V26 Synthetic Biology

1. V26 Synthetic Biology Synthetic biologists engineer complex artificial biological systems to investigate natural biological phenomena and for a variety of applications... They design and construct engineered cells with novel functions in a framework of an abstract hierarchy of biological devices, modules, cells, and multicellular systems. The classical engineering strategies of standardization, decoupling, and abstraction will have to be extended to take into account the inherent characteristics of biological devices and modules. To achieve predictability and reliability, strategies for engineering biology must include the notion of cellular context in the functional definition of devices and modules, use rational redesign and directed evolution for system optimization, and focus on accomplishing tasks using cell populations rather than individual cells. Bioinformatics III

2. Analogy with computers Bioinformatics III

3. Types of devices (A) Non-coding RNA device. The transcript of a target gene contains an artificial upstream RNA sequence complementary to its ribosome binding site (RBS), which forms a stem–loop structure in the RBS region, inhibiting translation of the target gene by cis-repression. When a transcribed non-coding RNA binds specifically to the artificial cis-RNA sequence, this prevents formation of the stem–loop structure in the RBS region, permitting the trans-activation of gene expression. Bioinformatics III

4. Types of devices (B) Allosteric protein. The gate is in an ‘off’ state when an output domain of an engineered protein binds to a tethered allosteric regulatory domain to form an autoinhibited complex. An input ligand can bind to the regulatory domain, relieving the inhibition to liberate the binding or active site of the output domain, switching the gate to the ‘on’ state. (C) Engineered receptor for trinitrotoluene (TNT), L-lactate, or serotonin. Redesigned E. coli periplasmic EnvZ receptors participated in His-to-Asp two-component signaling through autophosphorylation and subsequent transfer of the phosphate to the regulatory response element OmpR Bioinformatics III

5. Interfacing devices (A) Transcriptional inverter module with constitutive expression, IMPLIES, and inverter devices. IPTG and LacI are inputs to the IMPLIES device, CI is the input to the inverter device, and YFP is the module output. (B) Rational redesign improves inverter module output. The graph shows module output (YFP fluorescence) as a function of input (IPTG concentration). The ideal transfer function of the transcriptional inverter module is an inverse sigmoidal curve. The transfer function is flat and the component is non-responsive when unaltered genetic elements are used in constructing the inverter, but modification of the translational efficiency of the CI protein and further modification of operator binding affinity result in inversely sigmoidal curves with high gain and increased noise margin. (C) Directed evolution offers a complementary redesign strategy for the inverter module. A graph of module output (YFP fluorescence) as a function of input (IPTG concentration) shows that improvement of the transfer function as in panel B can be achieved by directed evolution instead of rational redesign. Bioinformatics III

6. Types of modules (A) Transcriptional cascade modules exhibiting ultrasensitive behavior. Ultrasensitivity increases as a function of cascade depth. Tet Repressor is expressed constitutively from PlacIq promoter. TetR dimer binds two tetO operator sites on PLtet-O1 and repress EYFP production in circuit 1 and Lac repressor (LacI) production in Circuit 2. aTc, which freely diffuses into the cell, binds TetR and prevents the repression of PLtet-O1 . Bioinformatics III

7. Context dependence (A) Modules operate within and modify the cellular context. (B) Successive insertions of modules recursively modify cellular context such that each new module is embedded in a new context, perhaps fundamentally altering module behavior. Bioinformatics III

8. Multicellular systems (C) Artificial cell–cell communication in S. cerevisiae using communication elements from A. thaliana. All exogenous proteins are shown with their respective promoters (yellow boxes). The sender expresses recombinant A. thaliana AtIPT4 under the control of GAL1 promoter. AtIPT4, which catalyzes isopentenylation of ATP, enables the sender to synthesize and secrete IP to nearby receiver cells. The receiver is composed of A. thaliana AtCRE1 cytokinin receptor and yeast YPD1 and SKN7 signaling proteins in an sln1 mutant strain. The receiver cells also overexpress PTP2 in order to suppress sln1 lethality as a result of the activation of downstream HOG1 kinase by the unphosphorylated SSK1 when cytokinin is absent. When IP signal binds AtCRE1, AtCRE1-YPD1-SKN7 phosphorylation activates GFP expression from the SSRE promoter in receiver cells. Here, the sender circuit is integrated into the receiver strain by placing AtIPT4 under the control of an SSRE promoter. The positive feedback motif results in quorum-sensing behavior that can be fine-tuned based on the regulatory mode for signal synthesis. The graph depicts output (GFP fluorescence) as a function of cell population (optical density) where the signal (IP) synthesis rate is controlled by expression of AtIPT4 enzyme under different promoters: unregulated (green), weaker basal expression with positive feedback (blue line), and stronger basal expression with positive feedback (red line) Bioinformatics III

