1. Engineered Post-Translational Logic (PTL). Samantha C. Sutton , Sara E. Neves , Lauren W. Leung , and Drew Endy  Why Post-Translational Logic? Building and Testing a PTL Device Abstract Protein-DNA (Transcriptional) logic (PDL) –Engineered around gene expression –Easier to engineer –Slow response time (hours) –Uses one subset of cellular functions Post-translational logic (PTL) –Engineered around protein modifications –Difficult to engineer –Fast response time (seconds) –Explores new set of applications PhysicsElectrical Engineering Biology Synthetic Biology Our Goal: Engineering Biology Types of Intracellular Circuits Activity of Activity of Activity InActivity Out PTL Inverter P Kinase An Example of a PTL Device Modeling as a Design Tool Necessary Components of a PO 4 -MAPK Part The Phospholocator Cell-Cycle Dependent Localization in Yeast Uses of the Phospholocator To build sets of post-translational logic (PTL) parts. To determine the docking site and p-motifs of MAP Kinases of interest. To detect the activity of MAP Kinases. YFP +  -gal Swi5 NLS PO 4 siteCdc 28 Docking site Nocodazole arrest (G2/M): cytosolic Pheromone arrest (G1/S): nuclear –+– Nocodazole Pheromone +–– Phospholocator Phospholocator-PO 4 Choice of Modification and Enzyme Substrate Protein NLS PO 4 siteDocking site P MAPK binds here MAPK adds phosphate group here Verification of Phosphorylation Build a Fus3 activated instance of the Phospholocator Build a simple inverter Develop a transcription-based localization assay for directed evolution of motifs Conclusions Future Directions. Acknowledgements + k1 k2 P P + + k3 k4 P + k1 k2 P P + + k3 k4 P State 2 Kinase P P P State 1 P P P Kinase k2 k4 # of fixed points 1 3 k1, k3 fixed PTL Flip-Flop is Robust to Parameter Fluctuations PTL Flip-Flop is Robust to Concentration Fluctuations In 1 In 2 Out 1 Out 2 0 0 0 0 1 11 11 0 not allowed hold 0 1 PDL Flip-FlopPTL Flip-Flop Example PTL Device: Flip-Flop In 1 In 2 Out 1 Out 2 Kinase Differences between PDL and PTL Flip-Flops Kinase Flip-Flop Model  Division of Biological Engineering and  Department of Biology, Massachusetts Institute of Technology Current synthetic biological circuits make use of protein-DNA and RNA-RNA interactions to control gene expression in bacteria. Systems that rely on the regulation of gene expression are relatively slow and unsuitable for many applications. Here, we describe our work to engineer synthetic biological systems in yeast using post- translational modifications of proteins to define system state and control cell function; such systems should have faster performance time and enable a wider range of applications. We have specifically chosen to focus on building phosphorylation-driven protein circuits. We modeled a specific instance of a post-translational circuit using methods such as Lyapunov exponents, and showed that the circuit should behave as desired within a large parameter space. We developed a set of peptide tags that can be used to drive the phosphorylation of a chosen substrate by a desired mitogen-activated protein kinase (MAPK). Each phosphorylation event alters a substrate output activity, such as translocation, degradation, or other binding event. These tags were developed using the Phospholocator – a construct whose phosphorylation-mediated translocation is controlled by MAPK activity. Specifically, MAPK phosphorylation of the Phospholocator nuclear localization sequence (NLS) controls recognition of the NLS by cellular import machinery. The Phospholocator serves three purposes: to determine the docking sites of MAPKs of interest, to measure the in vivo activity of such MAP Kinases, and to serve as a first set of post-translational logic parts. Currently, we have built a version of the Phospholocator that is targeted by Cdc28; our next step is to build Fus3-, p38-, and Hog1- activated instances. FunctionProsCons ActivationPowerful tool. Directly control kinase activity May involve 3 o structure engineering Nuclear localizationGood visualization, local expertise, previous examples of modular engineering Must be compatible with translocation machinery, screen sensitivity DegradationGood assays, well studied system, good screen No