COMPUTER EVOLUTION OF GENE CIRCUITS FOR CELL-EMBEDDED COMPUTATION, BIOTECHNOLOGY AND AS A MODEL FOR EVOLUTIONARY COMPUTATION
Chair: John H.Holland
This dissertation describes how to improve automated design and evolution in computers using the structuring of genetic programs in biological systems.It also shows how to 'reprogram' cells to perform useful tasks by embedding computer-evolved code in biological organisms.This reprogramming makes it possible to exploit in new contexts the cell's ability to self-repair, replicate, and generate chemicals, light, or motion at microscopic scales.
Biological systems evolve to create clever designs for solving problems, using mechanisms that do not explicitly involve knowledge or intelligence.The designs that result are often superior to the best human designs.The mechanisms involved reuse and recombine previously discovered structures -building blocks- to generate more complex designs.Such processes reduce problems that grow exponentially in difficulty with size to simpler problems with a hierarchical structure.
AI search techniques like Genetic Algorithms attempt to emulate this mechanism and harness it for automated programming and problem solving and have been successfully applied in a vast number of areas as a powerful black-box design and optimization tool.
However, current implementations still require significant human input in the initial problem formulation (whereas real evolution does not), which also results in different customizations for each problem, making it difficult to determine what characteristics are most useful for their performance. In contrast, biological evolution uses the same structures and mechanisms (DNA) to solve problems as different as flying or optimizing metabolic reactions.
I address these problems by identifying and testing key mechanisms and features responsible for the success of evolution from a computational perspective, and use them to explain previous results and build a single all-purpose implementation that, much like electronic circuits, avoids unwanted human overhead and customization by using the same sub-symbolic building blocks (gates and circuit patterns like loops, counters) for each problem.
I demonstrate its biological soundness by comparing simulation results to human-designed gene circuits and by developing a step-by-step mapping and fabrication of corresponding DNA sequences for a few examples (using genetic engineering to manipulate cellular aging, chemotaxis) which I then insert and test in cells.