Publisher's Synopsis
Forward engineering of synthetic genetic circuits-based artificial information
processing devices, termed synthetic genetic devices, has been carried out by applying
hierarchical computer and electronic design principles to biology in order to
accomplish cellular biocomputing. Such devices with high computing power can
process higher level information in microbes and mammalian cells. Expansion of the
computing power of these devices with increasing complexity might lead to the
solving of complex computing problems in certain areas where living cell-based
biocomputing might outperform traditional computers.
This thesis centrally focuses on distributed computing in engineered bacteria. Here
artificial neural network (ANN) is adapted as a computing system in living
Escherichia coli cells to develop a design framework for building complex computing
functions including a 2-to-4 decoder and a 4-to-2 priority encoder. The basic concept
of single layer ANN architecture is mapped into engineered E. coli cells where
individual engineered bacterial cells, termed bactoneurons, carry cellular devices and
act as artificial neuro-synapses. A complete set of rules is established to distribute the
truth table of a function among multiple fragmented truth tables followed by the
derivation of unit bactoneurons from those fragmented truth tables without
considering the electronic circuit design of that function. When those bactoneurons are
cultured together, they work similar to a single layer ANN type architecture and give
rise to the original computing function.
