Watch this detailed overview to understand everything necessary for Tadpole 💡:
- Model on which it's based
- User-friendly / Developer tools
- Essential Inputs and Outputs Format
Access Points
💻 GitLab Source Code (Official Repository)
All Tadpole source code is openly available on our GitLab repository under an OSI-approved license.
🌐 Web Deployment (For Non-Experts)
A live web deployment of Tadpole that can be accessed directly from any browser, no installation required.
📋 Docker, Package & API Instructions (README)
Step-by-step installation guide including instructions for Docker, the Python package, and the Tadpole API.
Overview
A comprehensive introduction to Tadpole and its capabilities
Tadpole designs RNA switches by linking a Functional RNA Element (FRE)
(e.g., frameshift element, ribozyme, Stop Codon Readthrough[3]) with a Conformational RNA Element (CRE)
(e.g., Aptamer[4], miniRNA, temperature-sensitive motif). The software generates a linker and designs a system so that in the OFF state the CRE blocks the FRE,
and in the ON state the FRE is released and functional. 💡
Relevance - Replace, Improve, Build upon
Traditional protein-based regulators are powerful but often slow,
noisy, or difficult to deliver.
RNA-level control is faster and more versatile, yet up until this moment, there has not been a tool
capable of systematically designing RNA switches from arbitrary parts.
Tadpole takes RNA regulation to the next level by offering a generalised switch designer.
Any CRE (any aptamer, any mini-RNA, or any other conformational element) can be combined with any FRE of interest
to produce next-generation RNA switches in seconds. This opens a vast design space, from temperature-sensitive or
metabolite-responsive systems to regulatory RNAs never before explored.
Taking as an example past IGEM teams , Tadpole can directly replace outdated systems,
such as traditional kill-switches, improve existing designs, like riboswitches, or build upon prior work, for instance,
mini-RNAs. These examples illustrate how Tadpole can transform the landscape of RNA regulation, serving as a practical
tool for future teams, accelerating design and enabling innovations that would have previously required weeks of
trial-and-error or deep expertise.
Validation & Accessibility
Tadpole has been validated both in silico and in the wet lab
with a Stop Codon Readthrough[3] element and an
aptamer.
Its web interface allows users to design RNA switches without programming or using a terminal,
making the tool accessible to iGEM teams and non-experts. For local deployment, Tadpole can be installed easily via
Docker, ensuring reproducible execution across systems.
SBOL3 export ensures interoperability with the wider synthetic biology ecosystem.
In Silico Validation: Comprehensive computational testing
ensures structural stability and predicted functionality of designed RNA switches.
Wet Lab Validation: Experimental verification conducted with
Stop Codon Readthrough[3]
element and
aptamer systems,
demonstrating real-world functionality. Of the 4 designed systems,
all 4 proved to be switches (see the Experimental Validation section for more information).
Accessibility Features:
Web interface designed for non-experts,
Docker deployment for reproducibility, and
SBOL3 export for ecosystem integration.
Reproducibility:
Docker deployment ensures reproducible execution across systems, and the
user-defined seed for the
Genetic Algorithm (GA) guarantees consistent and repeatable results.
Since the Genetic Algorithm (GA) uses random choices, wihtout a seed,
user could not help others reproduce their results. That is bad for reproductability, so we added the seed to allow users reproduce the same results.
Extensibility & Sustainability
Beyond the interface, Tadpole can be used as a
Python package
and through an API,
supporting ViennaRNA[1]
and Biopython[2] for seamless integration into computational pipelines.
Its Dockerized code,
openly available on GitLab under an
open-source license, guarantees
maintainability, reproducibility, and future extensibility by other groups.
Python Package: Install via pip for integration into existing Python workflows and computational pipelines.
API Access: RESTful API enables integration into web applications and automated systems.
ViennaRNA[1] & Biopython[2] Support: Built-in compatibility with standard bioinformatics tools and libraries.
Documentation: Comented code with architecture diagrams and an evalutaion on
the two algorithms that can be used and recomendations on when to use each
(see
Software Technicalities page).
The
README file includes instructions
on how to download and use the code for each of the
possibilities,
as well as instructions on how to use the execution scripts.
Open Source: Modular, documented code, made available under an OSI-approved license for community development.
Where to Find the Information
In this page, you will find the above informaiton detailed and extended.
For more information, see the software-technicalities page,
where you can find diagrams of the different algorithms used, detailed explanations and
tips for the inputs, and explanation on the results presentation.
💡
Workflow of the Software Tool.
