1. Emmanuel Lorenzo de los Santos 20.309 Presentation October 10, 2008
The Frequency Dependence of Osmo-Adaptation in S. Cerevisiae (van Oudenaarden et al.)
Emmanuel Lorenzo de los Santos
Presentation
October 10, 2008

2. Outline Background and Motivation Description of the System Examined
Experimentation
Results: Modeling and Validation
Impact of the Research

3. Current Methods Used for Systems Biology are Limited
Understanding of biological systems is important for future engineering applications
Many biochemical pathways are complex
Big range in reaction times - <1s to >103 s
Can involve hundreds of reactions
Difficult to identify dominant processes
Many models incorporate all reactions but this has problems

4. Solution: Propagation of oscillating signals through a cascade
Varying frequencies of input can give us insight into system dynamics
Frequency response time can provide new direction for research
Dominant processes can be easily identified by comparing wt vs. mutant system dynamics

5. Hog1-MAPK in S. cerevisiae is an ideal test system for this approach
High Osmolarity Glycerol (HOG) MAP Kinase cascade is involved in hyperosmotic response
Easy to measure:
Input: NaCl Concentration
Output: Hog1 Response
Well studied components – validation
Negative feedback loops on different time scales
(de Nadal et al., EMBO Reports 2002)

6. The Experiment
Shock cells with square wave pulses of medium with and without 0.2 M NaCl (To=2min-128min)
Two cells, wt and MAPKK deficient
Measure Cell Response – Hog1 Activation
(Mettetal et al. , Science 2008)

7. Measurement Tools Input – Known Output – use colocalization:
Hog1-YFP
Nrd1-RFP (localized in nucleus)
Measure <YFP>nuc/ <YFP>cell
Approximate Input and Output as sine waves:
To= , ω0 is the driving frequency
Perform Fourier analysis on output

8. Results: LTI-Nonlinear model predicts Amplitude and Decay Time
No biological concepts involved yet!
(Mettetal et al. , Science 2008)

9. LTI Model Compared to Standard Biological Model
LTI-Nonlinear Model Translated into two State Mechanistic Model:
Biological System: parallel mechanisms
Fps1 – Hog1 independent
Hog1 dependent
Good fit but dynamics suggest incomplete model
(Mettetal et al. , Science 2008)

10. System Dynamics suggests further experiments
Response generally over in 15 minutes – too fast for gene expression based response
Test cell recovery while inhibiting protein expression
Response to initial pulse is similar
Different response in later pulses
Longer frequency components discovered through system dynamics analysis
(Mettetal et al. , Science 2008)

11. Oscillation response is a valuable tool in analyzing biological systems
Determine dominant interactions in complex biochemical pathways
Information on system dynamics leads to better understanding of the system as opposed to other methods (e.g. microarrays)
Can be used in many pathways

12. Non-Linear Part