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:: Posted by American Biotechnologist on 08-12-2013
Scientists have been using Adeno-associated viruses (AAVs) as a gene therapy vector for a number of years. Depite the fact that there are over 80 clinical trials that involve the use of AAVs worldwide (Wikipedia), AAVs lack the stability and specificity to deliver a gene to a specific target such as particular subregions of the brain.
In a new study taking place at Rice University, researchers have made use of computational and bioengineering methods to create new, benign viruses that can deliver DNA payloads to specific cells.
Their technique is premised upon an algorithm that predicts how parts of very large viruses can recombine by homing in on the viral protein sequences that work well together. According to senior scientist Jonathan Silberg, the researchers are using a hybrid approach to design and select the ideal mix of viruses which will deliver its gene payload in the most efficient manner.
“We’re treating them like Legos,” Silberg said. “We’re taking distantly related viruses that nature might not recombine very efficiently and looking for self-contained pieces of these proteins that can be swapped.”
:: Posted by American Biotechnologist on 03-16-2012
Merck, known outside the United States and Canada as MSD, today announced a collaboration to create the California Institute for Biomedical Research (Calibr), an independent, not-for-profit organization (501c3) established to accelerate the translation of basic biomedical research into innovative, new medicines to treat disease.
Calibr will be led by Peter G. Schultz, Ph.D., a world-renowned chemist and biotechnology entrepreneur. The Institute will offer academic scientists, around the world, a streamlined, efficient and flexible path for translating their biomedical research into novel medicines.
:: Posted by American Biotechnologist on 10-05-2010
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.”