The Need for TADPOLE
Moving beyond traditional transcriptional control to unlock RNA-level regulation
🔓 Unlocking RNA control beyond the old playbook
(see more on Model)
Synthetic biology aims to program biological systems to perform defined tasks. For decades, the field has primarily relied on transcriptional control, using DNA elements like promoters and transcription factors to regulate gene expression. While this approach has enabled countless breakthroughs, it comes with significant limitations.
Transcriptional regulation is a complex and often slow process. It involves numerous factors like the binding of proteins to DNA, the intricate dynamics of RNA polymerase, and the presence of regulatory sequences (enhancers and silencers). This complexity can lead to undesirable effects such as cross-talk between low-specificity transcription factors, stochastic noise in gene expression, and slow response times, as the process involves multiple steps from DNA to protein. These shortcomings create a bottleneck for advanced applications like biomanufacturing and gene therapy, which demand precise, rapid, and multi-layered control.
Recognising these limitations, the field has increasingly explored alternative regulatory layers, particularly at the translational level. For a long time, the design of translational control tools has been limited to basic approaches like simple riboswitches and small RNAs (sRNAs), which primarily function by capturing the Ribosome Binding Site (RBS) or Internal Ribosome Entry Site (IRES) to block or enable protein synthesis. While effective, these methods often represent a basic, "one-size-fits-all" solution. We call this Riboswitches, small RNAs, etc. a Conformational RNA Element (CRE). A CRE is an RNA sequence that adopts a particular structure in response to external factors like small molecules (ligands), temperature, or sRNAs.
A new design space: Functional RNA Elements
The next leap in this field is to use more sophisticated Functional RNA Element (FRE) to engineer highly specific and efficient translational switches. An FRE is a sequence of RNA that, when folded into a specific secondary or tertiary structure, performs a biological function without the need for a protein catalyst. The crucial distinction with a Conformational RNA Element (CRE) is that a FRE's folded shape performs a specific function, unlike a CRE, which simply adopts a particular structure in response to external factors like aptamers, temperature, or small RNAs, without a concrete function.
(more details on Functional RNA Elements and Conformational RNA Elements can be found on our Model page)
CRE - Conformational RNA Element
RNA sequence that adopts a particular structure in response to external factors like ligands, temperature, or sRNAs.
Learn More
FRE - Functional RNA Element
RNA sequence that performs a biological function when folded into specific structure, without needing protein catalyst.
Learn More
SECIS Element
Highly conserved RNA hairpin that directs ribosome to bypass UGA stop codon and incorporate selenocysteine.
Part Details
Aptamer
Short RNA/DNA molecule that folds into 3D shape to bind target ligand, changing structure to control gene expression.
Part Details
Linker
Short 6-10 nucleotide RNA hinge crucial for connecting CRE to FRE and enabling reliable switching.
Part Details
This is precisely where our project began. We aimed to combine an FRE, a SCR element known as "SECIS"[5], with a ligand-responsive aptamer.
The linker, a short 6–10 nucleotide RNA hinge, is crucial for connecting a Conformational RNA Element (CRE) to a Functional RNA Element (FRE) ) and enabling reliable switching. Its sequence and length shape how the CRE and FRE fold and interact. With proper design, the linker ensures consistent ON/OFF control (More details on aptamers are available on its Part Composite page)
By fusing There are two possible RNA switch designs: ON-ON and OFF-ON (IMAGES).
- ON-ON configuration: When the CRE is stimulated by the external input, the FRE is active.
- OFF-ON configuration: When the CRE is not stimulated by the external input, the FRE is active.
To simplify the explanation of our Software’s inner workings, we will be using an ON-ON case example. Both types of RNA switches (ON-ON/OFF-ON) follow the same basic design principles.
Our Approach
The design of such a system is far from trivial. It requires precise engineering of RNA structures and linkers to ensure the switch functions correctly and to prevent off-target effects. The vast design space of possible sequences and structures makes manual or simple trial-and-error approaches inefficient and time-consuming.
This is why TADPOLE was developed: To aid in the wet lab project design.
It automates the design process focused on achieving two distinct, stable states: ON and OFF:
- Design Proposal Generation: TADPOLE's algorithms (which you will learn about in our Software Technicalities page) systematically generate a large number of RNA sequence proposals.
- Structural and Thermodynamic Prediction: Powered by ViennaRNA[1], for each sequence, the secondary structures and minimum free energies (MFEs) are predicted for both the ON and OFF states.
