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A model of spatio-temporal regulation within biomaterials using DNA reaction–diffusion waveguides

Cite this dataset

Dorsey, Phillip; Scalise, Dominic; Schulman, Rebecca (2024). A model of spatio-temporal regulation within biomaterials using DNA reaction–diffusion waveguides [Dataset]. Dryad. https://doi.org/10.5061/dryad.rxwdbrvb3

Abstract

In multi-cellular organisms, cells and tissues coordinate biochemical signal propagation across length scales spanning micrometres to metres. Designing synthetic materials with similar capacities for coordinated signal propagation could allow these systems to adaptively regulate themselves across space and over time. Here, we combine ideas from cell signalling and electronic circuitry to propose a biochemical waveguide that transmits information in the form of a concentration of a DNA species on a directed path. The waveguide could be seamlessly integrated into a soft material because there is virtually no difference between the chemical or physical properties of the waveguide and the material it is embedded within. We propose the design of DNA strand displacement reactions to construct the system and, using reaction-diffusion models, identify kinetic and diffusive parameters that enable super-diffusive transport of DNA species via autocatalysis. Finally, to support experimental waveguide implementation, we propose a sink reaction and spatially inhomogeneous DNA concentrations that could mitigate the spurious amplification of an autocatalyst within the waveguide, allowing for controlled waveguide triggering. Chemical waveguides could facilitate the design of synthetic biomaterials with distributed sensing machinery integrated throughout their structure and enable coordinated self-regulating programmes triggered by changing environmental conditions.

README: Data Repository for: A model of spatiotemporal regulation within biomaterials using DNA reaction-diffusion waveguides

https://doi.org/10.5061/dryad.rxwdbrvb3

Description of the data and file structure

The following document outlines the structure of the data package which encompasses all spatial simulations of the reaction-diffusion waveguide & well-mixed experimental analyses of the strand displacement reaction network. Datasets for each result are placed into a corresponding folder which contains MATLAB code used to analyze the data. In the next section, the structure of the repository is listed.

1. Spatial Models:

  • Idealized Waveguide: contains simulations of the abstract waveguide with and without patterned Sink, .csv, and .txt data output files for each model, and MATLAB scripts for analyzing waveguide dynamics. Each MATLAB file describes the analysis it conducts as well as the dataset it pulls from within the folder.
  • DNA Waveguide V1: contains datasets (.csv & .txt) for the first implementation of the strand displacement reaction-diffusion waveguide with and without patterned Sink in main text Figure 11a and 11b as well as MATLAB analysis code.
  • DNA Waveguide_V2_high_concentration: contains datasets (.csv & .txt) and MATLAB code for the 2nd instance of the strand displacement reaction-diffusion waveguide in main text Figure 12a.
  • DNA Waveguide Gradient: holds datasets (.csv & .txt) for the gradient patterned strand displacement reaction-diffusion waveguide in main text Figure 12b.

2. Well-Mixed Data:

  • Amplification toehold size Fig 7: contains a simulation of the well-mixed CRN with varying Carrier toehold size used for Figure 7.
  • Lock_amplification_SI Fig: holds dataset (.csv & .xlsx) and MATLAB code for analysis of the lock amplification strategy in SI Figure S4.
  • Perturbation and thresholded perturbation Fig 10: contains datasets (.csv, .xlsx, & .mat) and MATLAB scripts for analysis of delayed activation and extended thresholding of the well-mixed amplifier in Figure 10.
  • Unthresholded vs thresholded data Fig 8: hold datasets (.csv, .xlsx, .mat) and MATLAB code for analysis of unthresholded and thresholded amplification in Figure 8 and Figure 9.

Sharing/Access information

NA

Methods

The data was collected from fluorescence measurements of qPCR experiments and raw outputs from spatial simulations obtained from reaction-diffusion models. The resulting data from experiments and simulations were processed in Matlab.

Funding

National Science Foundation, Award: 1161941

National Science Foundation, Award: 1161941, CCF Division of Computing and Communication Foundations

United States Department of Energy, Award: DE-SC0015906

United States Department of Energy, Award: 221874

Johns Hopkins University, JHU Catalyst Award