I've got my issues with synthetic biology. Either synthetic biologists do something trivial dressed up in elaborate engineering language, or they achieve something impressive and complex the old fashioned way (the way molecular biologists have been doing it for decades) - genetic engineering through trial and error, with very little principles-based engineering involved.
What I want to see is a result that falls somewhere in between these two extremes: genetic engineering that's non-trivial, but not so complex that it's impossible to use simulation and the rudimentary quantitative design principles that are useful in biology.
There are some examples, and one of my favorites is the work of Ron Weiss, at Princeton's Department of Electrical Engineering. His general strategy is this:
An integral part of genetic circuit design is modeling the behavior of proposed circuits prior to their synthesis. In silicon chip fabrication, simulation tools help guide chip design to minimize the time and effort required for circuit construction. Similarly, genetic circuit development can also benet from modeling tools that can predict characteristics such as the steady state and dynamic behavior of the proposed system. The simulation tools are used to evaluate various network congurations for achieving particular functions, and to help rene existing and proposed designs. A unique and powerful capability of biological circuit engineering, as compared to its silicon chip counterpart, is our ability to exploit one of Mother Natures fundamental operating principles the process of evolution. Specically, through directed evolution, cells can be engineered to mutate their DNA sequences with the goal of optimizing circuit characteristics such as signal sensitivities and switching thresholds.
Weiss has done some impressive work in three steps:
1. Reframe some basic biological concepts in engineering language. For example, he renames the basic idea of a transcriptional repressor (a protein that binds DNA to shut off gene expression) an "inverter". Think of a repressor in terms of digital logic: when the repressor (the 'system input') is present (digital state '1'), the output is '0' (gene expression is off). When the repressor is absent (digital state '0'), the output is '1' (gene expression is on). By itself this kind of thing is trivial, but by talking about a biological inverter, you can begin to ask the right questions. What are the properties of the inverter? If I want to wire this together with some other biological device, what important parameters do I need to measure? Weis and his colleagues went ahead and characterized a real inverter, keeping in mind the need to link it up with other devices in future genetic engineering.
2. Weiss's lab decided to build a non-trivial system: a multicellular biological pulse generator. 10 years ago, pioneering synthetic biologists built primitive oscillators and toggle switches, but the problem is that few researchers have moved much beyond that - you still see marginally useful bistable toggle switches being published. Weiss's system is a step forward:
In this paper, we present a synthetic multicellular bacterial system that integrates positive and negative regulation of gene expression to achieve a transient response to cellcell communication. The system includes sender cells that can be induced to synthesize acyl-homoserine lactone (AHL), which then diffuses to nearby pulse-generating receiver cells.
He's wired up two types of E. coli: one type is a 'sender' cell, which emits a chemical signal. That chemical signal activates a 'receiver' cell, which in response to a steady signal, emits an oscillating response.
The real trick here was the advance modeling Weiss' lab did before physically wiring their system together. They ran simulations to find the key parameters of the system that controlled the shape of the pulse (the frequncy, the gain, the rise and fall times, etc.).
3. They then used their simulations to build the real system, and it worked. This is a multicellular device (in the sense that it involves sender and receiver cells), it's a system that has some reasonably complicated behavior, and modeling was a central and necessary part of the system design - in other words, you couldn't build this thing very well, with the desired set of final properties without modeling.
The Weiss group then went on to significantly improve on this system, using some interesting discoveries made during the original effort. (You can read their next paper on the lessons learned here.
This is the stuff to watch
Front page image from Weiss, et al. Molecular Systems Biology 2:2006.0028doi:10.1038/msb410007