The Value of Tools in Biology

Smolke Lab talk 11-1-06

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?

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

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

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.

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

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

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: 11967532

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

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

Protein Auto-regulation

• Transcriptional feedback: production of a repressor that inhibits transcription

• Becskei PMID: 10850721

• Translational feedback: production of a protein that decreases RNA stability– More efficient at reducing

relative variance– Higher metabolic cost

• Swain PMID: 15544806

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

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

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: 14581474

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

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

Controls• Open loop system: Rnt1p on

separate plasmid no feedback1. Test for Rnt1p substrate

cleavage and RNA destabilization after the expression of Rnt1p

2. Test for no destabilization with non-active Rnt1p hairpins

3. Test for no destabilization without Rnt1p hairpins

4. Test for no destabilization without protectice 5’ and 3’ hairpins

– With additional combinations for individual 5’ vs 3’ testing if necessary

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

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

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

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

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

Future plans

• Cloning

• Cloning

• Cloning

• Cloning

• More cloning…