Recent results in synthetic biology showed that it is possible to design and build a variety of gene circuits and implement them in bacterial and mammalian cells. Example include toggle switches, oscillators, counters, concentration range detectors, and logical gates. By drawing inspiration from electrical circuits, such biological circuits can be combined to achieve more complex and useful behavior. However, as in electrical circuits, the usefulness and dependability of an ensemble depends on the reliability of the components. Can we build "reliable" synthetic gene circuits? Can we guarantee their qualitative behavior (e.g., AND gate) under parameter uncertainty? In this talk, I will present some preliminary answers to these questions. I will show that, by using techniques such as abstractions and model checking from formal verification, realistic and widely used mathematical models of gene networks can be synthesized, analyzed, and controlled from rich, qualitative specifications. I will show how these results can be applied to "tune" the parameters of a transcriptional cascade and to analyze and control a mathematical model of a toggle switch.
Live cells process information using purely digital systems, such as the genetic code, and analog reaction networks that at times behave as digital circuits, such as signal transduction. We are developing approaches to build "designer" reaction networks in a systematic fashion in order to control and modify cellular behavior. As a first step we have constructed networks that control gene expression based on logic combinations of specified biomolecular inputs, including mRNAs, microRNAs and transcription factors. Our approach uses RNA interference as a logic processor and a variety of sensor "devices" to read out individual inputs. We are also addressing questions pertaining to robustness and reliability of these networks, and to this end demonstrated how an "incoherent feed-forward" network motif can limit fluctuations due to gene copy number variability.
Gene regulatory networks are at the heart of cellular function and help to determine cell fate and phenotype. A coherent mathematical understanding of how these networks operate will be necessary not only to elucidate the true function of cellular machinery, but also to guide the design of engineered networks for use in practical applications. In this talk, I will discuss the creation of a fast, robust and tunable synthetic gene oscillator in Escherichia coli. This oscillator, based on previous theoretical work, revealed fundamental flaws in our ability to computationally model dynamical gene regulation and cellular signaling. Discrepancies between the experimentally observed dynamics and the mathematically predicted behavior led us to new insights into the importance of fast reactions and dynamical delay in gene regulatory networks.
Synthetic biology is often considered to be the synthesis of large genetic circuits that are composed of independent and modular biological parts. However, this engineering-focused view of synthetic biology largely ignores biology itself and how the idiosyncracies of organisms can impact parts, circuits, and systems, including those that are crafted to be orthogonal. In essence, synthetic biology is a myth, and should be more rightly considered as a subset of a different buzzword, systems biology.
When viewed in this manner, the parts, circuits, and systems can be treated as just part of larger models for organisms, and their functionality need not be completely orthogonal. Indeed, it will often be advantageous to carefully integrate new systems with old in order to optimize function. In this regard, we will consider the function of RNA parts both independently and in the context of circuits, and will examine how mathematical models of function and selection can impact the utility of parts. In addition, we will discuss how larger systems can be modeled based on component parts, especially for biological computation.
Recent progress in nanotechnology has yielded new device components with unprecedented capabilities. However, the small size of these building blocks makes it difficult to position them into functional assemblies using existing patterning techniques. As one solution to this problem, we have converted the protein shells of two viruses into scaffolds that can position nanoscale objects with excellent spatial resolution. This strategy has been used to synthesize arrays of fluorescent molecules, providing efficient mimics of the light harvesting system present in photosynthetic organisms. In a second research area, well-defined core/shell materials have been prepared for applications in targeted drug delivery and diagnostic imaging. The cornerstone of these efforts has been a series of new synthetic reactions that can modify biomolecules with high site-selectivity and yield. This presentation will focus on the way these new methods have been combined with protein mutagenesis techniques to access complex multicomponent materials with emergent function.
