Sunday, September 21, 2008

Synthetic biology advances

The realization of synthetic biology, one of the cornerstone fields in this century’s life science revolutions, is a step closer this year with three important advances.

First, synthetic biology movement leader Drew Endy has arrived at Stanford from MIT. Knowing that people and tools are critical to the area’s development, he is assembling a world class curriculum and department to tackle the challenges of synthetic biology, estimating that Stanford is four years behind.

Second, more than 85 worldwide university teams have entered this year’s iGEM (international genetically engineered machines) competition. 900 students are estimated to be at MIT for the November 8-9 presentation of their work and the contest’s culmination. Previous year’s novel synthetic designs have included wintergreen and banana-scented E. coli bacteria, creating virtual-machine like computational platforms in cells and microbial cameras or light programmable biofilms.

Third, record attendance is expected at the fourth annual Synthetic Biology conference will be taking place October 10-12 at the Hong Kong University of Science and Technology.

Biology investigation, modeling, simulation and building
Synthetic biology is starting to have more process and rigor, particularly as articulated by Martyn Amos in Genesis Machines. Several areas have been simultaneously improving and coming together: biological system and process enumeration, 3D software modeling and simulation, and biological machine building. As CAD and EDA allowed semiconductor designers to achieve new levels of productivity and automate complex circuit design and test, so too are software tools aiding biology.

Bio-SPICE (Biological Simulation Program for Intra- and Inter-Cellular Evaluation) is an open source framework and software toolset for the modeling and simulation of spatio-temporal processes in living cells. The innovation process for synthetic biologists is now:

  • investigate the biological phenomenon or mechanisms
  • mathematically model the existing or novel phenomenon
  • use software simulation to test the model
  • build it in the lab with standardized off the shelf biological parts of synthesized DNA

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