9. Outlook - A biological device has no meaning isolated from a module; - a module has no meaning isolated from a cell; - a cell has no meaning isolated from a population of cells. This contextual dependence is an essential feature of living systems and is not an impasse, but rather a bridge to the successful engineering of living systems. As with the uncertainty principle in quantum mechanics, it may be prudent to treat some biological uncertainties as fundamental properties of individual cell behavior (e.g. gene expression noise, context dependence, fluctuating environments). The fact that we always use populations of synthetic cells to complete tasks means that the criteria of reliability and predictability should apply at the cell population level. As long as a significant number of the cell population performs our desired task, the unpredictability of events occurring at the molecular level should have minimal effect. Design and fabrication methods that take into account uncertainty and context dependence will likely lead to on-demand, just-in-time customization of biological devices and components, which need not behave perfectly. Building imperfect systems is acceptable, as long as they perform tasks adequately. Synthetic biology should use the strategies that make biological systems versatile and robust as part of its own design practices. Bioinformatics III

10. Ron Weiss Pattern formation is a hallmark of coordinated cell behaviour in both single and multicellular organisms. It typically involves cell–cell communication and intracellular signal processing. Here we show a synthetic multicellular system in which genetically engineered ‘receiver’ cells are programmed to form ring-like patterns of differentiation based on chemical gradients of an acyl-homoserine lactone (AHL) signal that is synthesized by ‘sender’ cells. In receiver cells, ‘band-detect’ gene networks respond to user-defined ranges of AHL concentrations. By fusing different fluorescent proteins as outputs of network variants, an initially undifferentiated ‘lawn’ of receivers is engineered to form a bullseye pattern around a sender colony. Other patterns, such as ellipses and clovers, are achieved by placing senders in different configurations. Experimental and theoretical analyses reveal which kinetic parameters most significantly affect ring development over time. Construction and study of such synthetic multicellular systems can improve our quantitative understanding of naturally occurring developmental processes and may foster applications in tissue engineering, biomaterial fabrication and biosensing. Nature 434, 1130 (2005) Bioinformatics III

11. Cell-cell response at various distances Figure 1 The band-detect multicellular system programs E. coli receiver cells to fluoresce only at intermediate distances from sender cells. a, Circuit operation for a sender and three receivers exposed to high, medium or low AHL concentrations, showing the correlation of the various AHL and protein levels (top left), approximation of the AHL gradient as a function of the distance from the senders (bottom left) and the relevant protein activities in cells at different distances from the senders as mediated through transcriptional regulation (right; orange, constitutively expressed response proteins; blue/ green, expression of regulated proteins; green and red arrows, transcriptional induction and repression respectively). High levels of LacI or LacIM1 (indicated by ++) are required to repress GFP. b, Plasmid map for senders. c, d, The high-detect (c) and low-detect (d) plasmids that implement the band-detect operation. Three versions of the high-detect plasmid with different sensitivities to AHL were constructed (regions of mutation are underlined: pHD1, LuxR; pHD2, wild-type; pHD3, ColE1). Nature 434, 1130 (2005) Bioinformatics III

12. Cell-cell response at various distances Figure 2 Simulated and experimental liquid-phase behaviour of high-detect and banddetect networks. a, b, Simulations (a) and experimental results (b) of the AHL dosage response for three high-detect network variants with wild-type LuxR (HD2, red), a hypersensitive LuxR (HD1, blue) and a reduced-copy-number plasmid (HD3, black). For the curve fits, the 95% confidence intervals have minimum/ maximum values of 2.03/6.58, 2.47/4.14 and 2.78/5.12 for HD1, HD2 and HD3, respectively. c, d, Band detect simulations (c) and experimental results (d) of three networks consisting of the high-detect variants from above and the same low-detect component (BD1 (blue), BD2 (red) and BD3 (black) contain the high-detect components from HD1, HD2 and HD3, respectively). For the curve fits, the 95% confidence intervals have minimum/maximum values of 0.48/4.43, 7.36/18.55 and 1.1/8.23 for BD1, BD2 and BD3, respectively. a.u.,arbitrary units. Nature 434, 1130 (2005) Bioinformatics III

13. Cell-cell response at various distances Figure 3 Experimental solid-phase behaviour of band-detect networks. a, Picture of the Petri dish used in the BD2-Red/BD3 experiment showing the sender disk in the middle. b, Bullseye pattern as captured with a fluorescence microscope after incubation overnight with senders in the middle of an initially undifferentiated ‘lawn’ of BD2-Red and BD3 cells. The senders in the middle are expressing CFP. c, Another bullseye pattern, this time with a mixture of BD1 and BD2-Red cells. Scale bar, 5 mm. Nature 434, 1130 (2005) Bioinformatics III

14. Formation of various patterns a, Simulation of band-detect behaviour on solid media with two senders that results in the formation of an ellipse. b–d, Experimental results showing various GFP patterns formed based on the placement and initial concentrations of sender cells expressing DsRed-Express: b, ellipse, two sender disks; c, heart, three sender disks; and d, clover, four sender disks. Nature 434, 1130 (2005) Bioinformatics III

15. Outlook The work described here shows the design and construction of an artificial multicellular system capable of programmed pattern formation. We have shown how a community of cells can sense a chemical gradient to form three distinct regions. The system consists of simple parts that are arranged in different configurations to elicit the desired patterns. Theoretical and experimental analyses of system behaviour are facilitated by the fact that the parts are well characterized and can be fine-tuned. The integration of such systems into higher-level organisms and with different cell functions will have practical applications in three-dimensional tissue engineering, biosensing, and biomaterial fabrication. We see the construction of this and similar systems as a step towards creating artificial differentiation patterns on demand and contributing to a better understanding of natural developmental processes. Bioinformatics III

