1. The Value of Tools in Biology
Smolke Lab talk
Eric Caltech

2. Framework
Thesis: our ability to understand and manipulate biology is limited by the quality and scope of our tools
cellular understanding - what determines the cell's behavior?
cellular manipulation - how can we control the cell's behavior?

3. Quantizing Biology
cellular behavior is determined by physical properties and their variation in time:
Structures
Locations
Energies
Numbers
Cellular processes often manipulate these quantities in tandem

4. Natural Systems
For example, transcriptional processes separate mechanisms for controlling protein (Number) vs (Structure):
Structure then determines the protein’s Location and Energies, and thereby its function

5. Independence of Tools
If we could manipulate cellular quantities independently, then more states would be reachable.
Analogy: like building a house with (nails, a hammer, and a saw) vs with a (nails-hammer-saw)
We can reappropriate natural systems for our own purposes, but their independent use is limited.
Example: PCR borrows from the transcriptional network. Some sequences of DNA are difficult to amplify.
Complete independence is not always possible
Example: the necessary connection between protein Structure and Energy, which limits functions.

6. A closer look at Number
Control over protein number is affected by cellular noise sources
Extrinsic noise: variation in environmental conditions. (temperature, nutrients, signals)
Intrinsic noise: follows from the stochastic nature of protein formation
Laboratory experiments often focus on reducing extrinsic noise
Repeated trials reduces measurement variance

7. A Simple model Protein produced at an average rate of λ proteins/sec
No RNA, no protein decay
Instrinsic noise is the single cell probability distribution
Extrinsic noise is the sum of many cellular distributions

8. Adding the effects of Translation
Translation efficiency is a major source of noise
variance of many small steps is less than that of fewer large steps
Translation amplifies transcriptional variation in addition to adding noise
Ozbodak PMID: 

9. Qualities of protein Number
Mean and the Variance are both important for cellular behavior
Example: robustness
Mean influences most probable action
Cellular robustness through error control averaging
Variance influences probability of alternative actions
population robustness through diversity

10. Independent control of protein Number
Goal: control over the mean and variance of cellular protein
Mean controlled by protein production rates
Variance controlled by feedback on rates
negative feedback on protein production reduces variance
More protein  lower rate  less production  less protein
Less protein  higher rate  more production  more protein

11. Protein Auto-regulation
Transcriptional feedback: production of a repressor that inhibits transcription
Becskei PMID: 
Translational feedback: production of a protein that decreases RNA stability
More efficient at reducing relative variance
Higher metabolic cost
Swain PMID: 

12. A Physical Feedback Mechanism
Translational regulation via modulation of RNA decay rate
RNA degraded though endogeneous endo/exo-nuclease pathways in E. Coli
5’ and 3’ hairpins increase the stability of RNA

13. RNA modulation
Removal of protective hairpins decreases stability of RNA transcript  less protein produced
Yeast Rnt1p cleaves RNA hairpins with high sequence specificity
Express Rnt1p from the protected RNA transcript, closing the feedback loop
Possibility of an orthogonal, modular feedback system

14. RNA hairpin substrate specificity
Rnt1p recognizes sequence dependent domains
E. Coli RNaseIII also cleaves dsRNA with some sequence dependence
Goal: high Rnt1p activity, low E. Coli RNaseIII activity
Orthogonal system
Lamontagne PMID: 

15. System Modularity Independence of functional parts:
5’ and 3’ protective hairpin sequences determine lifetime  control of protein number mean
Rnt1p hairpin sequence determines level of feedback  control of protein number variance
Hairpin libraries  tuning of variance and mean

16. Correlated Expression of YFGOI
Polycistronic coding regions have correlated expression levels
Express any other protein on the same transcript
Use GFPuv for testing purposes
Additional correlation if using same RBS

17. Controls Open loop system: Rnt1p on separate plasmid  no feedback
Test for Rnt1p substrate cleavage and RNA destabilization after the expression of Rnt1p
Test for no destabilization with non-active Rnt1p hairpins
Test for no destabilization without Rnt1p hairpins
Test for no destabilization without protectice 5’ and 3’ hairpins
With additional combinations for individual 5’ vs 3’ testing if necessary

18. Applications of controlled variance
Any decision can be modelled as maximizing over some Utility function
Cells make decisions to express or not express a specific protein with a certain probability
Rewarded if choice is correct
Penalized if choice is incorrect
Engineering systems have their own Utility functions

19. Low Number protein expression
Proteins toxic in large numbers
Low number expression is difficult, due to relatively high variance at small N
Variance control through feedback provides higher net population fitness

20. Signal Rectification
Electronic Digital circuits scale well due to voltage rectification after every computation
In contrast, in electronic Analog circuits, errors can propogate and amplify uncontrollably
Chemical rectification may be a useful method for reducing error propogation between separate circuit elements
Allowing for larger, more complicated synthetic circuits and computations

21. Measurement Probe
Remember that every measurement is actually the result of many individual measurements of individual cells
Reducing intrinsic cellular noise increases the accuracy of measurements

22. Conclusions
Tools for Independent manipulation of cellular quantities are intrinsically useful
Negative Feedback as a method for control of number variance
Modular Rnt1p system for orthogonal control of protein variance in E. Coli
Circuit designs using low variance systems

23. Future plans
Cloning
More cloning…