Synthetic Biology: Building on Nature's Inspiration: Interdisciplinary Research Team Summaries (2010)

What new foundational technologies and tools are required to make biology easier to engineer?

The engineering of biological systems holds great promise for developing solutions to many global challenges, including renewable energy production, material synthesis, and medical advancement. Synthetic biology is a rapidly growing, interdisciplinary field that involves the design, construction, and optimization of biological functions and systems. One of the long-term goals of synthetic biology is to make the engineering of biological systems easier and more reliable. Toward these goals, core activities of synthetic biology have focused on the engineering of complex biological systems and the development of engineering frameworks and foundational technologies that support the reliable programming of biological function.

Synthetic biology builds upon other more mature disciplines and most notably the field of genetic engineering. Genetic engineering began as a field more than thirty years ago and was largely developed around a set of foundational technologies that allowed researchers to amplify pieces of DNA, build relatively simple synthetic DNA elements by piecing together DNA fragments, and place those synthetic DNA elements into living systems to encode relatively simple, novel biological functions. However, the foundational technology set associated with genetic engineering does not scale readily to the engineering of large-scale integrated biological systems, such that biotechnology and medical technologies have not seen an increase in the complexity of reliably-operating biological systems that can be designed and constructed at a pace that is similar to the growth observed in other technology sectors. In addition, the knowledge-base supporting the design

of biological circuits that perform specified functions reliably is not well developed. As one example, there is not a comprehensive knowledge-base that guides the selection between transcriptional, post-transcriptional, or posttranslational control schemes or layering of these schemes to achieve a desired circuit performance.

The engineering of microbial chemical factories provides important case examples of engineering complex biological systems. Researchers are successfully engineering complex metabolic pathways (comprising up to 10-20 synthetic enzymatic steps) in microorganisms to achieve bioremediation and green synthesis strategies, the latter directed to the synthesis of various specialty drugs and chemical commodities, including biofuels. One recent example is based on the engineering of microorganisms, such as yeast and bacteria, to produce a cure for malaria based on the natural product artemisinin. Artemisinin is a molecule that is naturally produced in the plant Artemisia annua and is obtained through extraction from the plant material. Currently, the drug is expensive and thus does not allow effective treatment of malaria in the third world countries most afflicted with this disease. Researchers developed a solution to this problem by engineering a microorganism that can be grown cheaply, quickly, and in very large volumes to produce Artemisinin at nearly one-tenth of its current price. However, the success of this single engineering effort (and the current design, construction, and optimization processes in place) required a very large amount of dedicated resources and time. Therefore, the investment required to apply this strategy anew to every chemical and material we would like to produce is unrealistic with current technologies. As such, the ability of newly developed foundational technologies that will make these approaches cheaper, faster, and more reliable is critical to the broader application of synthetic biology.

• What are the most important experimental and computational technologies and tools that will support the engineering of biological systems?

• What new technologies are required to support increased efficiencies and scaling in design, construction, and optimization of biological processes?

• What tools are most needed to address the growing gap between our ability to construct and our ability to design large-scale integrated biological systems?

• What is the knowledge-base required to support the design of biological circuits and systems that operate reliably?

• How can we most effectively build the required knowledge-base?

• What are effective means for assessing newly-developed tools?

Andrianantoandro E, Basu S, Karig DK, Weiss R. 2006. Synthetic biology: new engineering rules for an emerging discipline. Molecular Systems Biology 2006;2:2006.0028: http://www.nature.com/msb/journal/v2/n1/full/msb4100073.html. Accessed online 28 July 2009.

Bio FAB Group, Baker D, Church G, Collins J, Endy D, Jacobson J, Keasling J, Modrich P, Smolke C, Weiss R. Engineering life: building a fab for biology. Scientific American 2006;294:44-51: Abstract available http://www.scientificamerican.com/article.cfm?id=engineering-life-building. Accessed online 28 July 2009.