- Design Evaluation: The software evaluates each proposal against a set of key design principles, such as the required energy difference between the ON and OFF states (ΔMFE) and the structural stability of each conformation (see more information on the Model page).
- Linker Optimisation: A key part of the process is designing the optimal linker sequence between the FRE and the CRE. TADPOLE's algorithms ensure this linker facilitates the correct folding of both the ON and OFF conformations.
- Output Analysis: Finally, it provides you with a set of optimal designs that meet all of the design criteria, ready for experimental testing.
TADPOLE Comes into Play
The revolutionary tool that democratizes RNA switch design
Tadpole: Small Beginning, Endless Possibilities
While our work in the lab focused on the SECIS+Aptamer model, we quickly realised that our approach could be generalised to any FRE. This insight pushed us to create TADPOLE as a tool to share with the synthetic biology community.
However, the true value of our tool became clear when we recognised that not only could aptamers be used as CREs, but so could many other elements, such as mini-RNAs. This inspiration came directly from exploring past iGEM projects: the UParis-BME 2021 team worked with mini-RNAs, designing a hairpin structure that would unfold to reveal a ribosome binding site (RBS). With Tadpole, we can take it to the next level: enabling a generalised design framework. Any conformational element, whether a hairpin like UParis-BME's, an aptamer, or even temperature-sensitive motifs, can be paired with functional RNA elements to create versatile, customizable switches. In this way, Tadpole doesn't just substitute or improve past efforts: it can also amplify their possibilities, turning isolated designs into part of a broad, systematic toolkit.
And this is where the true impact lies: Tadpole goes beyond saving time or simplifying workflows; it democratizes access to an entirely new design space. Not just skipping the limits of transcriptional control, but going beyond traditional translation regulation, by making the design of translational switches systematic, intuitive, and scalable.
Through an easy-to-use interface, an open-source package, and standards-based outputs, Tadpole ensures that the potential of RNA-level regulation becomes a shared resource rather than a niche tool. This way, the innovation can accelerate at the pace of community creativity, empowering every iGEM team and every lab to help build the next generation of biological control.
The impact of TADPOLE extends far beyond our laboratory. By democratizing access to sophisticated RNA switch design, we enable:
- Accelerated Research: Researchers can focus on innovation rather than tedious design iterations
- Educational Access: Students and new researchers can engage with advanced concepts through intuitive tools
- Community Building: Shared standards and open-source availability foster collaboration
- Therapeutic Applications: Faster development of RNA-based therapeutics and biosensors
- Industrial Innovation: Enhanced biomanufacturing through precise translational control
Exceeding Standards
A comprehensive ecosystem designed to make advanced synthetic biology accessible to all users
TADPOLE is more than just a computational tool: it is a complete ecosystem designed to make advanced synthetic biology
accessible to all users. Its multifaceted design ensures that biologists without programming experience, bioinformaticians,
and developers can use it efficiently. More than just writing code, we built a comprehensive platform for RNA switch design.
TADPOLE Accessibility Ecosystem
🌐
Interactive Web Interface
Streamlit-based GUI for all users
- ✓ No programming required
- ✓ Real-time visualization
- ✓ Online & local deployment
🐍
Python Toolkit
Modular library & CLI for developers
- ✓ Easy pip installation
- ✓ Modular components
- ✓ Command-line interface
⚡
REST API 💡
Programmatic access for integration
- ✓ JSON endpoints
- ✓ Automation support
- ✓ Pipeline integration
🐳
Docker Container
Reproducible deployment solution
- ✓ One-command setup
- ✓ All dependencies included
- ✓ Cross-platform compatibility
🔬
Validation & Quality
Rigorous testing & verification
- ✓ In silico validation
- ✓ Wet lab verification
- ✓ Continuous testing
🔗
Standards & Interoperability
SBOL3 export & open standards
- ✓ SBOL3 format support
- ✓ ViennaRNA[1] & Biopython[2]
- ✓ MIT open-source license
🌱
Community & Sustainability
Long-term development & support
- ✓ Open development
- ✓ Educational resources
- ✓ Regular updates
Interactive Web Interface (User Friendly)
TADPOLE offers an interactive web interface that serves as the most accessible entry point for users of all skill levels. Built with Streamlit, it transforms complex bioinformatics workflows into a simple, guided process without requiring programming knowledge.
Design and Usability
The Streamlit interface is intuitive and structured, with tabs and side menus (st.sidebar) that let users navigate through the main functionalities: from core design tools to a detailed help section. Elements like buttons, radio selectors (st.radio), and text input fields (st.text_input) collect essential user data, including RNA sequences and search parameters.
