TMT Thesis Project

Thesis Topic

The main objectives of my work is to develop the tools to perform time-dependent stimulation and analysis of signaling pathways, and show that this is more powerful than traditional time-independent or step response analysis. I am using a computational model of the prototype system, the yeast pheromone response pathway, to generate hypotheses about the pathway. In order to test these hypotheses, time-dependent stimuli will be delivered to cells via a microfluidic device, and in vivo fluorescent reporters will be used to observe the system state. In addition to showing the strengths of this new approach to studying biological systems, I would like to use it to further our understanding of the pheromone response pathway.

Research Goals

My research can be broken down into 4 main goals that follow (for the most part) chronologically.

Build a model of the pheromone response pathway

• Develop a model of the pheromone response pathway that can be used in conjunction with time-dependent stimulation and analysis of the pathway to propose and test hypotheses. Once completed, this model can be used as a predictive tool for pathway response.
  • This model is largely already built (with instances in Matlab and Moleculizer). Also, using BioNetGen2 I have generated an SBML version of my model, which can be read as input by Jacobian, SloppyCell, and the SimBio toolbox in Matlab.
  • The model needs to be further refined using data from the literature, and data that I will generate myself.

Build a microfluidic device for time-dependent stimulation of cells

• Design, build and characterize a device to allow for rapid variation of extracellular conditions for cells fixed in a microfluidic channel.
  • This chip has been designed using the technology out of the Quake Lab at Stanford (formerly Caltech). See protocols for more info on chip design. Early versions of the chip (called the Stimulator) have shown great promise. Preliminary tests have shown that I can vary the extracellular environment (with NO cells in the channel) on a sub 100ms timescale. I've also successfully adhered cells to the bottom of the channel, and had them resist detachment under fluid flow, though this needs further characterization. I made a File:Cells in stimulator.avi with the most recent version showing that I can change the fluid environment of cells in the channel (video in real time, with food dye used to color one of the fluids). Please see the Stimulator page for the latest information.

Investigate the pathway with time-dependent stimulation

• Examine the frequency filtering characteristics of the pheromone response pathway in order to study the limits of propagation of time-varying signals through the pathway. Use the model to form and test hypotheses generated by studying the response of the pathway to time-dependent stimulation.
• Alternative approach would be to show that using time-varying stimuli increases parameter sensitivity, and that this leads to an improvement in parameter estimation. This is really just a specific instance of hypothesis testing (where the hypothesis is the particular parameter values).

Identify and apply techniques for non-linear system identification

• Identify and apply tools developed for other fields to the analysis of signaling pathways, particularly with respect to time-dependent stimulation. This can be divided into into two thrusts, parameter estimation and other analysis tools.
  1. Parameter Estimation
     • My first instinct was to try to do parameter estimation using Matlab. This turned out to not be sufficient for my purposes. See my notes on Parameter Estimation in Matlab.
     • I settled on using Jacobian for parameter estimation. The current version that I have is still somewhat buggy, but I have been assured by one of the lead guys working on Jacobian that they have corrected all the problems that I identified in the new release which is due out any day now. I'll update on this as soon as I get the new release.
  2. Model analysis tools
     • One basic analysis of a model is parameter sensitivity. Some people think that models should be robust to changes in parameters (reference to be filled in, since it's not cool to just state things and reference it blankly to 'some people'). I'm not so sure that is true, but either way the parameter sensitivity can in the very least tell you to what parameters your model's behavior is sensitive (critically depends on), and to what parameters it is insensitive (does not depend on).
     • At ICSB 2005 I discovered this great program called SloppyCell written by Ryan Gutenkunst in the Sethna lab at Cornell. Basically, SloppyCell calculates the parameter sensitivities, but does so for identifiable parameter groups. More details on this can be found in the original publication of the algorithm, and in my SloppyCell page.

Relevant questions

Q. What will determine if using time-varying stimuli is a success?
A. I think that it would be sufficient for me to show that you can get better parameter estimates using time varying stimuli than you can with step increase. When I say better estimate, I mean that we can decrease the error bounds on parameters. This hinges on some intelligent way to put bounds or confidence limits on parameters. This is probably linked to the independence/coupling of paramters topic listed below under Signal Design.

