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Workshop Overview THE SCIENCE AND APPLICATIONS OF SYNTHETIC AND SYSTEMS BIOLOGY Introduction Humans have been modifying the genetic characteristics of plants and ani - mals for millennia by controlling the breeding of species in order to select for certain traits or characteristics and to reduce or eliminate others. The discovery of the structure of DNA in 1953, the recognition of its importance as the carrier of heritable genetic information, followed by the development of recombinant DNA technology two decades later, paved the way for powerful technologies to manipulate genes directly and in such a way that the genotype and phenotype of an organism can be altered with utmost precision in a single generation. The realization of viral and microbial genomics, in the last few decades of the 20th century, coupled with the completion of the initial draft of the human genome sequence in 2001, reflect a fundamental shift in the way biology is stud - ied, and has opened a portal to vast postgenomic possibilities. Because of the Human Genome Project, scientists have already identified more than 1,800 genes associated with particular diseases. More recently, scientists have developed tech- niques to more efficiently synthesize or modify larger segments of DNA, marking a significant change in the way people study biological systems and a growing capacity for both experts and amateurs to manipulate such systems. Until the past decade, the work was often painstakingly slow, and able to ad - dress only relatively straightforward challenges such as the manipulation of one gene at a time. More novel or complex genetic modifications would be difficult 1

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2 SYNTHETIC AND SYSTEMS BIOLOGY to construct using the more conventional recombinant DNA1 (rDNA) techniques of the 1970s and 1980s. In the past decade or two newer approaches—combin- ing engineering and biological techniques—have enhanced researchers’ abilities to manipulate DNA. These new synthetic techniques allow for genes and long chains of DNA to be designed and constructed from scratch using a computer and relevant chemical compounds, rather than by employing a “trial-and-error” approach to the identification and insertion of pieces of existing genes from living cells into a novel host environment. In May 2010, researchers at the J. Craig Venter Institute announced that they had produced the first functional, self-replicating, bacterium whose entire nuclear genome had been synthesized artificially in the laboratory, albeit using a naturally occurring genome sequence as a template (Gibson et al., 2010). While the achievement did not, as some media reports at the time suggested, represent the “creation of life,” it did propel the nascent field of synthetic biology into the mainstream, and generated a number of questions and much speculation about the potential power, utility and risks associated with work in this field. Although biologists may have a long way to go before they have enough knowledge and the tools necessary to design and build life, the emerging field of synthetic biology has already reduced several novel products and lead compounds for drugs and vaccines, fuel, biofabrication of materials, and other industrial ap - plications. Most, if not all, of these products and compounds are being generated via the type of top-down approach, with scientists reengineering existing cells to do things that they do not normally do. By inserting the genetic machinery for metabolic pathways into Escherichia coli and other host organisms, scientists are attempting to create microbial bio-factories for the production of pharmaceutical ingredients, flavors, fragrants, and other chemical products (Ro et al., 2006). The goals also include compounds and cells with new phenotypes and functionalities, such as cells that can produce carbon-neutral biological fuels with properties that are similar to those of petroleum-based fuels (Fortman et al., 2008; Keasling, 2010) and novel drugs (Li and Vederas, 2009). The United Kingdom’s Royal Academy of Engineering observed that “[s]ystems biology aims to study natural biological systems as a whole, often with a biomedical focus, and uses simulation and modeling tools in comparisons with experimental information. Synthetic biology aims to build novel and artificial biological parts, devices and systems. Many of the same methods are used and as such there is a close relationship between synthetic biology and systems biology. But in synthetic biology, the methods are used as the basis for engineering ap - plications” (Royal Academy of Engineering, 2009, emphasis added). While both disciplines use similar approaches, systems biology uses these approaches to bet- ter understand the inner-workings of life, whereas synthetic biology emphasizes 1 Recombinant DNA: DNA that is created in the laboratory by splicing together DNA molecules from different sources, usually for replication in a host organism.

