Autonomous Biomolecular Computer Modeled after Retroviral Replication

Nitta, Nao; Suyama, Akira

doi:10.1007/978-3-540-24628-2_20

Cited by 9 publications

(2 citation statements)

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“…In the Supplementary Information, we provide detailed descriptions of DNA strand-displacement species that emulate each proposed reaction, including the production and degradation reactions that supply energy to our system. However, the same abstract reactions could be compatible with a variety of other existing mechanisms [45][46][47] . Mechanisms that employ enzymatic activity could provide a higher energy density per molecule, consuming energy-source molecules at a lower rate compared to strand displacement networks.…”

Section: Discussionmentioning

confidence: 70%

Designing modular reaction-diffusion programs for complex pattern formation

Scalise

Schulman

2014

Technology

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Cells use sophisticated, multiscale spatial patterns of chemical instructions to control cell fate and tissue growth. While some types of synthetic pattern formation have been well studied1-6, it remains unclear how to design chemical processes that can reproducibly create similar spatial patterns. Here we describe a scalable approach for the design of processes that generate such patterns, which can be implemented using synthetic DNA reaction-diffusion networks7,8. In our method, black-box modules are connected together into integrated programs for arbitrarily complex pattern formation. These programs can respond to input stimuli, process information, and ultimately produce stable output patterns that differ in size and concentration from their inputs. To build these programs, we break a target pattern into a set of patterning subtasks, design modules to perform these subtasks independently, and combine the modules into networks. We demonstrate in simulation how programs designed with our methodology can generate complex patterns, including a French flag and a stick figure.

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Section: Discussionmentioning

confidence: 70%

Designing modular reaction-diffusion programs for complex pattern formation

Scalise

Schulman

2014

Technology

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“…RTRACS consists of individual modules which convert an input RNA strand with a specific sequence to an output RNA strand with another sequence (Figure 1C). In the design of a module, 21 an RNA strand is synthesized as an output after a logical operation that is achieved by a series of autonomously regulated hybridization and enzymatic reactions. This element contains a de novo combination of a reverse transcriptase, a DNA polymerase, an RNA polymerase, a ribonulease H, a primer DNA, and a converter DNA.…”

Section: Development Of Rtracsmentioning

confidence: 99%

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

2011

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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.

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