In synthetic biology, gene regulatory circuits are often constructed by combining together smaller circuit components. Wiring between components is achieved by transcription factors acting on promoters. If the individual components behave as true modules, the properties of the composite circuits can be predicted from the individual modules. del Vecchio et al. introduced retroactivity as a measure how one module affects frequency response of another. In this paper, we describe an experimental method for estimating the retroactivity of a genetic circuit by exploiting stochastic fluctuations in gene expression. We also introduce a new term, the fan-out, that describes the maximum number of downstream promoters that can be driven from an upstream circuit signal without significant time-delay or signal attenuation. We also discuss ways in which the fan-out can be improved and show that the fan-out can be determined from the system's retroactivity.
In the second half of the talk we will discuss how stochastic reaction processes including synthetic gene circuits are affected by external perturbations. We describe an extension of deterministic control analysis to the stochastic regime. We introduce stochastic sensitivities for mean and covariance values of reactant concentrations and reaction fluxes (e.g., PoPS measure in synthetic biology) and show that there exist MCA-like summation theorems among these sensitivities. We propose a systematic way to control fluctuations of reactant concentrations while minimizing changes in mean concentration levels by using the stochastic sensitivities. We also propose a possible implication in the control of flux fluctuation (e.g., PoPS variance): The control distribution for flux fluctuations changes with the measurement time window size within which reaction events are counted for measuring a single flux. When a control engineer applies a specific control operation on a reaction system, the system can respond contrary to what is expected, depending on the time window size.
Both the prediction and design of protein structure, using computational and rational approaches, remain significant challenges in protein chemistry. A major limitation to developing a comprehensive physicochemical model of the protein structure-sequence relationship is the vastness of sequence space and the low-throughput nature of biophysical studies. We are pursuing two avenues to understand better the sequence structure-relationship: sorting large libraries of protein variants for structured proteins, and statistical analysis of ubiquitous protein families for protein redesign. In the combinatorial approach, we have developed a high-throughput cell-based screen for activity of the well-studied four-helix bundle protein Rop. To collect quantitative stability data for large numbers of variants, we have developed a method of high-throughput hydrophobic dye binding called High-Throughput Thermal Scanning (HTTS) which can be applied using automation and a real-time PCR machine 96-wells at a time. This system is being used to directly test the "rules" of protein design, taking those rules as hypotheses and sorting the resulting libraries for structure and stability. We are also interested in the role of correlated occurrences of amino acids in natural protein families. To that end, we have generated a consensus version of triosephosphate isomerase (cTIM), which can be thought of as a "correlation-free" variant, as a host to interrogate the roles of correlated positions by mutagenesis and library methods. Two closely-related consensus variants differ dramatically in their physical properties and activity. Methods for the analysis of pair-wise correlations in protein families will be discussed.
Humans have long searched fields and forest for plants to serve their needs. Instead of endless searching, synthetic biology provides a means to re-design plants for human service. We're applying plant synthetic biology in two areas: (1) producing plants to be highly specific detectors and (2) renewable energy. Detector plants are beginning engineered using computationally designed receptors whose input is linked via a synthetic signal transduction pathway to visible readout system. Improvements to the system are in progress using rational design and directed evolution. The detector plant response is being further tuned with a toggle-switch to add ultra-sensitivity and memory. Renewable energy can also be approached using controllable switches in plants and algae.
Modularity is the property by which the behaviour of a system in isolation does not change upon interconnection; this property allows the design of complex systems from simpler ones and may have participated in the evolution of complex biological networks. Yet, the extent of modularity in cellular signalling networks remains unknown. We used theory and experiments with purified components to characterize the limits of modularity for a signalling system consisting of a cycle of reversible covalent modification. We show that competition between downstream targets of a signaling system could result in increased sensitivity to system output (inhibitor ultrasensitivity). We also show that the steady state output and dynamics of a signal transduction covalent modification cycle were altered upon connection to downstream systems, because of impedance-like effects called retroactivity. Steady state effects of retroactivity on system sensitivity and/or set-point of the response to the stimulatory effector depended on whether one or both activities of the covalent modification cycle were indirectly inhibited. Dynamical effects of retroactivity included increasing the response time, introducing a delay to time-varying input stimuli, and reducing the frequency bandwidth of the covalent modification cycle. Our results indicate that modularity depends on network structure as well as the dynamical properties of both the input stimuli and the covalent modification cycle.