Endy D. Foundations for engineering biology. Nature 2005;438:449-53: http://www.nature.com/nature/journal/v438/n7067/full/nature04342.html. Accessed online 28 July 2009.

Lucks JB, Qi L, Whitaker WR, Arkin AP. Toward scalable parts families for predictable design of biological circuits. Curr Opin Microbiol 2008;11:567-73: http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6VS2-4TX12FT 1&_user=4422&_rdoc=1&_fmt=&_orig=search&_sort=d&_docanchor=&view=c&_searcStrId=968268504&_rerunOrigin=google&_acct=C000059600&_version=1&_urlVersion0&_userid=4422&md5=e833ca071e17d025dd04dad98dac4b00. Accessed online 28 July 2009.

Ro DK, Paradise EM, Ouellet M, Fisher KJ, Newman KL, Ndungu JM, Ho KA, Eachus RA, Ham TS, Kirby J, Chang M, Withers ST, Shiba Y, Sarpong R, Keasling JD. Production of the antimalarial drug precursor artemisinic acid in engineered yeast. Nature 2006;440:940-3: http://www.nature.com/nature/journal/v440/n7086/abs/nature04640.html. Accessed online 28 July 2009.

Due to the popularity of this topic two groups explored this subject. Please be sure to review the second write-up, which immediately follows this one.

• Hal Alper, The University of Texas at Austin

• Shota Atsumi, University of California, Davis

• Randy Bartels, Colorado State University

• Steven Boxer, Stanford University

• Richard Braatz, University of Illinois at Urbana-Champaign

• Katie Brenner, Caltech

• Justin Gallivan, Emory University

• David LaVan, National Institute of Science and Technology (NIST)

• Linda Miller, Nature Publishing Group

• Sohi Rastegar, National Science Foundation

• Ehud Shapiro, Weizmann Institute of Science

• Jingdong Tian, Duke University

• Yingxiao Wang, University of Illinois at Urbana-Champaign

• Frederik Joelving, New York University

By Frederik Joevling, Graduate Science Writing Student, New York University

The engineering of biological systems could help develop solutions to some of the biggest global challenges, such as renewable energy production, material synthesis, and specialized drug manufacture. Synthetic biology is the rapidly growing, interdisciplinary area that involves the design, construction, and optimization of biological systems and functions. The field also promises an improved understanding of life, just like building a clock from scratch teaches you more about its inner workings than merely prying one apart.

But so far, the vision of what synthetic biology might accomplish remains beyond today’s technological prowess. Much scientific progress depends on technology to enable researchers to see in new ways and to collect data to which they previously did not have access. For example, when a few years ago it became possible to rapidly sequence the genomes of entire communities of organisms—the so-called metagenomics—the doors were suddenly thrown wide open to a microbial terra incognita. In the same way, an important element to the success of synthetic biology is the development of new tools and technology.

At the 2009 National Academies Keck Futures Initiative Conference, an IDR Team, comprising chemists, engineers, computer scientists, and others (Interdisciplinary Research Team 1A) met to identify the kinds of tools that will be needed to accelerate the design cycle of new biological systems, allowing researchers to test and execute their ideas fast and efficiently. The group categorized its recommendations according to different

levels of abstraction, moving from computer-based modeling to wetware implementation.

Synthetic biology should be described in a language that is usable by biologists and readily executable on a computer. Researchers already have good representations for protein structures and DNA sequences, but it is necessary to find a way to describe cellular processes and functions with equal precision. With such a description—encompassing everything from chemical reactions in a single cell to biological interactions in a community of cells—scientists would be able to simulate entire biological systems as well as their operations on them. In computer science, this type of language is known as an “executable specification language.” It should be compositional, meaning that the properties of any given system can be derived entirely from the properties of its parts. It should also be curated and validated, and experimentalists should to be able to add descriptions of various biological components in order to expand its functionality.

