Computing mathematical functions with chemical reactions via stochastic logic.

Area of Science:

• Biochemistry
• Computational Biology
• Digital Logic Design

Background:

• Stochastic logic utilizes random streams of binary values to represent probabilistic information.
• Molecular systems represent variables through the concentration of molecular species.
• Mathematical functions can be computed using logic gates in stochastic logic circuits.

Purpose of the Study:

• To present a novel strategy for computing mathematical functions using molecular reactions.
• To demonstrate the design of chemical reaction networks based on truth tables for analog functions.
• To establish a link between stochastic logic and molecular systems for computation.

Main Methods:

• Translating mathematical functions computed by stochastic logic circuits into chemical reaction networks.
• Designing chemical reaction networks based on truth tables specifying analog functions.
• Utilizing DNA strand displacement with DNA "concatemers" for experimental implementation.

Main Results:

• Simulations confirm accurate and robust computation by the reaction networks, even with variations in reaction rates.
• Reaction networks were developed to compute functions such as arctan, exponential, Bessel, and sinc.
• The methodology provides a general and efficient approach for molecular computation.

Conclusions:

• The proposed strategy offers a viable method for performing complex mathematical computations using molecular reactions.
• The developed chemical reaction networks are accurate and robust, showing potential for practical applications.
• DNA strand displacement presents a promising experimental platform for implementing these molecular computations.