PNNL's multicellular signaling networks research is strengthened by our signature capability to process high-throughput, high-dimensional biological data. Shown above is an interaction network of 169 genes/proteins regulated during the first 4 hours of EGFR activation in human mammary cells. Results were obtained by integrating microarray, proteomic, and Western blot data (Powerblot). Network inference was conducted using MetaCore software. Click for a larger version.
Pacific Northwest National Laboratory's (PNNL) extensive systems biology experience, world-class proteomics facilities, advanced imaging techniques, and sophisticated computational and bioinformatics tools have enabled us to effectively perform systems biology studies at the subcellular and cellular level. We are now pushing the envelope of systems biology by taking the next step: understanding multicellular signaling networks.
Understanding multicellular systems is an imperative element for adapting systems biology studies to medical applications. For example, such studies are necessary to understand host-pathogen interactions – both how cells on the defensive front lines interact with an invader and how different host cell types involved in the inflammatory cascade triggered by an infection interact with one another. Studying signaling among different cell types in a single organism is also important for understanding diseases and the processes involved in disease, such as metastasis.
PNNL's strategy to study multicellular networks includes expanding the capabilities of analytical and computational tools, coupling the development of both modeling and experimental approaches, and reducing the enormous complexity of biological organisms to simplest terms. This strategy, which is heavily dependent on data analysis, is strengthened by our signature capability to process high-throughput, high-dimensional biological data. Per our systems biology experience, our approach to study multicellular networks is based on lessons learned:
- The usefulness of high-throughput data can be greatly increased by integrating multiple data types that are obtained in parallel on the same system.
- Signaling networks can be accurately modeled as a series of functional modules instead of networks of individual interactions.
The Functional Module Approach
PNNL's approach to modeling multicellular networks reduces the complexity of cell signaling networks by describing them in terms of functional modules, rather than individual molecular reactions. Via this approach, researchers will be allowed to follow the flow of information among multiple cell types and connect molecular events to overall system behavior.
Our functional module strategy uses a top-down perspective. Biological systems are examined as an input-output system. A system of interacting cells is represented as a hierarchy of progressively more refined modules, such that each module is based on the functional behavior of a small part of the overall network. For example, we are working to identify the modules and feedback circuits associated with specific cell functions, such as cell proliferation. Each module identified as part of the cell proliferation process can then be independently refined to a greater level of detail.
In general, our approach using modules to build predictive models follows.
- Consider a biological system as an input-output system.
- Collect baseline, multidimensional data on the overall system.
- Analyze the data in the context of the literature and current databases to identify functional modules.
- Construct a high-level model based on tentatively identified functional modules.
- Make testable predictions of outputs in response to specific inputs and test those predictions with experiments specifically designed to enable step 6.
- Use differences between predictions and experiments to refine the original model, adding more modules when necessary.
- Repeat 5.
PNNL's Multicellular Networks Research Efforts Are Supported by a Variety of Projects
Ongoing projects at PNNL are being used to explore and validate our strategy for multicellular modeling. We have years of experience with the model systems that are being used for these projects, and they are physiologically relevant. In addition, we have performed extensive characterization of them using both gene microarray and proteomics studies.
Intercellular and Intracellular Communication in the Human Mammary Gland - Using the functional modules approach, PNNL systems biologists are in the initial stages of building a model that describes how human mammary epithelial cells and stromal fibroblasts interact.
Identifying Targets for Therapeutic Interventions of Salmonella Bacteria and Orthopox Viruses Using Proteomic Technology - Researchers at PNNL are investigating the interaction of Salmonella with susceptible and resistant macrophages.
Functional Interactions Between Macrophage and Epithelial Cells - PNNL researchers are studying the interaction of activated macrophages with epithelial cells that leads to inflammatory cytokine production.