Translating weighted probabilistic bits to synthetic genetic circuits.

Abstract

Synthetic genetic circuits in plants could be the next technological horizon in plant breeding, showcasing potential for precise patterned control over expression. Nevertheless, uncertainty in metabolic environments prevents robust scaling of traditional genetic circuits for agricultural use, and studies show that a deterministic system is at odds with biological randomness. We analyze the necessary requirements for assuring Boolean logic gate sequences can function in unpredictable intracellular conditions, followed by interpreted pathways by which a mathematical representation of probabilistic circuits can be translated to biological implementation. This pathway is utilized through translation of a probabilistic circuit model presented by Pervaiz that works through a series of bits; each composed of a weighted matrix that reads inputs from the environment and a random number generator that takes the matrix as bias and outputs a positive or negative signal. The weighted matrix can be biologically represented as the regulatory elements that affect transcription near promotors, allowing for an electrical bit to biological bit translation that can be refined through tuning using invertible logic prediction of the input to output relationship of a genetic response. Failsafe mechanisms should be introduced, possibly through the use of self-eliminating CRISPR-Cas9, dosage compensation, or cybernetic modeling (where CRISPR is clustered regularly interspaced short palindromic repeats and Cas9 is clustered regularly interspaced short palindromic repeat-associated protein 9). These safety measures are needed for all biological circuits, and their implementation is needed alongside work with this specific model. With applied responses to external factors, these circuits could allow fine-tuning of organism adaptation to stress while providing a framework for faster complex expression design in the field.