Download cellular understanding

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 (nailshammer-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/exonuclease 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
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
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Cloning
Cloning
Cloning
Cloning
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