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Please also visit the Dahlquist Lab Research page at Loyola Marymount University.


Schedule Summer 2011

Lab Meetings

  • Monday, May 16, 12:00: Overview of summer research
  • Monday, May 23, 12:00:
    • Katrina/Nick Journal Club: Yang et al. (2002) Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. Nucleic Acids Research 30: e15.
    • Katie/Andrew Journal Club: Schade et al. (2004) Cold Adaptation in Budding Yeast. Molecular Biology of the Cell 15: 5492–5502.
  • Tuesday, May 31, 12:00:

Overview of Projects

The Dahlquist Lab performs four distinct, but related research projects. The common thread amongst these projects is that they employ the techniques of bioinformatics and genomics and the perspective of systems biology. All of these projects involve undergraduate or Master's level students at LMU and most involve interdisciplinary collaborations with other faculty at LMU (see: People). These research projects have also been brought into the classroom (see: Courses).

  1. XMLPipeDB: A Reusable, Open Source Tool Chain for Building Relational Databases from XML Sources; creating MAPPs and Gene Databases for the GenMAPP software
  2. The Global Transcriptional Response of Saccharomyces cerevisiae to Cold Shock and Recovery
  3. Mathematical Modeling of the Transcriptional Network Controlling the Environmental Stress Response in Saccharomyces cerevisiae
  4. Identifying Soil Bacteria and Biochemical Pathways in the Ballona Wetlands for the Bioremediation of Organic Pollutants

Development of Bioinformatics Tools


XMLPipeDB is an open source suite of Java-based tools for automatically building relational databases from an XML schema (XSD). XMLPipeDB provides functionality for managing, querying, importing, and exporting information to and from XML data with minimum manual processing of the data. While its applicability is fairly general, the original motivation for XMLPipeDB was to create a solution for the management of biological data from different sources that are used to create Gene Databases for GenMAPP.


GenMAPP (Gene Map Annotator and Pathway Profiler) is software for viewing and analyzing DNA microarray and other genomic and proteomic data on biological pathways.

Understanding Yeast Gene Regulatory Networks

Mathematical Modeling of Yeast Gene Regulatory Networks

Gene expression is a complex biological process in which cells first transcribe their genes encoded in the DNA into an intermediary known as mRNA. Then the cell translates the mRNAs into proteins. Transcription factors are regulatory proteins which increase or decrease the rate at which a cell transcribes a gene. Recently, genome-wide location analysis has determined the relationships between transcription factors and their target genes on a global scale in budding yeast, Saccharomyces cerevisiae (Lee et al., 2002 Science 298:799; Harbison et al., 2004 Nature 431:99). While these data have identified properties of the network topology, they do not reveal the dynamics of the behavior of the network. Using differential equations, we have modeled how the concentrations of proteins in the cell change over time for a subset of a real gene expression network of twenty-one transcription factors controlling the environmental stress response in yeast. The differential equations governing the rate of change of concentration for each protein in the network were based on a sigmoidal function. A weight parameter determines how each transcription factor affects the transcriptional and translational rate of its target gene. The weights were optimized to experimentally derived gene expression data from yeast exposed to the environmental stress of cold shock. Sensitivity analysis was performed to understand the behavior of the different parameters in the model. We then used the model to generate a simulated gene expression dataset giving the steady-state concentrations of each protein after a period of time has elapsed. The simulated data determined which transcription factors have a greater impact on the overall dynamics of the network. Then each gene in the network was systematically deleted in silico to determine how the steady-state concentrations of the proteins in the network changed after the deletions.

  • Click here to see work in progress on this project.
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