RTRACS: A Modularized RNA-Dependent RNA Transcription System with High Programmability

Abstract

Creating artificial biological systems is an important research endeavor. Each success contributes to synthetic biology and adds to our understanding of the functioning of the biomachinery of life. In the construction of large, complex systems, a modular approach simplifies the design process: a multilayered system can be prepared by integrating simple modules. With the concept of modularity, a variety of synthetic biological systems have been constructed, both in vivo and in vitro. But to properly develop systems with desired functions that integrate multiple modules, researchers need accurate mathematical models. In this Account, we review the development of a modularized artificial biological system known as RTRACS (reverse transcription and transcription-based autonomous computing system). In addition to modularity, model-guided predictability is an important feature of RTRACS. RTRACS has been developed as an in vitro artificial biological system through the assembly of RNA, DNA, and enzymes. A fundamental module of RTRACS receives an input RNA with a specific sequence and returns an output RNA with another specific sequence programmed in the main body, which is composed of DNA and enzymes. The conversion of the input RNA to the output RNA is achieved through a series of programmed reactions performed by the components assembled in the module. Through the substitution of a subset of components, a module that performs the AND operation was constructed. Other logical operations could be constructed with RTRACS modules. An integration of RTRACS modules has allowed the theoretical design of more complex functions, such as oscillation. The operations of these RTRACS modules were readily predicted with a numerical simulation based on a mathematical model using realistic parameters. RTRACS has the potential to model highly complex systems that function like a living cell. RTRACS was designed to be integrated with other molecules or molecular devices, for example, aptazymes, cell-free expression systems, and liposomes. For the integration of these new modules, the quantitative controls of each module based on the numerical simulation will be instructive. The capabilities of RTRACS promise to provide models of complex biomolecular systems that are able to detect the environment, assess the situation, and react to overcome the situation. Such a smart biomolecular system could be useful in many applications, such as drug delivery systems.

Molecular Robotics: A New Paradigm for Artifacts

“…Sensing devices consisting of a complex of RNA and proteins have been proposed. 27) As for computing devices, a computational system called RTRACS 28) by Suyama et al, combining enzymatic reactions of RNA and DNA is currently under development.…”

“…A number of DNA actuators have been proposed so far, including DNA tweezers, 28) DNA walkers 29) and DNA motors. 30) Further, it is possible to control DNA walkers by means of DNAzymes.…”

Parallel Networks

“…The collaboration between computer scientists and synthetic biologists is critical for the continued advancement of synthetic biology technology and its applications [Bedau 2003;Benenson 2012;Ellis et al 2009;Kaern et al 2005;Kalmar et al 2009;Lanza et al 2012;Myers 2014a, 2014b;Roehner et al 2014]. While machine learning is a fundamental component to AI, this important topic has been reviewed elsewhere [Ayukawa et al 2011;Kitano 2002;Lee and Marcotte 2009;Vizeacoumar et al 2009;Yoo et al 2014], and is not discussed in detail in this review due to space constraints.…”

Scaling down DNA circuits with competitive neural networks

“…Besides those DNA-only circuits, hybrid DNA/enzyme circuits have been reported recently. Such molecular networks-which use DNA for encoding function and signalling but enzymes for production and degradation of signals-have emerged as a general approach to construct circuits with dynamic behaviours such as oscillations or bistability [43][44][45][46][47][48][49].…”