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3 WORKSHOP OVERVIEW the application of the lessons learned from systems biology for the purpose of engineering (or reengineering) living systems to behave in specified ways. Many potential applications of synthetic and systems biology are relevant to the challenges associated with the detection, surveillance, and responses to emerging and re-emerging infectious diseases. On March 14 and 15, 2011, the Institute of Medicine’s (IOM’s) Forum on Microbial Threats convened a public workshop in Washington, DC, to explore the current state of the science of syn - thetic biology, including its dependency on systems biology; discussed the dif - ferent approaches that scientists are taking to engineer, or reengineer, biological systems; and discussed how the tools and approaches of synthetic and systems biology were being applied to mitigate the risks associated with emerging infec - tious diseases. Through invited presentations and discussion, participants ex - plored the ways in which synthetic and systems biology are contributing to drug discovery, development, and production; vaccine design and development; and infectious disease detection and diagnostics. In addition, workshop participants considered how synthetic biology could be used to engineer, or reengineer, mi - crobial host cells to detect environmental toxins, produce carbon-neutral fuels, and produce novel raw materials. Organization of the Workshop Summary This workshop summary was prepared by the rapporteurs for the Forum’s members and includes a collection of individually authored papers and com - mentary. Sections of the workshop summary not specifically attributed to an individual reflect the views of the rapporteurs and not those of the members of the Forum on Microbial Threats, its sponsors, or the IOM. The contents of the unattributed sections are based on presentations and discussions at the workshop. The summary is organized into sections as a topic-by-topic distillation of the presentations and discussions that took place at the workshop. Its purpose is to present information from relevant experience, to delineate a range of pivotal issues and their respective challenges, and to offer differing perspectives on the topic as discussed and described by the workshop participants. Manuscripts and reprinted articles submitted by some but not all of the workshop’s participants may be found, in alphabetical order, in Appendix A. Although this workshop summary provides a description of the individual presentations, it also reflects an important aspect of the Forum’s philosophy. The workshop functions as a dialogue among representatives from different sectors and allows them to present their views about which areas, in their opinion, merit further study. This report only summarizes the statements of participants at the workshop over the course of two consecutive days. This workshop summary report is not intended to be an exhaustive exploration of the subject matter nor does it represent the findings, conclusions, or recommendations of a consensus committee process.

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4 SYNTHETIC AND SYSTEMS BIOLOGY What Is Synthetic Biology? The idea of managing or manipulating biology to identify or develop specific characteristics is not new. Scientists have used DNA to create genetically engi- neered cells and organisms for many years; the entire biotechnology industry has grown around our expanding abilities in this area. —Presidential Commission for the Study of Bioethical Issues (2010) Synthetic biology is not an entirely new science. Rather, aspects of it are an outgrowth of what plant and animal breeders have been doing for thousands of years and genetic engineers have been doing for decades—mixing and matching genetic material with the goal of “creating” novel plants and animals with desir- able traits. What differentiates synthetic biology from genetic engineering is its goal of designing new genetic systems and organisms using standardized parts from the “ground up.” Although the term “synthetic biology” has been used in various ways, it is generally understood to describe research that combines biology with the principles of engineering to design, construct, or adapt existing DNA, or other biological structures into standardized, interchangeable, building blocks for use in creating genetic systems that carry out desired functions. The vision behind this science is that these biological “parts” can be joined to create engineered cells, organisms, or biological systems that reliably behave in predictable ways to per- form specific tasks (Khalil and Collins, 2010; NSABB, 2010; Presidential Com - mission for the Study of Bioethical Issues, 2010; Royal Academy of Engineering, 2009). Synthetic biologists eventually hope to be able to program cells, cell systems, or organisms to perform specific tasks and functions (see Figure WO-1). Synthetic biology may also involve modifying naturally occurring genomes2 to allow these modified genomes to function in new contexts or to create en - tirely novel organisms. In 2010, when scientists at the J. Craig Venter Institute, Rockville, Maryland, reported having designed, synthesized, and assembled a complete Mycoplasma mycoides genome which they then transplanted into an M. capricolum recipient cell, creating a continuously self-replicating cell controlled by an artificial genome, much of the media proclaimed that the scientists had created “artificial” life (Gibson et al., 2010). Despite the “hype” surrounding this experiment the Venter Institute scientists did not create artificial, or even new, life. In fact, Venter’s team did what breeders have been doing for millennia—they facilitated and helped to direct the transfer of genetic material from one organism into another. But they did it in a way that had never been done before. They de - veloped new methods that allowed them to assemble an entire synthetic genome and fabricate its parts faster and with fewer errors than investigators have done in the past with other large pieces of DNA (Bedau et al., 2010). So while synthetic 2 An organism’s entire hereditary information usually encoded in DNA.

