A New Systems Strategy to Study Cell Surface Receptor Dynamics
A novel computational approach to systems biology research
Results: Scientists at Pacific Northwest National Laboratory have developed a new computational approach for comparing receptor networks in cells. This new approach is based on systems science concepts and can be applied to a wide range of biological problems. A simple but realistic mathematical model was created and then used to explore the design principles of signal transduction and transport receptors. Results, which were published in the June 1 issue of PLoS Computational Biology, have implications for how evolutionary pressures can improve the function of receptor systems by selectively optimizing a few fundamental dynamical parameters.
"Biological systems are shaped by evolution," says senior author Steven Wiley. "Traditionally, it's been difficult to translate this conceptual framework into quantitative terms, which is necessary so that we can use computers to help understand design principles. This work provides one conceptual framework by which to do this."
Why it matters: Cells communicate with their environment through molecules on their surface known as receptors. Receptors bind ligands—specific companion molecules that either carry information about the outside environment or are critical cell nutrients. A variety of receptors are internalized into the cell through a process known as endocytosis. Receptors display a wide range of state-dependent endocytosis rates, but the functional significance of these patterns was not understood.
Methods: Researchers Harish Shankaran, Haluk Resat and Wiley paired a new module-based systems theory approach with quantitative metrics for network function to show that receptor/ligand properties such as endocytosis can be described by just a few control parameters. Using mathematical analysis, they showed that receptor system efficiency and robustness are encoded by two fundamental parameters: the avidity, which quantifies the ability of a receptor system to capture ligand, and the consumption, which quantifies the ability to internalize bound ligand (see figure).
This figure illustrates how each control parameter for epidermal growth factor receptor (EGFR), transferrin receptor (TfR), low-density lipoprotein receptor (LDLR) and vitellogenin receptor (VtgR) can be identified based on its ability to move the system in the appropriate direction in the parameter space. The arrow lengths provide a sense of which receptors are the most sensitive to parameter changes. Sensitivity is in the order TfR > LDLR > EGFR > VtgR. Solid arrows indicate the effect of increasing the control parameter, while the broken arrows show the effect of decreasing the parameter. The contours demarcating the most robust (ms < 0.8) and the most sensitive (ms > 1.2) regions of the parameter space are indicated in gray. Enlarged View
By examining a number of receptor systems (see figure), the researchers demonstrated that receptor system response can be characterized as being either avidity-controlled, which depends primarily on ligand capture efficiency; consumption-controlled, where the ability to internalize surface-bound ligand is the primary control parameter; and dual-sensitive, in which both the avidity and consumption parameters are important. The location of various receptor systems in control parameter space dictates their specific function and regulation.
Most significantly, the researchers believe that the evolution of a given receptor system can be understood in terms of its optimal location in avidity-consumption parameter space. For example, induced endocytosis can be shown to be an optimal solution for transmitting high-fidelity information by signaling receptors.
What's next: In a related article not yet published, the same team of researchers shows that biological signaling networks can be understood in the context of human-engineered electrical and mechanical systems. By analyzing a widely used receptor signaling module, they show that the module behaves like an electronic low-pass filter with a definite frequency bandwidth or as a damped mechanical oscillator. Even though the dynamical parameters may be quite specific to a particular receptor system, the underlying structure of the module is essentially the same for different signaling receptors.
Acknowledgments: The research was funded by the National Institutes of Health and the Biomolecular Systems Initiative at PNNL.
Reference: Shankaran H, H Resat, and HS Wiley. 2007. "Cell surface receptors for signal transduction and ligand transport - a design principles study." PLoS Computational Biology doi:10.1371/journal.pcbi.0030101.eor.