16. Genome design T7 is an obligate lytic phage that infects E. coli. 57 genes code for 60 potential proteins. Only 35 of these have at least one known function. Of the 25 nonessential proteins, only 12 are conserved across the family of T7-like phages. Can we safely ignore these uncharacterized protein coding domains in our models of phage infection? Should we edit the genome to remove them? Genetics, and then biochemistry, enabled the discovery and characterization of some of the individual elements that participate in T7 development. Drew Endy Bioinformatics III

17. Element decompression and part design Six goals drove our design of the T7.1 genome. (1) we wanted to define a set of components that function during T7 development and, for each element, choose an exact DNA sequence that we could use to encode element function. (2) we wanted the DNA sequence encoding the function of any one element to not overlap with the DNA sequence encoding any other element. (3) we wanted the DNA sequence of each element to encode only the function assigned to that element and not any other functions. (4) we wanted to enable the precise and independent manipulation of each element. (5) we needed to be able to construct the T7.1 genome. (6) we needed the T7.1 genome to encode viable bacteriophage; at the start of this work, we were uncertain how many simultaneous changes the wild-type genome could tolerate. Bioinformatics III

18. Genome design We split the wild-type T7 genome into six sections, alpha through zêta, using five restriction sites unique across the natural sequence. Each section shown here has a wild-type section with representations of the genetic elements: protein coding regions (blue), ribosome binding sites (purple), promoters (green), RNase III recognition sites (pink), transcription terminators (yellow), and others (gray). The useful natural restriction sites across each section are shown (black lines). T7.1 sections are shown below the wildtype sections. Parts are given integer numbers, 1 through 73, starting at the left end of the genome. Unique restriction site pairs bracket each part (red/blue lines, labeled D[part #]L/R). Added unique restriction sites (purple lines, U[part #]) and part length (# base pairs, open boxes) are shown. Bioinformatics III

19. Genome design algorithm Bioinformatics III

20. Differences between wild-type T7 and T7.1 Bioinformatics III

21. Characterization of T7.1 Bioinformatics III

22. Discussion A system that is partially understood can continue to be studied in hope of exact characterization. Or, if enough is known about the system, a surrogate can be specified to help study or extend the original. Here, we decided to redesign the genome of a natural biological system, bacteriophage T7, in order to specify an engineered biological system that is easier to study and manipulate. The new genome, T7.1, is based on our incomplete understanding of the information encoded in the wild-type genome and our desire to insulate and independently manipulate known primary genetic elements. We constructed the first two sections of T7.1, making over 600 simultaneous changes or additions to the wild-type DNA, and observed that the resulting chimeric phage are viable. The T7.1 genome is easier to model and study. For example, by removing genetic element overlap, the T7.1 genome better matches the understanding of T7 biology encoded in our models, relative to the wild-type phage. However, more work is needed to demonstrate that the dynamic behavior of the system encoded by the T7.1 genome is easier to predict. Such work will benefit from the fact that the parts of T7.1 can be independently manipulated. Bioinformatics III

23. Discussion Phage viability demonstrates the following for sections alpha and beta. First, our parts as chosen can be separated by exogenous DNA sequence. Second, any functions encoded by genetic element overlap are, in aggregate, nonessential under standard laboratory conditions. Third, our current understanding of T7 is not insufficient to specify a viable bacteriophage. Viability does not demonstrate sufficiency because (i) if the chimeric phage had not been viable, then our current understanding would have been demonstrably insufficient, and (ii) while T7.1 is based on our current understanding, we do not have an exact understanding of all functions encoded in the T7.1 genome (e.g., genes of unknown function). Finally, viability, combined with the observed similarities in lysis times, suggests that T7.1 preserves polymerase-mediated genome entry and remains relatively independent of host cell physiology. Bioinformatics III

24. Discussion Our design of T7.1 was constrained by fears of producing a nonviable DNA fragment that would have been difficult to analyze and rescue. Given our initial success with T7.1, we have decided to revisit and extend our original design goals. For example, the design of our next phage, T7.2, will include (i) reduced gene sets that eliminate nonessential and nonconserved protein coding domains, (ii) codon shuffling of protein coding domains in order to disrupt secondary and cryptic regulatory elements, and putative mRNA secondary structure, and (iii) standard regulatory elements and regulatory element spacing. By actively removing all of the uncharacterized elements that we know about, as well as taking steps to disrupt any uncharacterized elements as yet unknown, we will be able to better study how the parts of T7 work to encode a functioning whole. Bioinformatics III

25. Question to bioethics Our work with T7 suggests that the genomes encoding other natural, evolved biological systems could be redesigned and built anew in support of scientific discovery or human intention. For systems beyond model laboratory organisms, pursuing such work will require the widespread societal acceptance of responsibility for the direct manipulation of genetic information. Bioinformatics III