As seen in previous entries in this blog, DNA
reaction networks are a substrate that enables the creation of computational
systems. These reaction networks have been a research topic for quite a long
time and they have their own set of advantages and operational challenges. One
of the present challenges is that up to the present day, all biomolecular
computing architectures have been conceived as digital deterministic ones,
while actual molecular signals are analog in concentration and involve
stochastic (or random) reaction events. These two signal characteristics, when
controlled in space and time, allow for the emergency of complex phenomena (such
as patterning or morphogenesis). Besides this, it is considered that the
integration of the analog/stochastic architectures would expand the
capabilities of DNA computing.
Qian’s group present the first step towards the integration of stochastic/analog systems in DNA circuits. The implementation is done through irreversible strand displacement reactions in which a given input can bind to 2 different gate molecules with a probability that depends on the gate concentration. The reaction with one gate or the other leads to two different outputs. This architecture, implemented in switches and signal splitters, works stochastically in the single molecule level. But, when observed in bulk, the stochastic elements in the circuit allow the transformation of a digital signal of n bits into an analog signal able to take 2n values.
Qian’s group present the first step towards the integration of stochastic/analog systems in DNA circuits. The implementation is done through irreversible strand displacement reactions in which a given input can bind to 2 different gate molecules with a probability that depends on the gate concentration. The reaction with one gate or the other leads to two different outputs. This architecture, implemented in switches and signal splitters, works stochastically in the single molecule level. But, when observed in bulk, the stochastic elements in the circuit allow the transformation of a digital signal of n bits into an analog signal able to take 2n values.
Figure 1. Example of a circuit with 3 stochastic elements (2 splitters
and 1 gate), and the different analog signal values that it can output.
This architecture has been proved to be able to
allow the implementation of feedback loops that allow adjustment of the
intensity of the output signal. Despite some limitations (in larger circuits,
fine tuning of the probability becomes harder thus requiring correction of the
initial input signal), the proposed probabilistic circuits are a bridge that
allows the interconnection of digital and analog computational circuits.
References:
Wilhelm, D., Bruck, J., & Qian, L.
(2018). Probabilistic switching circuits in DNA. Proceedings of the
National Academy of Sciences, 201715926.
No comments:
Post a Comment