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5 WORKSHOP OVERVIEW FIGURE WO-1 This figure illustrates the synthetic biology concept that complex bio - logical systems can be broken down into their component parts in a similar way as more traditional engineering disciplines. SOURCE: Andrianantoandro et al. (2006). biology represents a revolutionary change in the way people interact with life, as James Collins of Boston University observed, “Synthetic biology is in its very early stages. Don’t believe the hype.” (Dr. Collins’ contribution to the workshop summary report can be found in Appendix A, pages 117-150.) Speaker Christopher Voigt of the University of California, San Francisco (UCSF)3 noted that one goal of synthetic biology was to be able to “mix and match” functions from the natural world in order to create organisms that carry out these functions in ways that a single, naturally occurring organism cannot. (Dr. Voigt’s contribution to the workshop summary report can be found in Appen - dix A, pages 429-449.) Some researchers seek to exploit systems biology–derived 3 At the time of this workshop, Dr. Voigt was at the University of California, San Francisco. He is now at the Massachusetts Institute of Technology.

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6 SYNTHETIC AND SYSTEMS BIOLOGY modeling tools to guide the design of synthetic gene networks. In fact, because of this element of design, some have compared synthetic biology to engineering (Khalil and Collins, 2010; NSABB, 2010; Presidential Commission for the Study of Bioethical Issues, 2010; Royal Academy of Engineering, 2009). Yet, naturally occurring living cells and organisms are complex adaptive systems, whose be - havior lies far beyond our ability to re-create with currently available principles and components. Biological Systems One important driver of the rapid growth of synthetic biology has been a shift away from the traditional reductionist method for understanding biological processes to one that favors a more holistic “systems” approach. Historically, the typical way one examined biological systems had been to isolate a small subset of biological components, which could then be interrogated individually to bet - ter understand their structures and functions. This approach assumes that the interactions of biochemical components occur in isolation, resulting in discrete, cause-and-effect, relationships. As our understanding of biology at the level of DNA, RNA, and proteins has increased, it has become clear that biological processes occur not in isola - tion but rather within the context of complex systems of components, regulated by intricate networks of feedback loops. These systems operate on a variety of levels: from that of RNA polymerase interacting with a DNA strand to start the process of DNA transcription, to a signal-transduction pathway within a cell, to complex interactions between systems of organisms. While our appreciation of the complexity of interactions within and between these systems has grown, there has been a corresponding recognition that the traditional, reductionistic, scientific approach severely limits our ability to understand complex biological phenomena and interactions within and between cells. Investigators have increasingly em- braced systems approaches in their efforts to understand biological interactions, taking advantage of the power of mathematical and computer modeling to exam - ine the complex interactions between components of a biological system (Royal Academy of Engineering, 2009). What Is Systems Biology?4 The increased emphasis on understanding biological phenomena in the con - text of the system within which it occurs gave rise to systems or integrative biology—which is the “study of the behavior of complex biological organization and processes in terms of the molecular constituents” (Kirschner, 2005). It takes 4 This section was adapted from p. 173 of the 2006 Institute of Medicine report, Globalization, Biosecurity, and the Future of the Life Sciences. Washington, DC: The National Academies Press.