Work done in collaboration with Peng Jiang, Alejandra C. Ventura, Domitilla Del Vecchio, Lauren Van Wassenhove, Avraham E. Mayo, and Sofia D. Merajver..
Biotechnology plays an increasingly central part in the manufacturing of compounds in the pharmaceutical, chemical, and fuel industry. The underlying biological research has moved beyond the molecular reductionist dogma to a systems view, and novel system-wide analytic tools allow unprecedented insight into the relevant processes in cells. At the same time, metagenomics increases drastically the gene pool from which to recruit catalysts. The ETH Bioprocess Laboratory develops tools that are crucial on the way from designing biocatalysts from a systems perspective to implementing production processes. We concentrate on the rational engineering of in vitro multi-enzyme reaction networks, in particular for the production of natural and unnatural sugars and ultimately oligosaccharides. Crucial questions are how to insulate efficient pathways from highly interconnected networks, such as the central carbon metabolism, and how to optimize these pathways in terms of dynamic behavior. We will illustrate possible strategies using our efforts in multi-enzyme production of building blocks for C-C-bond forming enzymes and real-time analysis of in vitro metabolic networks.
Key features of Synthetic Biology include a focus on design and design principles, as well as the development of well-characterized and re-usable Parts. The field intersects with Metabolic Engineering in areas such as the design of novel pathways for product generation, in which enzymes may be considered as interchangeable Parts, and the improvement of those pathways for increased productivity. We have constructed a synthetic pathway for the production of glucaric acid, deemed a "top-value added chemical" from biomass, from glucose in Escherichia coli. Co-expression of the genes encoding myo-inositol-1-phosphate synthase (Ino1) from Saccharomyces cerevisiae, myo-inositol oxygenase (MIOX) from mouse, and uronate dehydrogenase (Udh) from Pseudomonas syringae led to production of glucaric acid. Flux towards glucaric acid is ultimately limited by MIOX, whose activity is dependent upon the concentration of myo-inositol, its substrate. To improve glucaric acid production, we have explored several options for increasing flux through the pathway, including the use of other enzyme Parts and the creation of synthetic scaffolds (Devices) to co-localize Ino1 and MIOX, thereby increasing the local concentration of myo-inositol. We will present results on the application of these scaffolds in various configurations to improve MIOX activity and glucaric acid productivity.
Work performed in collaboration with John Dueber, currently Assistant Professor of Bioengineering at the University of California, Berkeley.
The phenotypic differences between individual organisms can often be ascribed to underlying genetic and environmental variation. However, even genetically identical organisms in homogeneous environments vary, suggesting that randomness in developmental processes such as gene expression may also generate diversity. I will discuss the consequences of gene expression variability for the operation of genetic networks in multicellular organisms, using the intestinal specification network in Caenorhabditis elegans as an example. Wild-type intestinal cell fate is usually invariant, but mutations in elements of the specification network can have indeterminate effects: some mutant embryos fail to develop intestinal cells, while others produce intestinal precursors. By counting transcripts of the genes in this network in individual embryos, I will show how phenotypic variation can arise when mutations expose otherwise buffered stochasticity in the dynamics of cell fate decisions.
Determining quality of performance for a biological system is critical to identifying and elucidation its design principles. This important task is greatly facilitated by enumeration of regions within the system's design space that exhibit qualitatively distinct phenotypes. I will present an approach to the generic construction of the design space for biochemical systems. This approach is grounded in the power-law equations that characterize traditional chemical kinetics and, by transformation, the rational functions that characterize biochemical kinetics. In steady state, the analysis of these equations can be reduced to that of linear algebraic equations. These methods will be illustrated with applications to common classes of biochemical system motifs.
