Engineering Simple Living Systems: Parts, Modularity, and Simplicity for the Biological World

Thursday, October 21, 2004 - 7:00pm
Tom Knight, MIT Computer Science and Artificial Intelligence Laboratory

Biology performs some of our most sophisticated computation, and exhibits a robustness and adaptability uncharacteristic of our current generation of computers. One way of capitalizing on this realization is to learn and copy techniques from biology and apply them to building and improving our existing computational infrastructure. But we can also consider another agenda -- that of replacing or augmenting the electronics/silicon substrate of modern computation with a living, biochemical substrate. Important engineering advantages of this synthetic biology approach include the (unique) capability of self-replication, a straightforward interface to the chemical world, and access to the most sophisticated nanostructural assembly system, the ribosome. In the past several years, we have begun the long process of intentional engineering of behavior into simple living systems. In this talk, I will try to explain what we, and others, have accomplished to date, and to present a plan for further development of this technology. In particular, I will summarize some preliminary results in

  • embedding digital logic within living cells, using protein concentration as an intracellular signal;
  • signaling cell to cell as a mechanism for orchestrating more complex multicellular behavior;
  • identification, sequencing and annotation of a very simple living system, and the prospects for intentional further reduction of its complexity.

Think of this as the chassis, power supply, and manufacturing facility. I think of this technology as the field of microbial robotics -- with an agenda to take control over the existing sensory, computational, and actuation mechanisms of living cells. Gaining control over these mechanisms is an important stepping stone to bulk fabrication of information-rich nanoscale components, with important applications in (among many other areas) high performance and low power computation.