At PNNL, we are working to understand the underlying structure of cell signaling networks and how changes in these networks can affect the transmission of information. Cellular proteins, shown in red and green, can be visualized, identified, and quantified using techniques such as microscopy, mass spectrometry, and high-throughput enzyme-linked immunosorbent assay (ELISA). Cell nuclei are labeled blue.
All living cells sense and respond to their environment by a set of mechanisms known as cell signaling – part of a complex system of communication that governs basic cellular activities and coordinates cell actions. To understand cell behavior, we must understand cell signaling. Although the tools of molecular biology have revealed many of the genes and proteins involved in cell signaling, we still lack a deep understanding of how it actually works.
All living cells must respond appropriately to their environment, whether they live freely in the soil or are part of a tissue. Indeed, cell communication is necessary for the existence of multicellular organisms. The ability of cells to perceive and correctly respond to their microenvironment is the basis of development, tissue repair, and immunity as well as normal tissue homeostasis. Errors in cellular information processing are responsible for diseases such as cancer, autoimmunity, and diabetes. We want to understand cell signaling enough to treat these diseases effectively and, potentially, to build artificial tissues.
Cells receive information from their environment through a class of proteins known as receptors. The information is then processed through signaling pathways and decoded in the nucleus and other areas of the cell. To understand cell signaling, we need to understand the spatial and temporal dynamics of both receptors and the components of signaling pathways. We need to know what parts are actually present in a given cell, where the parts are located, and what the parts are doing.
Traditional work in biology has focused on studying individual parts of cell signaling pathways. However, we need to understand the underlying structure of signaling networks and how changes in these networks can affect the transmission of information. To accomplish this task, new technologies and approaches are needed. This is a major focus of the systems biology program at Pacific Northwest National Laboratory (PNNL).
Generating the Data to Understand Cell Signaling
One of the greatest challenges in understanding cell signaling is being able to follow the flow of information through cells. Information is encoded in many different molecules, all found at vanishingly small amounts in cells. Classic "second messengers," such as cAMP, can be followed using sensitive analytical tools. However, much of the information content of cells is encoded in the modification state and activity of key proteins. Data-driven projects provide a relatively unbiased approach to establish the cell components that are engaged in signaling processes. Other projects are focused on understanding specific mechanisms that regulate the flow of cellular information.
Quantitative Characterization of Post-Translational Protein Modifications Using Mass Spectrometry – Mass spectrometry-based approaches are being used to follow the state of modification of dozens of signaling proteins simultaneously.
Mechanisms of Regulated Ligand Shedding – Researchers are following the complement of proteins that are being secreted in response to cell stimulation.
Regulation of Cell Surface Ligand Dynamics – Scientists are investigating the mechanisms that control the release of growth factors.
Transmodulation of Cell Responses – The molecules that coordinate signaling between different signaling pathways are being identified.
Integrating Heterogeneous Data Sets
A systems-level approach to understanding cell signaling requires large amounts of data because of the complexity of signaling networks. Current high-throughput technologies generate relatively noisy data that must be extensively processed to yield interpretable results. A powerful approach to improving the quality of experimental results is to integrate multiple types of data. Because the inherent biases of different techniques are unlikely to be the same, combining different types of data will tend to cancel out experimental noise. In addition, multiple types of data collected on the same system will provide a more complete picture of what is actually happening, reducing the chances of misinterpretations. Unfortunately, there is currently no standard way to combine different types of data. PNNL scientists have tackled this challenging problem and are building software tools that will allow biologists to explore different ways to combine their experimental data. The most fundamental task is to collect and store different data types in a form that facilitates their integration.
Integrated Data Structures for Mapping Cellular Networks – Researchers are developing a database to store, organize, and manage large and divergent sets of information, while providing links to the bioinformatic and computational tools needed to interrogate the data across multiple experiments and experimental approaches.
Complex Queries – To access biological data, bioinformaticists are building a system to support metadata tracking and data retrieval in a structured framework.
Bioinformatics Resource Manager – The database tools developed in the above projects are integrated in the Bioinformatics Resource Manager, which is designed for automated data gathering, interfacing between data storage and analysis programs, and linking data between applications.
Data Integration and Pattern Recognition – New bioinformatic approaches for integrating and analyzing high-throughput data, such as microarray and proteomic data sets, are being developed, as are novel visualization tools to facilitate comprehension of extremely large data sets.
Modeling Signaling Networks
Data gathered from cell signaling studies are being used to build mechanistic models of the process of signal transduction. Data generated at PNNL are also being used by investigators in the Receptor Tyrosine Kinase Consortium to build predictive models of this important cell signaling pathway.
Software Environment for BIological Network Inference (SEBINI) - PNNL's SEBINI project team has created a software platform for the inference of (1) genetic regulatory networks from high-throughput microarray, messenger RNA (mRNA) expression data; (2) protein regulatory networks from high-throughput, protein abundance data; and (3) protein signaling networks from protein activation state data. The algorithms within SEBINI use correlations in gene expression, protein abundance, and protein activation state to infer direct regulatory connections between genes or proteins. With these tools, scientists are able to rapidly reconstruct biological regulatory networks with greater ease and accuracy.
Crosstalk Among Receptor Signaling Pathways – The dynamic behavior of both positive and negative regulatory components of signaling pathways is being modeled to understand how information is integrated by cells.
The following project is supported by the National Institutes of Health.
Modeling Cell Receptor Signaling Pathways – High expression levels of epidermal growth factor receptor (EGFR) and HER-2 have been observed in human cancers, particularly breast and ovarian cancers. Overexpression of HER-2 has been implicated in cancer promotion and HER receptors are believed to contribute to cancer development. Researchers at PNNL are systematically investigating how the interactions between EGFR and other members of the HER family modify each other's signaling behavior.