While biological systems are inherently fuzzy and contain imprecise parts that collectively interact, synthetic computation in living cells is mostly inspired by precise computer engineering principles. Lab research demonstrated that neuromorphic synthetic genetic circuits can be engineered in living cells, while monolithically integrating signal-processing and decision-making. Such circuits exhibit perceptual behavior of artificial neural networks, to build intelligent systems with collective computational capabilities. Principles and architectures of artificial neural networks were first transferred to synthetic gene networks, and were then applied in Escherichia coli cells to create fuzzy logic (e.g. smooth functions) exploiting cooperativity, feedback loops and binding reactions. In addition, lab research showed that circuits can be controlled according to the gradient-descent rule and back-propagation algorithm. Therefore, morphing rules shared across bio-inspired systems, will pave the way for emerging industrial and therapeutic applications with adaptive engineered cells.
- Circuits involve fewer components and execute more complex operations than their digital counterparts
Applications and Opportunities
- Living cells programmed to produce pharmaceutical compounds
- Microbiome bacteria programmed to detect and respond to changes in health status
- Gene circuits constructed to identify cancer cells based on integrated activity of multiple genes
- Synthetic gene networks as biosensors to detect toxins in water or to detect reactive oxgyen species in human gut and saliva