Q. I say that a time varying stimulus can drive a system to a state that it won't normally attain in response to a step increase stimulus. For what types of systems is this true?
A. I think that I can concoct systems that this is true for, but I should try to show that this is indeed true for the pheromone response pathway.

Near Future Plan

Biology

1. Show that cells can live on a chip
   • Stick yeast cells down in the channel, and flow media (at a slow rate) over them. Take a picture every 5 mintutes and compile into a movie of yeast cells growing (hopefully). Need to start with cells growing exponentially, and concentrate to OD 1.5-2. Try sonicating briefly to break up clumps (talk to Jeff).
   • I've proven to myself that cells can grow on a chip, though I still don't have a good movie demonstrating this. I will try to get this evidence so everyone will be a believer.
2. Show that you can control in ON/OFF fashion response of cells to alpha factor
   • Using strain with YFP driven by P_prm1 promoter. Show that cells won't react (ie fluoresce) when they are not in part of channel where alpha factor is flowing, and that they do react when they are exposed to alpha factor. I need to use casein in the media to block pheromone adsorption to the tubes and channel walls.
3. Find out if reset of receptor/G protein sub-system is limited by pheromone dissociation or Ste2 internalization.
   • Hit cells with a short dose of pheromone and see if reset is on the order of 4-5 mintutes (internalization) or 10 minutes (dissociation). See if Alejandro has already done this.
4. (Is yeast pheromone response the best model system for this project?)

Data Collection/Analysis

• Show that you can measure Ste5-YFP translocation to membrane
• This will involve either using or reimplementing the image analysis tools used by Alejandro and Andrew. Also, I might want to use/reimplement their autofocus routine. I should look into this soon.

Signal Design

Find out the extent of coupling and independence of parameters

• How can we use the model to get an idea of how well we're going to be able to estimate parameters? How many of the parameters are linked (eg, what if only the ratio of param1 to param2 matters)?
• SloppyCell appears to be useful for this purpose. I need to do some validation to prove to myself that SloppyCell is working as expected
  1. Compare species timecourses (vs Matlab, Jacobian & BNG2)to ensure input file and simulation engine are good.
     • DONE!
  2. Check sensitivities and normalized sensitivities to make sure they're as expected.
     • Can compare with Jacobian (generates automatically) and Matlab (need to make a numerical approximation).
       • So far, the sensitivities are not matching. I need to debug this. I'm waiting on hearing back from Ryan about some sloppycell questions.
  3. Interpret parameter groupings.
     • Should be able to compare the stiffness of the parameter groupings (as given by the relative eigenvalues) with the sensitivities of the individual parameters in that grouping. Higher eigenvalue/stiffness should correlate with higher sensitivity?

Parameter Estimation

1. Get jacobian working
2. Would knowledge of parameter groupings affect parameter estimation? I want to think about this some more, maybe chat with some people in the lab, and try to talk to John Tolsma (@Jacobian) about this.

Meta/Administrative Issues

Thesis Committee
Q1. Do we need Thorner on the committee?

• I think so.

Q2. Do we need a yeast person on the committee?

• Thorner

Q3. Do we need a dynamic systems person on the committee?

• It's looking more and more like the answer is yes. I don't know enough to be efficient at guiding myself through the parameter estimation and dynamic system analysis. Figuring out who we can add should be a top priority.
• People to talk to:
  • Jacob White
  • Alan Oppenheim
  • Paul Barton

Should have a committee meeting ASAP to discuss current directions.
Should have another committee meeting mid/late Spring 2006 to update and plan.

Decision Points/Milestones
Much of my research depends on several programs working correctly (Jacobian, SloppyCell). Since neither of these programs are in their final releases yet (and may still contain bugs), I need to set decision points by which I need to have complete confidence in the program. If the program is not working by this decision point, then I will find a new program/method.

Talk at the Alpha Project Retreat