Real-Time Visualization
One of the interface's most notable features is real-time visualization of results. As the analysis runs, Streamlit displays progress and outputs directly on the web page, including:
- Search Results: Tables with candidate sequences, structures, and energies (See report below 💡)
- Structure Visualizations: Images of RNA structures in both ON and OFF states
ON state
OFF state
Linker
Aptamer (CRE)
SECIS (FRE)
- Clustering Metrics: Organized displays of clustering results, enabling exploration of design families (See report below 💡)
Even thought there are other formats to download the results, the report returns the clustering metrics and the tables:
Barrier-Free Accessibility
The TADPOLE interface makes the software accessible in two main ways:
- Online Access: Deployed on a public web page (via Render), allowing any user to run the tool from a browser without installing additional software or configuring a local environment. Ideal for biologists or students unfamiliar with command-line tools.
- Local Use: Advanced users or developers can run the interface on their own machine. The README.md provides step-by-step instructions, simplified further by Docker, ensuring all dependencies are automatically managed and the environment is fully reproducible.
The Streamlit interface is more than just a component; it's the key piece that democratizes access to advanced synthetic biology, allowing a wide range of users to design RNA switches efficiently.
REST API (Advanced)
TADPOLE features a robust RESTful API built with the Flask micro-framework. This API transforms the software into a programmable tool, allowing other services and scripts to interact directly with its core RNA design functions. This architecture is essential for automation, integration into broader computational workflows, and the development of custom applications.
1. Architecture and Endpoints
The API follows a modular design, with each main functionality exposed through a dedicated endpoint:
- /predict_secondary_structure: This endpoint accepts an RNA sequence via a POST request and uses TADPOLE's engine to predict its secondary structure and minimum free energy (MFE). It is ideal for rapid sequence analysis.
- /run_ga_search: This is the primary endpoint for design searches. It accepts a JSON object containing all necessary parameters for the genetic algorithm, including RNA sequences, linker length, mutable positions, and search criteria.
Both endpoints communicate through JSON objects, ensuring seamless interoperability with any programming language.
2. Functionality and Practical Use
The API transforms TADPOLE from a desktop application into a web service, providing key usability benefits:
- Automation: Users can automate repetitive design tasks. For example, a script can iterate over a list of aptamers, sending requests to the API to find the optimal linker for each one without manual intervention.
- Integration: The API enables TADPOLE to be embedded in bioinformatics pipelines. Laboratories or companies can incorporate TADPOLE's design logic into their workflows, alongside sequencing and visualization tools. The test_api.py file demonstrates how to interact with the API using Python's requests library, facilitating adoption by developers.
- Decoupling: The API separates business logic (design algorithms) from the user interface (Streamlit frontend). This allows maintainable and extensible development, as new interfaces—mobile or desktop—can be created without altering the core code.
TADPOLE's API is a foundational layer that reinforces accessibility and integration, making its powerful algorithms usable beyond the web interface on one hand, and the local execution on the other.
Containerized Environment (Docker, Advanced)
TADPOLE's use of Docker represents a robust solution to common installation and reproducibility challenges in bioinformatics.
Docker packages the entire application environment, ensuring the software runs identically on any system. Instructions on how to set up the Docker container can be found on the READ ME file in Gitlab.
What is a Docker Container?
A Docker container is like a self-contained box holding everything needed for an application to run: the code, libraries, system dependencies, and configurations. Unlike a virtual machine, which emulates a full operating system, a container is lightweight and efficient. This eliminates the frustration of a program working on one machine but not another.
Implementation and Key Advantages
TADPOLE's Docker setup is defined in a Dockerfile, which acts as a recipe for building the container image. Key steps include:
- System Dependencies Installation: TADPOLE relies on complex packages such as ViennaRNA[1] for structure prediction and Ghostscript for visualization. The Dockerfile installs these dependencies automatically, avoiding manual, error-prone setups.
- Environment Configuration: The file sets up the Conda and Python environment, installing all required libraries from requirements.txt.
- Code Copying: The application code is copied into the container, making it fully self-contained.
- Port Exposure: The Dockerfile exposes port 8501, the default port used by Streamlit, so the web interface is accessible outside the container.
- Execution Command: A default command starts the Streamlit application, enabling automatic container execution with a simple docker run command.
User Instructions
For users, the process is drastically simplified: instead of manually installing and configuring multiple dependencies, all they have to do is follow the simple instructions on the README.
