Biological System – Yeast Pheromone Signal Transduction Pathway Rapid Characterization of Cellular Pathways Using Time-Varying Signals Ty M. Thomson and Drew Endy Division of Biological Engineering, MIT Contact:tmt@mit.edu Input: Microfluidics Introduction The use of traditional tools for the discovery and characterization of biological systems has resulted in a wealth of biological knowledge. Unfortunately, only a small portion of the biological world is well-understood to date, and the study of the rest remains a daunting task. This work involves using time-varying stimuli in order to more rapidly interrogate and characterize signaling pathways. The time-dependent stimulation of a signaling pathway can be used in conjunction with a model of the pathway to efficiently evaluate and test hypotheses. We are developing this technology using the yeast pheromone signal transduction pathway as a model system. The time-varying stimuli will be applied to the yeast cells via a novel microfluidic device, and the pathway output will be measured via various fluorescent reporters. The output of the pathway can then be compared to the output from a computational model of the pathway in order to test hypotheses and constrain our knowledge of the pathway. Initial work shows that a computational model can be used to identify stimuli time-courses that increase the parameter sensitivity, meaning that corresponding experiments could potentially be much more informative. Biological System – Yeast Pheromone Signal Transduction Pathway A microfluidic device was designed, and subsequently manufactured out of PDMS, to allow for time varying stimuli to be applied to cells anchored in a channel. On-chip valves allow for rapid control of the flow rates of two fluids through a single channel. By varying the flow rates, the position of the boundary layer between the fluids is altered, exposing cells anchored in the channel to varying extracellular environments. The pheromone response pathway is an obvious model system to use. It is a well-studied prototype for regulatory networks that govern response to external stimuli in higher eukaryotes. It contains many common elements of signaling pathways (MAPK cascade, G protein, etc.) A number of stimuli and reporters are now available for this pathway, including specific inhibitors for several kinases. 0s Traditional biochemistry and genetics proceed essentially one protein at a time through a biological system. This leads to tediously slow progress at discovering and describing systems. With only a few pathways well-characterized so far, the discovery and characterization of the rest of the biological world remains a daunting task. Recently, system-independent and system-wide technologies have been developed and applied to increase the rate of biological systems discovery. D B C E I A H G F However, system-independent technologies often produce heaps of “low grade” data. For example, to the left is mass spectrometric data of phosphorylation sites in the yeast proteome, only a small portion of which we can make sense of. Ficarro, S. et al. (2002) Nat. Biotech. 20, 301-305 Informative given current understanding Does this data matter? Current Methods of Characterization 0.1s 0.2s Time Varying Stimulation - Parameter Sensitivity y=0 y=L x Direction of flow Pheromone Concentration A Pheromone Concentrations B Extracellular Concentration B- A- Time y=L/2 Boundary Layer Position L- L/2- Yeast Cells Response to 1M pheromone: simulated data, and model results for Gpa1/Ste4-Ste18 dissociation rate 10x too high. In general, the output of a given experiment or simulation will depend critically on certain parameters, and depend weakly on others. Fitting parameters in our model to experimental data gives us strong data for some parameters (on which the results critically depend) and weak data for others. We are using a computational model of the pathway to identify stimulus time-courses that have the greatest potential to produce highly informative experimental results. 0.3s Same as above plot, but data is linearly scaled to match simulation (since data in arbitrary units). 0.4s My Solution: Time Varying Stimuli Output Signal Input Signal More information in  more information out? Stimulating a pathway with a time-varying input might Push the system into states in which it wouldn’t normally exist Result in more complex behavior Response to two 1M pheromone pulses (at 0-10s and 190-200s): simulated data (scaled), and model results for Gpa1/Ste4-Ste18 dissociation rate 10x too high. 0 min 30 min 45 min 60 min 75 min 90 min 1 mM F added at t=0 minutes Several genetically encoded fluorescent reporters exist for the pathway. Ste5-YFP (membrane translocation of the MAPK scaffold protein) Ste12/Dig1 FRET pair (a transcription factor and its repressor) Ste12-activated gene expression Colman-Lerner (unpublished) Output: Reporters For several stimulus time-courses, quantify mean square error Time-Varying Input/Output Input – -fluidics Biological System Output - Reporters Output Signal (Experimental) Conclusions Acknowledgements Kirsten Benjamin Alejandro Colman-Lerner Larry Lok Jeremy Thorner Todd Thorsen Endy Lab Molecular Sciences Institute Alejandra Torres Numerica Technology – John Tolsma Time Varying stimulus time courses show a definite improvement in parameter sensitivity over a step input, which should improve our estimation abilities for some parameters. Time-varying stimulation of a pathway will likely not increase sensitivity enough for some other parameters to allow for accurate estimation Controllable microfluidics is a practical method for controlling the fluid environment of immobilized yeast cells on sub-second timescales. My technology platform will improve and scale along with advances in fluidics, reporter technology, and hypothesis testing and non-linear parameter estimation as they pertain to cellular systems. Hypothesis Testing Input Signal Refine Model Pheromone/Ste2 dissociation rate Gpa1/Ste4-Ste18 dissociation rate References Microfluidic Soft Lithography Foundry - http://nanofab.caltech.edu/foundry Ficarro, S. et al. (2002) Nat. Biotech. 20, 301-305 Output Signal (Simulated) Computational Model Future Directions Support Signal Design Use microfluidics to stimulate the system with an information-rich, time-dependent signal in order to observe more varied pathway behavior. Measure the states of various reporters over time. Evaluate hypotheses by comparing computational results with experimental results. Test inferences and improve understanding by selecting a new input signal and repeating the process. Further characterize cell behavior in the microfluidic chip environment. Begin to perform pheromone response experiments with chip. NHGRI Center of Excellence in Genomic Science CSBi Cell Decision Process Center MIT Presidential Fellowship