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Part 1: Defining the field and its capabilities

Question: How is synthetic biology different from existing, related fields like genetic engineering and metabolic engineering?

It is important to note that there are different synthetic biology groups pursuing distinct agendas. Some go after applications. For example, Jay Keasling and colleagues at UC Berkeley have worked to engineer yeast to produce the antimalarial artemisinin cheaply. It is difficult to distinguish synthetic biology groups with application goals from groups working in a field such as genetic engineering. One distinguishing characteristic is that the current synthetic biology application projects have access to more information and technology, allowing them to tackle bigger problems in a more informed way.

Others in synthetic biology pursue foundational, enabling technologies (like Drew Endy's or Tom Knight's research groups at MIT). The goal of these foundational groups is to standardize the engineering of biology to make it more predictable. These groups borrow concepts from traditional engineering disciplines to enable the construction of multi-component biological systems using reusable and standard biological parts. The belief of these foundational groups is that in the long run, this standardized, less ad hoc approach to engineering biology will become the accepted approach to tackling any given application.

Despite the diverse agendas within the synthetic biology community, points of agreement can be found. These include the belief that there is enormous potential of biology as a substrate for engineering, that biological engineering is hard and that it must be pursued in a thoughtful and responsible fashion.

Question: Why is biology so hard to engineer now?

An important effort in synthetic biology aims to develop improved foundational technologies for reusing genetic elements. If successful, biological engineers might work with the confidence enjoyed by other engineering disciplines who don't, for example, need to build a bridge to know if it will fall down. Furthermore, once tamed, the features that make the engineering of biological systems difficult may yield novel systems capable of operations and behaviors not achievable by other engineering methods.

Biology has several features that are difficult or lacking in other engineering mediums including

  1. Biological systems can manufacture materials and chemicals fast, on very small or very large scales, with minimal toxic byproducts and under gentle reaction conditions
  2. Biological systems can evolve.
  3. Most importantly, biological organisms can self-replicate.

Genomic DNA sequences have been described as the programs that run biological machines, analogous to the computer programs that run PCs. Reading and interpreting DNA sequence (strings of A's,T's,G's and C's) is just as challenging as reading and interpreting binary code (strings of 0's and 1's). Imagine that someone has given you a printout of the binary code for the Microsoft Windows operating system (without telling you what it is) and asks you what the program does. It would be an incredibly difficult question to answer. Similarly, understanding DNA sequence information is also challenging. In fact, it is an even more difficult problem because at least Microsoft Windows was written by humans in a reasonably rational way. DNA sequences were written by evolution and so our ability to understand them is limited for now. Synthetic biology seeks to take the next step and actually "write new code" so to speak. Thus, given our lack of understanding of naturally occuring DNA code, it is not surprising that synthetic biology poses a challenge currently.

Part 2: Defining the community

Q1: What is the nature of the synthetic biology community

*Approaches for answering:

    • estimates of numerical strength (both commercial and academic)
    • international distribution?
    • how are they funded?
    • Maybe we should also describe the typical backgrounds of those working in SB? Biologists, electrical engineers, computer scientists etc.

To get numbers may want to count: meeting attendance numbers, # of SB departments, jobs that use SB in description, number of papers published with SB in title or abstract and where investigators are housed.

As important as who is doing the work today is who will be doing the work tomorrow, so we may want to cite iGEM growth. One measure of the growth of the field is the international Genetically Engineered Machines competition or iGEM. iGEM is a competition in which teams of students from various universities compete to design, build and test an engineered biological system from standard biological parts. iGEM has its roots in a class held at MIT in January 2003 with ~20 students. It then expanded to an intercollegiate competition in 2004 between five U.S. schools. Currently, in 2006, there are ~39 universities and ~400 participants from across the world (see map).

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