Docker ensures TADPOLE is portable and reproducible, eliminating compatibility issues and simplifying the installation experience for anyone wishing to run the tool locally.
Technical Advantages
- One-Command Setup: Install and run TADPOLE with a single Docker command
- Complete Environment: All dependencies including ViennaRNA[1], Python libraries, and system requirements pre-configured
- Cross-Platform: Runs identically on Windows, macOS, and Linux systems
- Version Control: Tagged releases ensure consistent results across different environments
- Lightweight Design: Optimized container size for fast deployment
- Security: Non-root user execution and minimal attack surface
- Scalability: Easy horizontal scaling for high-throughput applications
Deployment Options
Supports various deployment scenarios: local development, cloud deployment (AWS, GCP, Azure),
Kubernetes orchestration, and HPC cluster integration. Includes both web interface and
command-line variants for different use cases.
Validation and Quality Assurance
TADPOLE has undergone extensive validation to ensure reliability, accuracy, and real-world applicability
across diverse RNA switch design scenarios.
Computational Validation
- Thermodynamic Accuracy: VienaRNA used, a widely known project based on Thermodynamics
- Benchmark Testing: Comparison with existing tools and manual designs from literature
- Performance Metrics: Evaluation of 'unique' designs by analysing the different output structures
Experimental Validation
- Wet Lab Testing: Experimental verification of designed switches in living cells
Quality Assurance
- Documentation: Comprehensive documentation with examples and troubleshooting guides
- User Feedback: Active incorporation of user feedback and bug reports
Interoperability and Standards
TADPOLE is designed as a highly interoperable package, enabling seamless integration into the broader synthetic biology ecosystem. This is achieved through adherence to standards and reliance on well-established libraries.
SBOL3 (Synthetic Biology Open Language)
TADPOLE can export RNA switch designs in SBOL3 format, a key standard for describing biological components and designs.
- Standardized Description: An SBOL3 (.jsonld) file contains a complete and standardized description of the design, including the sequence, its biological role, and important metadata such as folding energies and performed mutations.
- Exchange and Reuse: By adhering to this standard, TADPOLE designs can be easily shared and imported into other design tools or synthetic biology databases. This promotes collaboration and accelerates innovation in the field.
Restriction Enzymes Checked
In our sequence generation code, we also check for the presence of restriction sites for commonly used type IIS enzymes. These enzymes are important in iGEM projects because they allow precise DNA assembly using techniques like Golden Gate cloning.
The type IIS restriction enzymes considered in our workflow are:
- BbsI → GAAGAC, GTCTTC
- BsaI → GGTCTC, GAGACC
- BsmB → CGTCTC, GAGACG
- BspQI → GCTCTTC, GAAGAGC
- BtgZI → GCGATG, CATCGC
- Esp3I → CGTCTC, GAGACG
- PaqCI → CACCTGC, GCAGGTG
- SapI→ GCTCTTC, GAAGAGC
By checking for these restriction sites in the generated sequences, we ensure that the sequences can be safely used in cloning experiments without introducing unwanted cutting sites. This step helps prevent errors during assembly and is a standard practice in synthetic biology workflows.
Use of Recognized External Packages and Format Conversion
TADPOLE builds on widely-used, well-documented libraries, ensuring reliability and smooth integration.
- Reliability and Consistency: The software uses libraries like ViennaRNA[1] for RNA structure prediction and Biopython[2] for sequence handling and alignments. Leveraging these recognized tools guarantees that TADPOLE results are consistent with community bioinformatics standards.
- Seamless Integration: As a Python-based package, TADPOLE integrates seamlessly with the rich ecosystem of Python libraries. This simplifies data conversions, whether to text strings, FASTA files, or other synthetic biology standards like SBOL.
- Flexibility for Data Handling: Users can work with their data in the format that best suits their workflow, whether combining TADPOLE functionality with other Python libraries or converting outputs for downstream analysis.
Open Source Licensing
TADPOLE is released under the MIT License, an OSI (Open Source Initiative)-approved open-source license (https://opensource.org/licenses/MIT). This ensures that the software is freely available for use, modification, and distribution, supporting collaboration and further development within the synthetic biology community.
TADPOLE's design prioritizes interoperability, standardization, and integration, allowing its powerful RNA design algorithms to fit effortlessly into a variety of computational pipelines and collaborative workflows.
