The Value of Tools in Biology Smolke Lab talk

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:

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: Translational feedback: production of a protein that decreases RNA stability –More efficient at reducing relative variance –Higher metabolic cost Swain PMID:

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:

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 feedback 1.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 More cloning…