1. Introduction to Synthetic Biology II Jonathan Foley //Josh Kittleson // Emily Pertu // Lane Weaver

2. Opportunities for Synthetic Biology Is evolution the ultimate engineer? i.e., if we have a desired function in mind (e.g. pattern formation), can we develop a better architecture than nature has stumbled upon via random mutations and reshuffling? (random walk vs. rational approach) Even if we can’t beat nature in the engineering game, there are still plenty of novel opportunities for a (semi)-rational approach. For instance, natural selection never asked bacteria to make artemisinin, but we have….

3. Synthetic Design and Modeling Due to their complex, dynamical nature, biological systems present a significant challenge to predictive modeling Synthetic biological design provides a means to construct model or ‘toy’ systems that can both test and enhance our ability to accurately model genetic circuits The construction and analysis of such systems allows for the systematic characterization of ‘parts’ and ‘devices’ that either mimic natural systems (bottom-up engineering) or implement novel functions (forward-engineering) How important is the relationship between predictive modeling and synthetic design?

4. Rational Design and Parts Many examples of synthetic design ‘one-offs’ that prevent construction of complex, hierarchical systems from simpler functional units How can we design frameworks that will aid in the rational design of synthetic systems? Can we compose complex systems from functional units (‘parts’) that have well defined inputs, outputs and operational parameters? Does the modeling of individual parts allow for predictive modeling of the behavior of systems?

5. A Bottom-Up Approach to Gene Regulation Nicholas J. Guido, Xiao Wang, David Adalsteinsson, David McMillen, Jeff Hasty, Charles R. Cantor,Timothy C. Elston and J. J. Collins

6. O R 1 O R 2lacOR3OR3 O R O lac Promoter P RM Promoter CI LacI GFP IPTG pBAD PLtetO1 Arabinose Placed on high copy ColE1 plasmid System Architecture

7. Examined the probability of each of 6 binding states by looking at the repressor only, activator only, and repressor-activator systems separately Fit the above to transcriptional data using the unregulated system as a baseline Devised deterministic model and added stochastic effects Included the fluctuations of GFP as well as cell growth and division. Modeling the System

8. Figure 1: a) The unregulated system. b) The repressor-only system. e) Histograms of the unregulated system: experimental data (red) and stochastic model data (blue). f) Repressor- only system results: GFP expression represented as normalized fluorescence versus IPTG level; red circles are experimental data and the blue lines are the results of the deterministic model. The inset shows CV versus IPTG level: experimental data (red) and stochastic model data (blue). Results for Unregulated and Repressor-Only Systems

9. c) The activator-only system. d) The repressor–activator system. g) Activator-only system results with an inset showing CV versus arabinose level. h) Repressor– activator system results where inset shows CV versus arabinose level, with 10mM IPTG in each case. Results for the Activator-Only and Repressor-Activator Systems

10. i) Histograms of normalized cell counts versus arbitrary fluorescence units, of experimental data (red) and stochastic model data (blue) for the repressor-only system. The solid lines = no inducer (IPTG) and the dashed lines = highest level of IPTG. j) No inducer = solid lines and the highest level of arabinose = dashed lines for the activator-only system. k) No arabinose = solid lines and the highest level of arabinose = dashed lines, with 10mM IPTG in each case for the repressor– activator system. Results for Varying Levels of IPTG and Arabinose on Different Systems

11. Repressor-activator system on a low copy plasmid with a) 1x10 -6 % arabinose, not IPTG; b) 5x10 -4 %, no IPTG; c) 1mM IPTG, no arabinose; d) 50 mM IPTG, no arabinose. Repressor-Activator System on a Low Copy Plasmid

12. Now with positive feedback …

13. Blue = modeled Red = experimental Repeat of experiments with the new architecture. Data shows induction of GFP as a function of inducer concentration.

14. Blue = modeled Red = experimental Distribution of cell fluorescence at fixed inducer levels.

15. Effects of Copy Number on Noise Experiments confirmed the model’s prediction that cell growth and division decreases noise at high plasmid copy, but increases noise at low plasmid copy. The authors rationalized this as the moderating effect of division (halving concentration) outweighing the introduced cellular process noise at high, variable plasmid copy.

16. # Params Deterministic Model15 Stochastic Model: Repressor, Activator, and Repressor Activator 13 Stochastic Model: With positive feedback 15 Total43 Given the use of this many parameters, do the qualitatively accurate, if quantitatively slightly skewed, predictions seem like an indication of success?

