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BiosystemsSynthetic BiologyPeer-reviewed journal article

A Computational Design of a Programmable Biological Processor

Miha MoškoniD, Žiga PušnikiD, Lidija StanovnikiD, Nikolaj ZimiciD, Miha MraziD

PDFDOIPreprint

Abstract

Basic synthetic information processing structures, such as logic gates, oscillators and flip-flops, have already been implemented in living organisms. Current implementations of these structures are, however, hardly scalable and are yet to be extended to more complex processing structures that would constitute a biological computer. Herein, we make a step forward towards the construction of a biological computer. We describe a model-based computational design of a biological processor, composed of an instruction memory containing a biological program, a program counter that is used to address this memory and a biological oscillator that triggers the execution of the next instruction in the memory. The described processor uses transcription and translation resources of the host cell to perform its operations and is able to sequentially execute a set of instructions written within the so-called instruction memory implemented with non-volatile DNA sequences. The addressing of the instruction memory is achieved with a biological implementation of the Johnson counter, which increases its state after an instruction is executed. We additionally describe the implementation of a biological compiler that compiles a sequence of human-readable instructions into ordinary differential equations-based models. These models can be used to simulate the dynamics of the proposed processor. The proposed implementation presents the first programmable biological processor that exploits cellular resources to execute the specified instructions. We demonstrate the application of the proposed processor on a set of simple yet scalable biological programs. Biological descriptions of these programs can be written manually or can be generated automatically with the employment of the provided compiler.

Citations
3
FWCI
0.24
Field percentile
42th percentile

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