Viruses are complex systems whose behavior is governed by intricate interactions between their genomes and those of the host. HIV, the retrovirus that causes AIDS, can establish rare, latent infections of host cells, and the resulting latent pools of virus represent the most significant barrier to elimination of virus from a patient since they persist for decades and can reactivate at any time. After HIV enters a cell, it semi-randomly integrates its genetic material into the host genome. The viral promoter then integrates several inputs to regulate the viral gene expression rate: epigenetic and genetic effects at the integration position, the activity of host signaling pathways and in particular host transcription factors that bind to the viral promoter, and the levels of virally-encoded proteins that feed back and regulate promoter activation. A high viral gene expression rate rapidly initiates viral replication, whereas low expression can lead to stochastic effects in viral gene expression that we have hypothesized contribute to viral latency. We have conducted a rigorous experimental and computational analysis to elucidate mechanisms that govern gene expression dynamics in a lentiviral model of HIV-1 and thereby contribute to the establishment of latent viral infections, knowledge that can enable the development of new therapies.
In addition to causing disease, viruses can be engineered to deliver therapeutic genetic material to help cure disease. Retroviruses are in general effective gene delivery vehicles and have succeeded in clinical trials; however, they must be improved in several areas, including efficiency, the capacity for targeted gene delivery, and safety. For the latter, one significant problem that has arisen in human clinical trials is insertional mutagenesis, wherethe tendency of retroviruses to integrate in transcriptional units can lead to the misregulation of oncogenes and thereby contribute to cell transformation. One way to potentially restrict or retarget viral integration into desired regions would be to add a specific or selective DNA binding domain into the virus; however, it is unclear where to insert this "part" into a viral particle such that it enhances selective integration in a modular fashion. We have applied protein engineering and library selection tools to engineer retroviral vectors with selective integration properties into the human genome, which promises to create retroviral vectors with greatly enhanced safety.
Characterization is a challenge both for synthetic biology and the discovery of natural gene circuit structure and function. However, measurements of cell-to-cell variability (i.e. noise) in gene-expression have proven a powerful technique to probe the underlying gene-regulatory architecture of cells. Unfortunately, current experimental implementations for measuring expression variability, which are primarily flow cytometry methods, are limited by: (i) an inability to provide any information on expression kinetics, (ii) an inability to specifically measure intrinsic sources of noise without technically tedious controls (e.g. two-color approaches), and (iii) the time-consuming process of isolation and expansion of isoclonal populations. Here, we present an approach that overcomes all of these previous limitations by using time-lapse microscopy to measure both the kinetics and magnitude of gene-expression fluctuations in single cells over time. We will present the use of measured single-cell fluctuations to produce a 'noise map' and demonstrate that noise map structure reveals the structure and function of the underlying gene circuit.
The rich genome sequence information being generated from diverse organisms presents us with a golden opportunity not only to learn from nature, but to build upon it - to forward engineer synthetic biosystems for beneficial purposes. Currently, our forward engineering skills are still less sophisticated. A major research interest of my group is to develop these skills and use them for understanding natural biological systems as well as engineering synthetic ones. This talk is going to present some crucial capabilities which we have been developing, including high-throughput gene and genome synthesis technology, new protein design algorithm and precise protein expression control methods. Their applications in biomolecular engineering, metabolic engineering, gene therapy, and vaccine development are going to be presented. It is believed that the combined power of these technologies will lead to better understanding and more efficient engineering of biological systems and contribute to the development of synthetic and systems biology in general.
Synthetic biology is revolutionizing how we conceptualize and approach the engineering of biological systems. Recent advances in the field are allowing us to expand beyond the construction and analysis of small gene networks towards the implementation of complex multicellular systems with a variety of applications. In this talk I will describe our integrated computational / experimental approach to engineering complex behavior in living systems ranging from bacteria to stem cells. In our research, we appropriate useful design principles from electrical engineering and other well established fields. These principles include abstraction, standardization, modularity, and computer aided design. But we also spend considerable effort towards understanding what makes synthetic biology different from all other existing engineering disciplines and discovering new design and construction rules that are effective for this unique discipline.