examples of modular engineering, confounding fluctuations in expression BindingWell-studied, local expertise Less interesting function Modification of choice : phosphorylation Best studied phospho-mediated functions Enzyme of choice : MAP Kinase Signaling pathways Well-studied Yeast has two well-known MAPKs: Fus3, and Hog1 Examples of modular MAPK docking sites Unlimited species concentrations Use hill coefficient to describe cooperativity Can make Pseudo-steady state assumption about protein-DNA binding A and B are active until doubly phosphorylated by the other. Non-processive phosphorylation gives rise to the requisite ultrasensitive behavior of pink and green proteins Conservation of species means that we are dealing with a 4-D system. Capped species concentrations Must generate cooperativity in new ways Cannot make pseudo-steady state assumption anywhere. We used Matlab to vary k1, k2, k3, k4 over biologically relevant values, and then used fsolve to locate the fixed points. Shown above is the number of fixed points obtained for different values of k2 and k4 ( k1 = k3 = 10 -4 (nM s) -1 ). Three fixed points can indicate a functional flip-flop, while one cannot. We computed the Jacobian of the system evaluated at each fixed point, and determined the corresponding eigenvalues. Two fixed points are asymptotically stable because they have all negative eigenvalues. The remaining fixed point is an unstable fixed point because it has one positive and three negative eigenvalues, indicating it has 3D stable and 1D unstable manifolds. Our three stable points define a 2D plane in 4D space. We transformed coordinates so the plane was perpendicular to two axes, and thus we could work in two dimensional space. Varying initial concentrations of the two kinases, we measured the ratio of the change in initial concentration to the change in equilibrium concentration. This is known as the method of Lyapunov exponents. Larger ratios indicate a separatrix, which is the boundary of a domain of attraction. We can use this map to determine: 1.The range of concentrations over which our flip-flop will hold state. 2.The amount of stimulus needed to switch states, or “flip.” AoAo 150001000050000-5000 5000 10000 15000 -5000 0 5 -20 []o]o [ ]o]o Inactive Active Inactive divergent (high Lyapunov exponent) convergent (low Lyapunov exponent) When unphosphorylated : Import machinery binds the NLS and brings the device into the nucleus. When phosphorylated : Import machinery cannot bind the NLS, and thus the device remains in the cytosol. Cdc28-Cln2 is active during late S and G2/M phase in yeast. In cells arrested with nocodazole, Cdc28 should be active, and phosphorylate the Phospholocator. The Phospholocator should then be cytosolic. Cdc28-Cln2 is inactive during G1 phase in yeast. In cells arrested with pheromone, Cdc28 should be inactive, and unable to phosphorylate the Phospholocator. The Phospholocator should then be nuclear. We ran a SDS-PAGE gel of crude yeast lysate from cells arrested with nocodazole or pheromone. The Phospholocator was detected using anti-GFP antibody (gift from Bob Sauer). Phosphorylated construct runs slower than non-phosphorylated construct. We have shown that a PTL flip-flop will theoretically behave as expected over a wide range of parameter values. We have specified a system of PTL based on MAPKs and translocation We have designed a testing scaffold for identifying and characterizing docking and phosphorylation motifs, and are working on a first set of motifs. We have built a working instance of a PTL device: the Phospholocator @Cambridge: Pam Silver, Mike Yaffe, Doug Lauffenburger, Gerry Sussman, the Endy lab, the Bob Sauer lab, the Chris Kaiser Lab, the Steve Bell Lab. @Berkeley: Alejandro Colman-Lerner, Jeremy Thorner, Kirsten Benjamin, Richard Yu, Roger Brent, Gustavo Pesce Funding : Howard Hughes Medical Institute, National Institute of Health, Merck & Co., Inc. “Our Goal” Images from:http://chemcases.com/cisplat/ cisplat01.htm ; http://www.nature.com/nsu/030421/ 030421-14.html http://www.northern.wvnet.edu/~tdanford/ icons/CELL.JPG; Ricarose Roque Transcriptional Modeling example from Gardner et al, Nature. 2000 Jan 20;403(6767):339-42.