Along with a new computational language, researchers need better predictive models to describe dynamic behavior at different levels of biological organization, including molecules, organelles, cells, organs, organisms and ecosystems. The models should account for multiple interactions between the components of a biological system as well as internal feedback loops in which the end products of certain biochemical pathways influence their own synthesis. Finally, new mathematical approaches to analyzing biological systems could prove useful, but there was no consensus as to the nature of these approaches, nor the exact problem they would address.

Cells are inherently complex and noisy milieus. One might imagine—indeed, hope—that a great deal of this information can be safely ignored when using them as backdrops for new gene circuits; methods are needed to determine the necessary and sufficient set of parameters that specify their context and biological state.

Given their complex and adaptive nature, engineered cells also tend to do what is best for them and not necessarily what scientists want them

to do. Therefore it is essential to be able to analyze, predict and design for stability and robustness that will ensure reliable performance in a complex environment. And if unwanted change does occur, measures should be taken to ensure self-repair. The synthesized biological system should also include a sensing and diagnostic apparatus to inform us of errors that cannot be automatically corrected; just like the checkpoints included in the code of a computer program can help debug it, signals inserted at critical points in a biological circuit would reveal when and where the error occurred. An actuator system should also be coupled to this sensing and diagnostic apparatus to carry out the repair.

Of course, scientists should have means of interacting with the cells, of sensing their state and of manipulating and controlling them. Imagine that cells were engineered to produce a certain level of insulin in the pancreas of patients with type 1 diabetes in response to a blood sugar spike. If somehow these cells became corrupted and started secreting too much insulin, which could kill the patient by fatally lowering blood sugar levels, there needs to be an easy fix. Because extracting the cells directly is at best cumbersome—and at worst impossible—inserting a “kill switch” in the cells that scientists can control might be a solution.

To facilitate this interaction between scientists and cells, a variety of interfaces should be developed between biological and chemical, electronic, optical, thermal, and mechanical signals. In the case of insulin production in the pancreas, the engineered cells could be designed to respond to a synthetic chemical injected intravenously. In drug-manufacturing cell systems that function outside the body, on the other hand, an easy way to control production would be by making the cells sensitive to temperature. The sensors should report in real time on the state of the biological system, at various levels of organization and in terms of both time and space.

Ultimately, the goal of these technologies is to improve the design cycle of synthetic systems, leading to rapid prototyping of new designs. To enhance the process further, it would be useful to have large collections, or libraries, of biological components and systems that could be screened and evaluated fast. The elements in the library should be well designed, for instance based on computer models, high-throughput experimental systems or a combination of both.

When it comes to the cells themselves, there are a variety of ways researchers could optimize the design cycle. It would be interesting to determine the theoretical limits of how fast cells can reproduce, for example, in order to create cells that multiply as fast as possible and thus enable experiments to be carried out quickly. Increasing the number of cell types with open protocols and useful characteristics would also be useful. So far, researchers have relied largely on E. coli as their model organism, but other species may have more suitable properties for a specific purpose—for instance, thermophilic bacteria that thrive in very hot environments could prove useful in carrying out reactions requiring high temperatures.

At present, the success of a single engineering effort—such as the production of artemisinin, an anti-malarial drug that is now being produced cheaply by engineered microorganisms—requires a very large amount of resources and time. Synthetic biologists would want to create minimal cells by developing methods to quickly determine the cellular components sufficient to achieve a particular objective. Different cell lines might then be optimized in a task-specific manner for ideal performance. Today, most synthetic systems rely on tinkering with natural cell components, but it would clearly be useful to create entirely artificial cells with known functionality, the paragon of synthetic biology.

