, (UCSD)
4. Senescence and apoptosis, (UPenn / The Wistar Institute, Philadelphia, PA)
5. Autism study, (UCSD and Institute Pasteur / College de France, France)
6. G protein-coupled receptors as novel targets for drug discovery in cancer, (The Scripps Research Institute, La Jolla, CA)
7. Integrative analysis of embryonic stem cells regulation by OCT4 family of mammalian transcription factors , (UCSD)
8. Drosophila melanogaster stem cells regulation by ESCARGOT transcription factor family, (SALK Institute, La Jolla, CA)
9. Gene regulatory Map of Asthma, (UCSD)
10. Host-pathogen interaction resource , (UCSD)
11. Human – Influenza interaction , (Institute of Health, Madrid, Spain)
12. Apoptosis in human lymphocytes , (Institute of Health, Madrid, Spain)
13. Reconstruction of Lactococcus garivieae metabolic and gene regulatory network, (Institute of Health, Madrid, Spain)
14. NF-kappaB signaling, (UCSD)
15. Pathway mapping of molecular off-targets for drug discovery, (UCSD)
16. Systems pharmacology tools for drug targets, off-target pathways discovery and safety biomarkers, (Q-Mol, LLC, San Diego, CA)
17. Metabolic Pathways mapping of microarray experimental data in buffalo and cow, (National Bureau of Animal Genetic Resources (NBAGR), India)
18. Human hypertension, (UCSD)
19. Brain Injury study, (University of Florida, Gainesville, FL)
20. Parasite studies (Life cycle of Giardia parasite), (UCSD, School of Medicine)
21. Microbial metabolism studies (Thermatoga metabolism), (Sanford-Burnham Medical Research Institute, La Jolla, CA)