This year we are implementing a cross-disciplinary project, mixing the knowledge from different fields to coodrinate them. Since we are constructing a compatible platform this year, we have designed and created modules, from wet-lab to dry-lab, which could be useful for future iGEM teams. In the process, we have met several problem and gain valuable experiences dealing it. We have built general and delicate hardware, models, software, and parts. Here is a short summary of our contribution:
- Hardware: the design and construction of NOX kit with desirable detection function.
- Model: the diffusion visualization program, the modeling procedure of a threshold switch.
- Software: a NLP-based search tool for iGEM standard parts, Ask NOX.
- Parts: a scalable threshold control switch, a general amplifier.
Hardware
NOX Kit is a general biology experiment environmental detection hardware kit that provides temperature control, temperature, and bioluminescence monitoring functions.
In this project, our bioamplifiers can amplify trace analytes and convert them into bioluminescence. Currently, expensive fluorescence microscopes are often required to detect bioluminescence reactions, which makes large-scale measurements difficult. To enable low-cost and high-throughput detection, we designed the NOX Kit to address this need.
NOX Kit has the following features (see the [[feature-description][Feature Description]] section for details):
- Modular Design: Extensible configuration, add/remove modules by needs
- Multiple Working Modules: Works standalone or in array for high-throughput
- User-friendly Interface: Simple clicks to collect and visualize data in real time
- Reproducible Low-Cost Design: Core board costs less than $1 (5 RMB). Streamlined for statble reproduction and mass production.
So for general users, NOX Kit is simple and easy to use - just connect and collect/visualize data. For developers, NOX Kit is low-cost and extensible - build upon it to create more low-cost, high-throughput biosensors.
Model
Our model consists of five main parts. Accordingly, our contributions focus on **three respects**: for iGEM community, for the theory of Biology Blocks Simulation associated with cybernetics, and for the open-source computer project, a diffusion visualization APP.
The first part is for iGEM community, we demonstrate the concept for three basic parts, which we build for iGEM community. For the self-renewal design, we compare and model two circuits to show the tradeoff of effectiveness and renewability, thus for our wet lab to choose. For the threshold guard, we calculate the rough interval of the threshold of our digitalizer, design a series of experiments to locate the real threshold, and finally get the well-fitted data. We further estimated the repressor strength to be more than 10 in terms of the relative strength intervals we divided. For other data fitting, we calculate the decay coefficient of the luminescence, and according to the relation between the luminescence and the product, we can further get the coefficient of our product for our modeling. For the hardware, we use COMSOL to build a simple 3D and 2D model to see the point diffusion, and thanks to the group-sensing response, we simplify the reaction in the container to be quick and complete. Then we can see the concentration in the container increases uniformly and rapidly.
The second contribution is for the theory of the Biology Blocks Simulation associated with cybernetics. We have found differential equations and reaction chains in the fields of biology and automatic control. In the field of control theory, there are perfect time-domain frequency-domain transformation rules to get the solution of multiple series differential equations, so as to conveniently calculate the transfer function from input to output. We found the correspondence of these rules in the biological field and established our single input single output model to model our calibration curve. However, due to the ideal assumptions and inadequate parameters, our system can confirm different output levels for different inputs but cannot do accurate quantitative research.
The third part is for the open-source computer project, we use basic physical rules to build a 2D diffusion model, to visualize this process, we innovatively use the concept of convolution in computer vision and use video frames to display. These are our first try. Further, we designed a perfect APP to realize the function of user-defined input diffusion initial concentration, real-time display of diffusion screen, and real-time calculation of cross-section material concentration at each moment.
All of our works are based on codes and software, and our codes are available in our gitlab.
Software
We designed a NLP-based search tool for iGEM standard parts, Ask NOX. Powered by Llama2 and BERT, it automatically summarizes the function and application of all the iGEM standard parts available on https://parts.igem.org, and creates a mapping between iGEM standard part name and the "summary" of its functions by model training. When searching on the Ask NOX website, users can input all functions of their desired BioBrick using a sentence in natural language instead of keywords.This tool functions like ChatGPT, improving the efficiency of searching and helping find out the optimal BioBrick among the huge database. This tool has been validated by our teammates and improved efficiency, so we hope that other teams can benefit from this tool when searching for a BioBrick. We also hope that this work can inspire other teams to explore the application of LLMs in a software project.
Parts
We tested two main scalable composite parts, developed in the Design part, tested in the Engineering part, and recorded in the Parts page.
The threshold guard possesses fabulous mathematic properties, which is both important for biosignaling and model construction. It enables a strong and strict control over the desired downstearm element and could be easily replaced by any other activator for target molecule.
The orthogonal quorum sensing module is identified for its capability of signaling in a highly specific and rapid way. We also perform rational design over the design, which adds more possibility to the optimization of this module.
We are hoping that these parts could further be explored, creating more modulated elements, contributing to the overall community of synthetic biology.
