Several weeks ago we shared a video where Dr. Steve Jones discusses how bench scientists will soon be spending more time (if they aren’t already) at the computer than at the actual bench. A recent article by Robert Fortner on the ars technica science news site may indicate that this trend may not necessarily be beneficial.
In his article “Neither models nor miracles: a look at synthetic biology”, Fortner analyzes the rise of systems biology , (a science that uses computing power to create mathematical models to solve biological problems), and claims that ever-expanding data is complicating the system-based approach and will ultimately lead to its failure. In other words, the complexity of biological systems are too difficult to predict with currently available computing power and mathematical models. In the words of Mike Williamson from the University of Sheffield (as quoted in the article) “There are so many unknowns that it seems we are condemned to spend many years collecting data before we can even start to think about modeling what is going on.”
Next, Fortner tackles the world of synthetic biology which involves the design and building of engineered biological systems to study a wide array of biological functions. A great example of how synthetic biology can play an important part of technical advancement is the engineering of viruses to create portable and long-lasting rechargeable batteries. (See video below).
“Synthetic biologists” (note…they are still real people) create organism using standardized, interchangeable biological parts and try to figure out what’s going on by swapping them out and studying how such changes affect the organism. While this may seem like an elegant approach, scientists have been unable to engineer in more than 6 promoters at a time which has significantly hindered the feasibility of this model. Furthermore, the synthetic nature of this brand of science means that it may not necessarily translate into a practical model of research. Similar to the systems approach to biology, the complexity of the nature may render any attempt to create a synthetic biological model futile. In the words of Nitin Baliga from the The Institute for Systems Biology “I have serious reservations about using a simplified system—it defeats the entire premise of investigating complexity.”
For more information, read “Neither models nor miracles: a look at synthetic biology”