• J. Christopher Anderson, University of California, Berkeley

• Yizhi Cai, Virginia Tech

• John Glass, J. Craig Venter Institute

• Samie Jaffrey, Weill Medical College, Cornell University

• Kerry Love, Massachusetts Institute of Technology

• Ronald Milligan, The Scripps Research Institute

• Todd Peterson, Invitrogen, Life Technologies

• Nathan Price, University of Illinois at Urbana-Champaign

• Michael Strano, Massachusetts Institute of Technology

• Yinjie Tang, Washington University

• Valda Vinson, Science, AAAS

• Huimin Zhao, University of Illinois at Urbana-Champaign

• Lauren Whaley, University of Southern California

By Lauren Whaley, Graduate Science Writing Student, University of Southern California

Science is often driven by the technology available to it. At the 2009 National Academies Keck Futures Initiative on Synthetic Biology, an Interdisciplinary Research (IDR) team of researchers with experience in bioengineering, pharmacology, chemical engineering, genomics, computer technology and other disciplines came together to discuss what tools and technologies could support the engineering of biological systems. The team came up with ideas.

Before looking at the list, it is useful to step back and consider the IDR team’s definition of synthetic biology as the discipline in which humans make biology into useful things for society, such as drought-resistant food, drugs, and environmentally applicable organisms, such as a coral that sequesters carbon. Synthetic biology encompasses BOTH the design and construction of new biological parts (such as DNA) and systems (such as cells and entire organisms) as well as the re-design of existing systems (reconfiguring a cell so it behaves how the scientist wants it to). By creating new life and redesigning things that are already living, synthetic biologists will create anything they want.

To determine what tools would advance such a multidisciplinary and complicated field the team decided that examining Systems Biology would be a useful starting point. Systems Biology studies complex biological systems as integrated wholes, using many different tools, including DNA sequencing, epigenetics (looking at cells that have the same genotype, but a different phenotype), and protein-to-protein interactions. By understanding how natural biological systems work, synthetic biologists will be able to use them as parts or models for their made systems.

Dr. Wendell A. Lim said at the opening of the conference that by using synthetic biology as a model for natural biology, humans could begin to understand why and how things—like cells—break down and will learn a better way to design them so they don’t. Lim is a professor of Pharmacology and Biochemistry at the University of California, San Francisco.

As an organizational strategy, the IDR team decided to organize its tool list into Requests for Applications (RFAs), much as science foundations do. The team recognized that creating new tools will require funding, and that funding will likely come from grants. One of the major problems with

advancing the field has to do with finding financial backing for projects. Arranging the tool wish list into a request for applications seemed like a logical way to proceed. The IDR team’s goal in framing its report in terms of potential grants will encourage novel collaborations, and therefore, novel results, from scientists across many disciplines who may not currently work together.

Some characteristics of new tools include cheaper and faster ways to sequence and synthesize DNA. Sequencing is the process of finding out what series of base-pairs makes up a piece of DNA, while synthesis is the process of actually making the DNA from separate base pairs.

The IDR team also hoped for the creation of a “smart Web-cam” that could be inserted into a cell to see and understand everything that was going on without disturbing any of the cell’s function. Such an advancement would make it possible to understand how the system of a cell functions down to every working part, so that new or modified cells could be reliably produced.

The scientists also wanted to create a futuristic “photocopying machine” that could copy cells, tissues, organelles and whole organisms. This technology would be invaluable in making synthetic biology “scalable”—that is, able to produce useful products in large quantities.

Building on that, the team agreed that the ultimate accomplishment would be to develop a computer algorithm to mimic a cell. If such code existed, it could be typed into another item on the scientists’ wish list, a machine that could read that code and produce the cell it requested. A researcher imagines a cell and types its characteristics into a futuristic machine. Then, that machine, call it a “Star Trek replicator,” would rumble and shake and finally swing open its doors, revealing the physical form of cell or tissue the scientist had imagined. The machine would also print out an ingredient and recipe list to go along with the made concoction. One group member summed it up when he asked, “What could be more enabling than that?”

The IDR team organized its Request for Applications wish list into three categories: Synthesis, Analysis and Modeling.

1. Methods to reduce cost, increase length, and increase fidelity of DNA synthesis.

• Target: Reduction in the cost of DNA synthesis.

