Rapid Characterization of Cellular Pathways Using Time-Varying Signals
Ty M. Thomson and Drew Endy
Division of Biological Engineering, MIT
Contact:tmt@mit.edu
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.
0.2s
G
Ficarro, S. et al. (2002) Nat. Biotech. 20, 301-305
My Solution: Time Varying Stimuli
Time Varying Stimulation - Parameter Sensitivity
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.
Response to 1M pheromone:
simulated data, and model results
for Gpa1/Ste4-Ste18 dissociation
rate 10x too high.
Same as above plot, but data is
linearly scaled to match
simulation (since data in arbitrary
units).
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.
For several stimulus
time-courses, quantify
mean square error
Output Signal
y=L
Pheromone Concentration A
Direction of flow
y=L/2
Pheromone Concentrations B
y=0
x
Boundary
Layer Position
H
Does this data matter?
Input Signal
0.1s
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.
Informative given
current
understanding
0s
I
C
E
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.
D
B
Input: Microfluidics
Extracellular
Concentration
Current Methods of Characterization
• 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.
A
Biological System –
Yeast Pheromone Signal Transduction Pathway
0.3s
Yeast Cells
LL/20
0.4s
Time
BATime
Output: Reporters
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
1 mM F added at t=0 minutes
0 min
30 min
45 min
60 min
75 min
90 min
• 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
Time-Varying Input/Output
Input –
-fluidics
Input
Signal
Biological Output System
Reporters
Refine Model
Colman-Lerner (unpublished)
Output Signal
(Experimental)
Hypothesis
Testing
Pheromone/Ste2
dissociation rate
Computational
Model
Output Signal
(Simulated)
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.
Acknowledgements
Conclusions
Gpa1/Ste4-Ste18
dissociation rate
• Kirsten Benjamin
• Endy Lab
• Time Varying stimulus time courses show a definite improvement in
• Alejandro Colman• Molecular Sciences
parameter sensitivity over a step input, which should improve our
Lerner
Institute
estimation abilities for some parameters.
• Larry Lok
• Alejandra Torres
– Time-varying stimulation of a pathway will likely not increase sensitivity
• Jeremy Thorner
• Numerica Technology
enough for some other parameters to allow for accurate estimation
– John Tolsma
•
Todd
Thorsen
• 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 • Microfluidic Soft Lithography Foundry in fluidics, reporter technology, and hypothesis testing and nonhttp://nanofab.caltech.edu/foundry
linear parameter estimation as they pertain to cellular systems.
• Ficarro, S. et al. (2002) Nat. Biotech. 20, 301-
References
305
Future Directions
• Further characterize cell behavior in the microfluidic chip
environment.
• Begin to perform pheromone response experiments with chip.
Support
•
•
•
NHGRI Center of Excellence in Genomic
Science
CSBi Cell Decision Process Center
MIT Presidential Fellowship