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7 WORKSHOP OVERVIEW advantage of high-throughput, genome-wide tools—such as microarrays—for the simultaneous study of complex interactions involving molecular networks, including DNA, RNA, and proteins. It is, in a sense, classical physiology taken to a new level of complexity and detail. The term “systems” comes from systems theory or dynamic systems the - ory: systems biology involves the application of systems- and signal-oriented approaches to the understanding of inter- and intracellular dynamic processes (Wolkenhauer et al., 2005). Systems-level problem solving in living systems is based on the observation that cellular behavior involves a complex coordina- tion of dynamically interacting biomolecular entities. Systems biologists seek to quantify all of the molecular elements that make up a biological system and then integrate that information into network models that can serve to generate predictive hypotheses. A growing number of investigators within the life sciences community are recognizing the utility of systems biology tools and approaches for studying complex regulatory networks—both inside the cell, as well as the regulatory networks that integrate and control function of distinctly different cell types in multi-cellular organisms like humans—and for making sense of the vast, and rapidly accumulating, genomic and proteomic data sets (Aloy and Russell, 2005; Goldbeter, 2004; Rousseau and Schymkowitz, 2005; Uetz et al., 2005). These efforts draw heavily on computational methods to model the biological systems. Systems biology has become a valuable approach for drug discovery (Apic et al., 2005; Young and Winzeler, 2005). In medicine, disease is often viewed as an observable change of the normal network structure of a system resulting in damage to the system.5 A systems biology approach can provide insights into how disease-related processes interact and are controlled; guide new diagnostic and therapeutic approaches; and enable a more predictive, preventive, personalized medicine (Hood et al., 2004). Relationship Between Synthetic and Systems Biology Advances in synthetic biology are closely dependent on, and interactive with, advances in systems biology. When assembling new structures, whether single genes or more complex whole cells or organisms, the complexity imbued by in - teractions among components is vitally important to the proper functioning of the system-to-be. The key to successfully engineering, or reengineering, biological systems, is through understanding their complexity. Synthetic biologists are making headway toward handling more complex structures in more efficient ways. Chris Voigt’s work with code refactoring is an example of how advances in synthetic biology are paving the way for more re - 5 Disease-perturbed proteins and gene regulatory networks differ from their healthy counterparts, because of genetic or environmental influences.

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8 SYNTHETIC AND SYSTEMS BIOLOGY fined biological synthesis. Code refactoring is a reordering of the DNA sequence of a gene or gene cluster in order to eliminate inefficiencies—such as overlapping sequences—and otherwise alter the genetic code so that it can more readily be integrated into multiple genetic backgrounds. Because synthetic and systems biology both employ similarly complex com- putational modeling and emphasize the role of the biological systems to contextu- alize and make sense of biological phenomena, the distinctions between these two disciplines sometimes seem blurred. Nevertheless, there is an important distinc - tion between these two approaches. “While [s]ystems [b]iology attempts to obtain a quantitative understanding of existing biological systems, [s]ynthetic [b]iology is focused on the rational engineering of these systems” (Serrano, 2007). Is Synthetic Biology a True Engineering Discipline? DNA synthesis enables the de novo generation of genetic sequences that spe- cifically program cells for any of a wide range of purposes, including the expres- sion of a given protein. Technical developments continue to increase the speed, ease, and accuracy with which larger and larger sequences may be chemically generated. By the early 1970s, scientists had demonstrated that they could produce synthetic genes (Agarwal et al., 1974). Yet, it was the automation of de novo DNA synthesis and the development of the polymerase chain reaction (PCR) in the early 1980s that catalyzed the development of a series of cascading methodologies for the analysis of gene expression, structure, and function. Our ability to synthesize short oligonucleotides (typically 10 to 80 base pairs in length) rapidly and accu - rately has been an essential enabling technology for countless advances, not the least of which has been the sequencing of the human genome. The past few years have seen remarkable technological advances in this field, particularly with respect to the de novo synthesis of increasingly longer DNA constructs. The field of synthetic biology is driven by this increasing capacity to make long, accurate, DNA molecules of pre-specified sequence. Indeed, DNA synthesis is arguably the most important tool in the synthetic biologist’s toolbox. Keynote speaker Andrew Ellington of the University of Texas emphasized that sequence information is the basic commodity of synthetic biology—it is fungible, digital, and portable. (Dr. Ellington’s contribution to the workshop summary report can be found in Appendix A, pages 150-159.) Moreover, sequence information has the potential to be standardized, recoded, or programmed. Genome sequences, moreover, provide a huge amount of information about model organisms that synthetic biologists use as platforms for their gene circuit designs. Ellington observed that this growing capacity for de novo synthesis is due, in large part, to the large number of participants in the commercial sector, as well as funding from the public sector. The chemical synthesis and ligation of large segments of a DNA template, followed by enzymatic transcription of RNA led to the de novo creation of the poliovirus genome in 2002 (about 7,500

