Synthetic Biology

Parts, Devices and Applications

Christina Smolke, Sang Yup Lee, Jens Nielsen, Gregory Stephanopoulos, Christina Smolke

• Publisher: Wiley-Blackwell
• Year: 2018
• ISBN:
  9783527688098
• Edition: 1
• Topic: Biological Sciences
• Subtopic: Biotechnology
• Index: Biological Sciences

A review of the interdisciplinary field of synthetic biology, from genome design to spatial engineering.

Written by an international panel of experts, Synthetic Biology draws from various areas of research in biology and engineering and explores the current applications to provide an authoritative overview of this burgeoning field. The text reviews the synthesis of DNA and genome engineering and offers a discussion of the parts and devices that control protein expression and activity. The authors include information on the devices that support spatial engineering, RNA switches and explore the early applications of synthetic biology in protein synthesis, generation of pathway libraries, and immunotherapy.

Filled with the most recent research, compelling discussions, and unique perspectives, Synthetic Biology offers an important resource for understanding how this new branch of science can improve on applications for industry or biological research.

Part I
DNA Synthesis and Genome Engineering

Chapter 1
Competition and the Future of Reading and Writing DNA

Robert Carlson

Biodesic and Bioeconomy Capital, 3417 Evanston Ave N, Ste 329, Seattle, WA, 98103, USA

Constructing arbitrary genetic instruction sets is a core technology for biological engineering. Biologists and engineers are pursuing even better methods to assemble these arbitrary sequences from synthetic oligonucleotides (oligos) [1]. These new assembly methods in principle reduce costs, improve access, and result in long sequences of error‐free DNA that can be used to construct entire microbial genomes [2]. However, an increasing diversity of assembly methods is not matched by any obvious corresponding innovation in producing oligos. Commercial oligo production employs a very narrow technology base that is many decades old. Consequently, there is only minimal price and product differentiation among corporations that produce oligos. Prices have stagnated, which in turn limits the economic potential of new assembly methods that rely on oligos. Improvements may come via recently demonstrated assembly methods that are capable of using oligos of lower quality and lower cost as feedstocks. However, while these new methods may substantially lower the cost of gene‐length double‐stranded DNA (dsDNA), they also may be economically viable only when producing many orders of magnitude with more dsDNA than what is now used by the market. The commercial success of these methods, and the broader access to dsDNA they enable, may therefore depend on structural changes in the market that are yet to emerge.

1.1 Productivity Improvements in Biological Technologies

In considering the larger impact of technological monoculture in DNA synthesis, it is useful to contrast DNA synthesis and assembly with DNA sequencing. In particular, it is instructive to compare productivity estimates of commercially available sequencing and synthesis instruments (Figure 1.1). Reading DNA is as crucial as writing DNA to the future of biological engineering. Due to not just commercial competition but also competition between sequencing technologies, both prices and instrument capabilities are improving rapidly. The technological diversity responsible for these improvements poses challenges in making quantitative comparisons. As in previous discussions of these trends, in what follows I rely on the metrics of price [$/base] and productivity [bases/person/day].

Figure 1.1 Estimates of the maximum productivity of DNA synthesis and sequencing enabled by commercially available instruments. Productivity of DNA synthesis is shown only for column‐based synthesis instruments, as data for sDNA fabricated on commercially available DNA arrays is unavailable; exceptions are discussed in the text. Shown for comparison is Moore’s law, the number of transistors per chip.

(Intel; Carlson, 2010 [3]; Loman et al. 2012 [4]; Quail et al. 2012 [5]; Liu, 2012 [6].)

Figure 1.1 also directly compares the productivity enabled by commercially available sequencing and synthesis instruments to Moore’s law, which describes the exponential increase in transistor counts in CPUs over time. Readers new to this discussion are referred to References 3 and 4 for in‐depth descriptions of the development of these metrics and the utility of a comparison with Moore’s law [3, 7]. Very briefly, Moore’s law is a proxy for productivity; more transistors enable greater computational capability, which putatively equates to greater productivity.

Visual inspection of Figure 1.1 reveals several interesting features. First, general synthesis productivity has not improved for several years because no new instruments have been released publicly since about 2008. Productivity estimates for instruments developed and run by oligo and gene synthesis service providers are not publicly available. ¹

Second, it is clear that DNA sequencing platforms are improving very rapidly, now much faster than Moore’s law.

Moore’s law and its economic and social consequences are often used to benchmark our expectations of other technologies. Therefore, developing an understanding of this “law” provides a means to compare and contrast it with other technological trends.

1.2 The Origin of Moore’s Law and Its Implications for Biological Technologies

Moore’s law is often mistakenly described as a technological inevitability or is assumed to be some sort of physical phenomenon. It is neither; Moore’s law is a business plan, and as such it is based on economics and planning. Gordon Moore’s somewhat opaque original statement of what became the “law” was a prediction concerning economically viable transistor yields [8]. Over time, Moore’s economic observation became an operational model based on monopoly pricing, and it eventually enabled Intel to outcompete all other manufacturers of general CPUs. Two important features distinguish CPUs from other technologies and provide insight into the future of trends in biological technologies: the first is the cost of production, and the second is the monopoly pricing structure.

Early on Intel recognized the utility of exploiting Moore’s law as a business plan. A simple scaling argument reveals the details of the plan. While transistor counts increased exponentially, Intel correspondingly reduced the price per transistor at a similar rate. In order to maintain revenues, the company needed to ship proportionally more transistors every quarter; in fact, the company increased its shipping numbers faster than prices fell, enabling consistent revenue to grow for several decades. This explains why Intel former CEO Andy Grove reportedly constantly pushed for an even greater scale [9].

In this sense, Moore’s law was always about economics and planning in a multibillion‐dollar industry. In the year 2000, a new chip fab cost about $1 billion; in 2009, it cost about $3 billion. Now, according to The Economist, Intel estimates that a new chip fab costs about $10 billion [9]. This apparent exponential increase in the cost of semiconductor processing is known as Rock’s law. It is often argued that Moore’s law will eventually expire due to the physical constraints of fabricating transistors at small length scales, but it is more likely to become difficult to economically justify constructing fabrication facilities at the cost of tens to hundreds of billions of dollars. Even through the next several iterations, these construction costs will dictate careful planning that spans many years. No business spends $10 billion without a great deal of planning, and, more directly, no business finances a manufacturing plant that expensive without demonstrating a long‐term plan to repay the financiers. Moreover, Intel must coordinate the manufacturing and delivery of very expensive, very complex semiconductor processing instruments made by other companies. Thus Intel’s planning and finance cycles explicitly extend many years into the future. New technology has certainly been required to achieve each planning goal, but this is part of the ongoing research, development, and planning process for Intel.

Moore’s law served a secon...