Before I begin reviewing the field of Synthetic Biology and discussing topics of interest, I just wanted to start by listing some of the questions that drive the research. Before you can find useful answers, you must always ask good questions.
What is Synthetic Biology and what do we want to gain by it? How is it useful? To what extent is it 'molecular biology' under a different name and why is it called 'synthetic'? What are we creating and how do we do it? What sort of methods are used?
I'll start answering those questions, but since the field is so new, feel free to pipe up and throw in your own two cents. I won't go into too many specifics or else I'll be writing all night. ;)
Synthetic biology is the study or design of biological molecules whose function did not previously (knowingly) exist in nature. This includes proteins that have catalytic activity (enzymes) or new structural binding properties, such as DNA binding regulators. This also includes engineered mRNA or DNA that has a specific, designed function. The most common example of an engineering biological system is a 'gene network' or 'gene circuit', which is a system of one or more genes whose function has been engineered to perform a specific task.
Why is synthetic biology useful? First, if we can first design a biological system and then build it, then we know how it all (mostly) works. By first predicting what will happen before you build it and then building it, you not only state the hypothesis that a) if built according to the design, it will work, but also that b) the biological system exists as you represented it in the design. So if the system doesn't behave as one might think, then something unknown must exist. Like all good scientific efforts, we have a hypothesis. But as engineers, if something doesn't work, we can investigate the problem and determine the solution.
Secondly, biology naturally interfaces with other biology. The most effective treatments of disease will naturally be biological molecules whose purpose has either evolved to treat the disease or which has been designed (by us) to do so. Not only can we engineer molecules to activate/inhibit/bind/etc, treating a disease, but we may also engineer the production, degradation, and localization of that molecule to control its effects and prevent the cure from becoming worse than the disease. We may also construct biological devices that detect the presence of other biological or chemical molecules (a biosensor), which would have tremendous use in medical diagnostics or defense.
So we can use synthetic biology to study biology while we build new and useful biological devices.
Which brings me to another aspect of synthetic biology: There's really a lot of engineers doing it. I'm also an engineer so I'm happy about that, but I've become accustomed to entering a seminar and being the only engineer there. Why does synthetic biology attract engineers, then? Well, I've been using the word 'design' over and over so that should be a clue.
How is synthetic biology different from molecular biology? Well, ... synthetic biology IS molecular biology, except more quantitative and precise. If you're reading a journal catering to molecular biologists, you'll typically see a model as a slightly cartoonish diagram depicting the interactions between a collection of proteins/etc and arrows showing the order of events. Basic questions are left unanswered by such models: How strong are the interactions? For every protein/etc in the diagram, are there additional interactions that will affect the model? Even though the interactions are listed, what are the dynamics that result? These answers may be counter-intuitive. One part of synthetic biology is to create a more defined, quantitative (and predictive) model of biological systems. Depending upon the level of detail, one could include the kinetic constants of all interactions (reaction/binding events), all unique chemical species, diffusion of all species, and membraned compartments. One outstanding question is what amount of detail is necessary to get predictive results. Conversely, what approximations may we make without sacrificing accuracy?
Finally, what are some of the methods that we use? To build these designs, common genetic engineering techniques are employed to cut and paste DNA into vectors, transform vectors (plasmids) into an organism, and (possibly) integrate the vector into the genome of the organism. These techniques have been used for the past 50 years and, while there are difficulties in extreme cases, it is relatively easy to construct something interesting. (By easy, I mean, it won't take one person their _entire_ PhD program...maybe only a year or two. ;) )
The real obstacle is not the experimental construction, but the design of the DNA sequences. In order to quickly design the system and avoid excessive experimental trial and error, mathematical tools must be used to analyze a design and ascertain whether it will function as expected. Relying on experimental construction alone would result in years of wasted effort. When using mathematical tools, there are two main questions: What quantitative, mechanistic model best predicts the dynamic behavior of the particular biological system of interest? What mathematical representation (and simulation) best reflects the process that occurs? These are two separation questions because one may take a good model and generate faulty equations with it, where the assumptions used in forming those equations are wrong. Solving those equations perfectly would reflect an incorrect answer, even though the model is perfectly valid. Conversely, forming and solving the most accurate and complete equations with the least number of assumptions would be futile if the model itself was not accurate and predictive.
This is where my research starts. As you could tell, I haven't gone into details. Over the next few weeks, I plan to review specific topics within the synthetic biology area, including experimental construction of different gene networks, the mathematical theory behind the most advanced simulators, and the guiding principles behind the optimal design of gene networks.
The format will be informal. I'll reference where it's necessary and include pictures when I can find them. For the math, there's no LaTex and so I will probably just upload PDFs.
This blog is not a substitute for my published papers. I spend a lot more time on them than I do on this (wisely, as you may agree). Feel free to post comments below.Posted by sali0090 at October 19, 2004 8:39 PM