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9 WORKSHOP OVERVIEW nucleotides in length), from which the infectious, virulent virus was rescued fol - lowing its transfection into permissive cells (Cello et al., 2002). The following year, scientists announced the successful assembly of a bacterial virus genome (Smith et al., 2003). Parallel efforts in industry and academia led to the synthesis and assembly of large segments of the hepatitis C virus genome, from which replication-competent RNA molecules were rescued. Ellington cautioned that even these systems would perform differently in different host “contexts” and in ways that are not entirely predictable. Ellington went on to remark that, while scientists clearly are building syn - thetic biological systems that work, the “ability to model such systems—not make them work but model them in a true engineering way—is somewhat limited.” He pointed to the photographic bacteria built by Levskaya et al. (2005) and the “edge detector” E. coli built by Tabor et al. (2009)—illustrated in Figure WO-2—as examples of synthetic systems that clearly work. Even those parts that are well characterized, quantitatively or otherwise, do not always behave as expected. As Ellington observed, “The construction of very large fragments of DNA is no longer a limitation in the engineering of biological systems. Predicting the behavior of complex genetic programs de novo is now the limiting step in the programming of cellular behavior” (Tabor et al. 2009). Ellington reported that the reengineered viruses that he and his team built did not function once they were actually inserted into host cells. Therefore, successfully building, or rebuilding, a virus or other biological system according to Ellington does not necessarily mean that the system is actually going to work. Ellington and his team have applied a so-called “supercharging technol- ogy” to the anti-MS2 antibody (Lawrence et al., 2007). They synthesized a large number of genes that conferred either large negative or large positive charges on the antibodies, overexpressed them, and then tested them for functionality. In the end, only a few candidate antibodies were able to bind to MS2. Despite using the best predictive modeling available, the success rate was low enough that only by rapid prototyping of multiple, different antibody variants could they find ones that had both improved thermal resistance as well as improved affinity. Ellington concluded, “You can make a lot of DNA, but that doesn’t mean it is going to function the way you want it to.” Added to this systems-level complexity is the challenge of evolution and the reality that genetically-based biological systems gain, and lose, functionality over time (Tabor et al, 2009). According to Ellington and others at this workshop, organisms are “evolutionary machines.” Not only does evolution happen, mak - ing it extraordinarily difficult to predict how an engineered biological system will behave over time but, as Ellington observed, a device that works in one host organism does not necessarily function the same way in another host organism. Ellington commented that while the development of standardized parts was not impossible, our present capacity to accurately predict how these “parts” would function in a particular system is still “somewhat limited.” He referred to

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10 FIGURE WO-2 Construction of bacteria that are capable of light-dark edge detection. SOURCE: Ellington (2011; adapted from Cell).

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11 WORKSHOP OVERVIEW the “unrealized promise of BioBricks,”—“that we are going to be able to say that a given part works a given way in a system not yet determined.” He went on to observe that unfortunately, the complexity of organisms—and this is the systems biology part of things—dwarfs our ability to accurately model function. The same technologies employed by synthetic biologists for “good” could also be exploited for malevolent purposes—a classical dual-use dilemma. Ellington pointed to DNA synthesis and the growing capacity to make larger pieces of DNA quickly and affordably as the greatest cause for concern. While the dual-use dilemma is real, Ellington observed that the threats posed by syn - thetic biology are dwarfed by the expansive realm of real microbial threats that already exist in nature. Design and Complexity Speaker Herbert Sauro, of the University of Washington, observed that the level of complexity in a synthetic biological system expands very quickly as the number of feedbacks in a system increases. (Dr. Sauro’s contribution to the workshop summary report can be found in Appendix A, pages 394-417.) Simple linear pathways, whereby a perturbation of a single enzyme affects another enzyme in a predictable manner, according to Sauro, is largely understandable and engineerable; the greater the complexity, the less “engineerable” the system is. As depicted in Figure WO-3, if the pathway has any sort of feedback in it, understanding—and engineering—that pathway becomes slightly more com- plicated, requiring some simulation. Sauro observed that pathways with two or more feedbacks are virtually impossible to understand analytically and require a considerable amount of simulation. Largely understandable without the need to do simulations. Includes Linear Pathways branched systems Requires some simulations Negative Feedback Positive Feedback Requires a considerable degree of simulations to “understand” Double Positive Feedback: Chaotic Behavior FIGURE WO-3 Biosynthetic pathways with increasing complexity. SOURCE: Sauro (2011). Figure WO-3.eps bitmap w some vector labels

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