  • Ideally, costs would go down to $.005 / base pair. Right now, synthesis costs are about $1 per base pair.

• Includes methods focused on oligos on a chip, new chemistry for DNA synthesis, very long length reads, and engineering epigenetic DNA.

• Technologies for cheaper DNA synthesis that are currently in the Research and Development pipeline include: microfluidics, new chemistries, chip-based or bead-based techniques, novel polymerization and single-molecule sequencing.

2. New approaches to enable the synthesis of a broad range of biological entities, beyond simple polymers such as DNA and RNA.

Synthesis targets include the following cellular entities:

• Proteins

• Bacterial and archaeal cells

• Subcellular machines

• Eukaryotic cells

• Organelles

• Tissues

• Viruses

3. Methods to measure composition and biophysical states of biological systems. This includes the measurements of genes, proteins, metabolites, and interactions among biological molecules.

Ideally, the methods should be highly multiplexed, at high spatial and temporal resolution, and minimally invasive to the system.

Examples of technologies needed include:

• Novel detection methods, including reagent generation.

• Multi-parameter measurements with single molecule sensitivity (proteins, genes, metabolites, etc.).

• Development of organic/inorganic interfaces.

• Systems for measurement of endogenous interactions such as protein to protein, protein to nucleic acid, protein to small molecule, and nucleic acid to small molecule, particularly in living cells.

4. a. A tool that would design cells from scratch, using knowledge of natural cells.

Currently, the ability to synthesize exceeds science’s ability to model in

advance. The capacity to design cells from scratch would vastly increase the chances that one could create useful organisms for both medical and environmental uses. In order to design such cells, we need to more completely understand such things as protein to protein interactions and phenotype to genotype prediction.

b. Development of systems models via combined experimental and modeling approaches; methods to enable in silico design of cells.

The ultimate goal here would be to predict a genome sequence that would completely encode a cell with desired capabilities. In the near term, the group wants predictive cell re-design methods. Experimental data generation will be integrated with the modeling. Models should be formatted in ways that can be readily communicated to and implemented across the whole community. The information could be shared using common computer language, such as Systems Biology Markup Language (SBML).

The in silico design of cells could include:

• Introduction of whole pathways into cells, including transport between organisms.

• Aiding in the tuning of synthetic circuits.

• Elucidating the relationship between promoter sequence and protein expression.

• Remodeling a well-studied pathway, modulation of various parameters such as promoter strength, operons, terminator strength, etc.

• Methods for predicting toxic effects of small molecules.

• Harnessing high-throughput computational technology (e.g., Graphic Processing Unit) to solve previously untenable computational problems.

5. Additional ideas

In addition to discussing tools that can be used now and tools they’d like to use now, the members of the IDR team also imagined technologies that seemed a bit far-fetched, but that if made, would really help them engineer biology. One of those ideas is the Safety/Kill Switch.

The team would like to see the construction of a fail-safe mechanism to ensure regulatory compliance and public acceptance of cells made using synthetic biology. We seek proposals to develop genetic fail-safe mechanisms for use in prokaryotic and eukaryotic cells that would cause cell death.

• Prokaryotic and Eukaryotic devices to terminate synthetic cells.

• Eukaryotic devices that, for instance, might be used in therapeutic stem cells.

• Examples of fail-safe device activation could include chemical treatment, cell-division counters, and auxotrophy. Auxotrophy is when a cell dies because it is not being “fed” by the scientist. These synthetic cells could be engineered to only survive in very specific environments. If they escaped said environments, they would terminate.

If the scientists of the IDR team had their druthers, they would design every bit of DNA that goes into an organism so that they—the scientists —know exactly how it would behave now and in the future.

The more tools that can make what is currently hard easy, the more quickly scientists will see rapid increases in productivity and development. The scientists want all of this to advance medicine, come up with alternative energy sources, create food and develop more and more remarkable materials that could enhance human life on earth.