We will briefly describe the implementation of genetic circuits with finely-tuned digital and analog behavior and the use of artificial cell-cell communication to coordinate the behavior of cell populations for programmed pattern formation. Recent results with implementing Turing patterns with engineering bacteria will be presented. Arguably the most significant contribution of synthetic biology will be in medical applications such as tissue engineering. We will discuss preliminary experimental results for obtaining precise spatiotemporal control over stem cell differentiation. For this purpose, we couple elements for gene regulation, cell fate determination, signal processing, and artificial cell-cell communication. We will conclude by discussing the design and preliminary results for creating an artificial tissue homeostasis system where genetically engineered stem cells maintain indefinitely a desired level of pancreatic beta cells despite attacks by the autoimmune response. The system, which relies on artificial cell-cell communication, various regulatory network motifs, and programmed differentiation into beta cells, may one day be useful for the treatment (or cure) of diabetes.
Chemical synthesis of viral genomes is independent of a natural template and, thus, it allows modifying the structure and function of a virus' genetic information to an extent not possible before. We have used this new strategy to further our understanding of an organismís properties, particularly its pathogenic armory if it causes disease in humans, and to make use of this new information to protect from or treat human viral disease. Specifically, we have recoded the genome of poliovirus, altering the capsid-coding region by introducing 600 to 1,000 nucleotide changes. Specifically, we altered favored to unfavored codon pairs, thereby changing the codon pair bias within the polyprotein without changing codon bias or the sequence of the viral proteins. Such large-scale changes yielded surprising phenotypes, particularly those related to the specific infectivity of virus variants and to the attenuation of virus pathogenicity in CD155 tg mice. The strategy has been tested with influenza virus yielding highly attenuated influenza virus strains.
Also of interest is a poliovirus variant in which sequences of the polyprotein were "scrambled" by maximizing the number of nucleotide changes while preserving both codon bias and amino acid sequences. The scrambled sequences were used to replace the domains P1 (structural proteins), P2, or P3 (non-structural proteins) of the P1-P2-P3 polyprotein. Surprisingly, the scrambled sequence of P1 (934 changes out of 2,643 P1 nucleotides) in a P1scrambled-P2-P3 virus did not alter the virus' growth properties. We have used scrambled sequences in the non-structural region of the poliovirus polyprotein (P2+P3) to search for RNA signals essential for vial replication. As expected, a P1-P2scrambled-P3 virus was dead because the known essential cre element (an RNA hairpin) in P2 was destroyed; repairing the defect by inserting a wt cre into the 5' non-translated region restored the wt replication phenotype. We, therefore, can conclude that, other than cre, P2 does not contain essential RNA replication signals, a method currently applied to the P3 region. Interestingly, the P1-P2scrambled-P3 virus fails to recombine with a human C-cluster coxsackie virus (C-CAV20) in vivo because the sequence region where cross over occurs was scrambled. PV/C-CAV recombinants that are highly neurovirulent evolve in different parts of the world from oral poliovirus vaccines and C-CAVs, causing small epidemics of poliomyelitis.
To realize the promising practical applications of synthetic biology, bioengineers must interface the engineered genetic circuits in living cells with the environment. Although cells mostly rely on proteins such as receptors and transcription factors to transduce the chemical information into genetic signals, adapting such proteins to sense and respond to novel molecules is a daunting task. Despite its chemical simplicity, RNAs offer several attractive properties and engineering tools which make them an attractive platform for engineering chemical interfaces for applications in synthetic biology. We combine rational and combinatorial approaches to harness the diverse capacities of RNAs (molecular recognition, chemical catalysis, gene regulation) to construct synthetic chemical interfaces that operate in bacteria and in mammlian cells.