Phase-based genetic logic circuits
Rudge, T. J.; Vidal, G.
Show abstract
Bio-computation is the implementation of computational operations using biological substrates, such as cells engineered with synthetic genetic circuits. These genetic circuits can be composed of DNA parts with specific functions, such as promoters that initiate transcription, ribosome binding sites that initiate translation, and coding sequences that are translated into proteins. Compositions of such parts can encode genetic circuits in which the proteins produced by genes regulate each other in different ways. The expression level of each gene may be considered as a signal which may be high or low, encoding binary logic, and the combinations of genes can then encode logic circuits. This is equivalent to the level-based logic used in modern electronic computers in which the voltage forms the logical high or low states. A different approach to binary logic is to encode the high and low states in the phase of an oscillating signal. In this approach a signal in phase with a reference represents high, and a signal antiphase with the reference represents the logical low state. We present here designs and models for phase-based genetic NOT, OR, AND, MAJORITY and complementary MAJORITY gates, which together form multiple complete logical sets. We derive analytical expressions for the optimal model parameters for circuit function. Our simulation results suggest that this approach to genetic logic is feasible and could be less sensitive to gate input-output mismatch than level-based genetic logic. To demonstrate the scaleability of phase-based genetic logic, we used our complementary MAJORITY and NOT gates to design and simulate a fully functional 4-bit ripple adder circuit, and showed that it was robust to molecular noise.
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- The interaction between dynamic ligand signaling andepigenetics in Notch-induced cancer metastasis 92%
- Integrating multimodal data sets into a mathematical framework to describe and predict therapeutic resistance in cancer 92%
- GIFT: New method for the genetic analysis of small gene effects involving small sample sizes. 91%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.