Dataset Information

Modular protein-oligonucleotide signal exchange.

ABSTRACT: While many methods are available to measure the concentrations of proteins in solution, the development of a method to quantitatively report both increases and decreases in different protein concentrations in real-time using changes in the concentrations of other molecules, such as DNA outputs, has remained a challenge. Here, we present a biomolecular reaction process that reports the concentration of an input protein in situ as the concentration of an output DNA oligonucleotide strand. This method uses DNA oligonucleotide aptamers that bind either to a specific protein selectively or to a complementary DNA oligonucleotide reversibly using toehold-mediated DNA strand-displacement. It is possible to choose the sequence of output strand almost independent of the sensing protein. Using this strategy, we implemented four different exchange processes to report the concentrations of clinically relevant human ?-thrombin and vascular endothelial growth factor using changes in concentrations of DNA oligonucleotide outputs. These exchange processes can operate in tandem such that the same or different output signals can indicate changes in concentration of distinct or identical input proteins. The simplicity of our approach suggests a pathway to build devices that can direct diverse output responses in response to changes in concentrations of specific proteins.

SUBMITTER: Agrawal DK

PROVIDER: S-EPMC7337525 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Modular protein-oligonucleotide signal exchange.

Agrawal Deepak K DK Schulman Rebecca R

Nucleic acids research 20200701 12

While many methods are available to measure the concentrations of proteins in solution, the development of a method to quantitatively report both increases and decreases in different protein concentrations in real-time using changes in the concentrations of other molecules, such as DNA outputs, has remained a challenge. Here, we present a biomolecular reaction process that reports the concentration of an input protein in situ as the concentration of an output DNA oligonucleotide strand. This met ...[more]

PMID: 32442276

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Project description:BACKGROUND: Combinatorial complexity is a challenging problem in detailed and mechanistic mathematical modeling of signal transduction. This subject has been discussed intensively and a lot of progress has been made within the last few years. A software tool (BioNetGen) was developed which allows an automatic rule-based set-up of mechanistic model equations. In many cases these models can be reduced by an exact domain-oriented lumping technique. However, the resulting models can still consist of a very large number of differential equations. RESULTS: We introduce a new reduction technique, which allows building modularized and highly reduced models. Compared to existing approaches further reduction of signal transduction networks is possible. The method also provides a new modularization criterion, which allows to dissect the model into smaller modules that are called layers and can be modeled independently. Hallmarks of the approach are conservation relations within each layer and connection of layers by signal flows instead of mass flows. The reduced model can be formulated directly without previous generation of detailed model equations. It can be understood and interpreted intuitively, as model variables are macroscopic quantities that are converted by rates following simple kinetics. The proposed technique is applicable without using complex mathematical tools and even without detailed knowledge of the mathematical background. However, we provide a detailed mathematical analysis to show performance and limitations of the method. For physiologically relevant parameter domains the transient as well as the stationary errors caused by the reduction are negligible. CONCLUSION: The new layer based reduced modeling method allows building modularized and strongly reduced models of signal transduction networks. Reduced model equations can be directly formulated and are intuitively interpretable. Additionally, the method provides very good approximations especially for macroscopic variables. It can be combined with existing reduction methods without any difficulties.

Dataset Information

Modular protein-oligonucleotide signal exchange.

Publications

Modular protein-oligonucleotide signal exchange.

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