17. Explicit Assumptions 1.Binding to CI sites ordered. 2.Kinetics of binding/release are fast compared to transcription, translation, degredation, division. 3.IPTG acts by increasing the off rate of the tetramer 4.CI dimer concentration depends linearly on Arabinose concentration 5.Dimer binding follows first order kinetics. 6.Max cell volume is 1 um 3. 7.LacI and CI concentrations are at steady state. 8.mRNA basal translation occurs at a rate of 10 proteins/mRNA/cell cycle 9.All mRNA half-lives are 6 minutes. 10.There is no GFP degradation 11.Cells always divide upon reaching an certain volume 12.High plasmid copy number follows a gamma distribution 13.The mean high copy plasmid number is 50, low copy is exactly 3. 14.CI association has same thermodynamics as LacI dimerization 15.Other mathematical wizardry: “The free parameter p was tuned by eye until a good match between the experimental results and model output was achieved” Many of the assumptions are reasonable, others seem a little more arbitrary. To what extent is modeling an art, to what extent is it a science?

18. In the end, then, are you convinced that –we have the capability now to accurately characterize a simple system? If not, will we? –a set of such characterizations will correctly predict the behavior of a more complicated, composite system? –trying to build such an understanding highlights gaps in our knowledge? Conclusions

19. A synthetic multicellular system for programmed pattern formation Subhayu Basu, Yoram Gerchman, Cynthia H. Collins, Frances H. Arnold and Ron Weiss

20. Motivation Coordinated spatiotemporal regulation is hallmark of cellular and multi-cellular systems Signal processing is a critical component of complex systems regulation in the face of noisy external stimuli and internal responses

21. Design Overview A ‘band-detect’ system rejects signals below and above certain tunable thresholds, only passing intermediate signals Useful to detect position within a chemical gradient and produce a desired output Implemented through a feed-forward architecture with differential repressor efficiencies (CI vs. LacI) and GFP output

22. Sender production of acyl-homoserine lactone (AHL) Under control of Tet-O doxycycline inducible Promoter AHL is sensed by LuxR which inputs into a feed-forward network topology involving induction of both LacI MI and CI ( repressor) GFP expression can be repressed by either LacI, which is constitutively expressed, or LacI MI, which Is induced by AHL. CI in turn represses LacI, allowing GFP expression when LacI MI is below the concentration required for repression CI repression > LacI MI repression, thus at intermediate AHL levels CI is repressive while LacI MI is sub-repressive yielding GFP output System Overview

23. Band-detect implemented with two plasmids, termed high-detect (HD) and low-detect (LD) due to their setting of the high and low thresholds in between which yield GFP output HD variants with hypersensitive LuxR (HD1) or reduced-copy number (HD3) tune range of AHL concentrations that yield GFP output Why are LacI MI and LuxR both on the same plasmid? Would you system to behave differently if LacI MI and cI were expressed from the same plasmid? Band Selection

24. sender GFP ON GFP OFF GFP OFF Spatial regulation of GFP output (or abitrary otuput) based on position in chemical gradient Only band-detect cells within a ring around a sender that experience levels of AHL passed by the network will express GFP Chemical Gradients and Spatial Regulation

25. Modeling v. Experiment (a), (b) model and experimental data of AHL responsiveness of HD system alone (c), (d) model and data of full band detect with either hypersensitive, wild-type, or reduced sensitivity HD system coupled with same LD system Liquid-phase culture, steady state ODE model, no dynamics Why does the width of the band-pass seem to be differ significantly from the model? Also, the model doesn’t seem to predict the observed differences in gain.

26. Pattern Formation magnified fluorescent image of a sender colony (light blue) forming the middle of a bullseye with concentric rings formed by band-detect cells with different HD systems, which tunes output (green or red) to position in the AHL radial gradient

27. Ring formation dynamics After induction of sender cells, there is a delay in the formation of a fluorescent response by the band-detect cells The ring starts at position roughly Underneath the position of maximum GFP (8-9mm) induction, Followed by some spreading about this position

28. Spatiotemporal regulation and LacI Spatiotemporal simulations with Stable LacI (b) and fast-decaying LacI reveals wider variance in rate and extent Does this make sense in terms of the system architecture?

29. Statistical Analysis LacI decay rate significantly correlated with ring width and duration of ring formation Longer ring formation Ring width

30. More patterns Senders can be arrayed to generate different patterns, (a) is the result of spatial simulation Can their newly fit model predict the experimental results in (b)-(d) (or more complicated initial conditions)? Neither the paper nor supplementary material seem to indicate that they can….so other than making pretty pictures, is there greater utility to this system?

31. Discussion The modeling seems a bit post-hoc, does this inform a rational design/forward-engineering methodology? What experiment could the authors have included to test their LacI hypothesis? Does this system provide well-characterized, useful parts that could be incorporated into more complex systems? The dynamics of even a simple system are difficult to predict without simulation How much emphasis should be placed on ‘toy’ systems v. synthetic systems with defined end-uses?