Additional Standard Formats
- Sequence Formats: Import/export in FASTA, GenBank, and other standard bioinformatics formats
- Structural Data: Vienna dot-bracket notation and CT format for RNA secondary structures
- Metadata Standards: Rich annotation support for design provenance and experimental conditions
- Laboratory Tools: Export formats compatible with DNA synthesis and cloning software
- Analysis Pipelines: Integration with popular bioinformatics workflow managers
Impact
Concrete examples of how TADPOLE transforms RNA-based regulation
We have consistently said that TADPOLE represents the next step in the evolution of RNA-based regulation. Now it is time to illustrate this with concrete examples.
Fortunately, the iGEM community has left us a rich legacy of projects, each one exploring unique ideas and approaches. These past achievements are more than inspiration—they form a living foundation that we can draw from. Some of these projects point toward strategies that TADPOLE can enhance or reimagine, others provide frameworks upon which we can build more sophisticated circuits, and still others simply spark ideas that take us in entirely new directions. The sheer diversity of these contributions highlights the creativity of the community and the endless possibilities for future innovation.
TADPOLE Impact Categories
💀
Replacing Kill Switches
From slow protein cascades to instant RNA responses
- ✓ NYCU-Taipei 2021 improvement
- ✓ Kyoto 2023 enhancement
- ✓ Faster, more precise control
🔬
Improving Riboswitches
Adding amplification and multifunctionality
- ✓ PennState 2024 biosensor
- ✓ Signal amplification
- ✓ Enhanced sensitivity
🔄
Building on Toehold Switches
From simple detection to complex circuits
- ✓ UParis-BME 2021 foundation
- ✓ Multi-functional RNA circuits
- ✓ Cascading logic systems
💀 Replacing Protein-Based Kill Switches
Kill switches are essential safety mechanisms in synthetic biology, designed to eliminate engineered organisms when they are no longer needed or if they escape containment. However, traditional protein-based kill switches suffer from significant limitations that TADPOLE can address through RNA-level control.
A. NYCU-Taipei Kill Switch (iGEM 2021)
🔬 Original Project Overview
The NYCU-Taipei team developed a kill switch system based on the MazE/MazF toxin-antitoxin pair. In their design, the antitoxin MazE is constitutively produced to neutralize the toxin MazF. When an external signal (like arabinose) is absent, MazE production stops, allowing MazF to kill the cell by cleaving mRNA molecules.
❌ Fundamental Problems
- Multi-step cascade delays: Signal absence → transcription stop → MazE protein degradation → MazF activation → mRNA cleavage
- Protein half-life issues: MazE proteins persist after transcription stops, delaying kill switch activation
- Energy waste: Continuous protein production required for maintenance
- Escape potential: Cells can survive and potentially evolve resistance during the delay period
- Leaky expression: Basal transcription can provide enough MazE to prevent killing
✅ TADPOLE Revolutionary Approach
- Direct RNA control: Aptamer directly senses signal presence/absence
- Immediate response: No protein degradation waiting period
- Energy efficient: No continuous protein production needed
- Escape-proof: Instant activation prevents adaptation
- Digital switching: Clear ON/OFF states with minimal leakiness
TADPOLE Proposal: Kill Switch Design
1️⃣
Signal Detection
Aptamer directly binds survival signal (e.g., arabinose)
2️⃣
Conformational Switch
Signal absence triggers ribozyme activation
3️⃣
Immediate Cleavage
Active ribozyme cleaves essential mRNAs
4️⃣
Rapid Cell Death
Cell death within minutes, not hours
Specific TADPOLE Design for NYCU-Taipei Improvement
Components:
- CRE: Arabinose-binding aptamer (maintains same trigger as original)
- FRE: Ribozyme targeting essential mRNAs (e.g., those encoding ribosomal proteins)
- Logic: Arabinose present → ribozyme OFF → cell survives; Arabinose absent → ribozyme ON → cell dies
Advantages over original:
- Response time: Hours → Minutes
- Energy cost: High (continuous protein production) → Low (RNA-only)
- Reliability: Variable (protein degradation dependent) → Consistent (direct RNA control)
B. iGEM Kyoto Kill Switch (iGEM 2023)
🔬 Original Project Overview
The Kyoto team designed a population control system using quorum sensing. Their kill switch activates when cell density reaches a threshold, detected through accumulation of signaling molecules. The system uses protein-based detection and response cascades to trigger cell death, preventing overgrowth.
❌ Critical Limitations
- Complex cascade delays: Quorum molecule accumulation → receptor binding → transcriptional activation → lytic protein production
- Threshold imprecision: Protein-based detection creates noisy, gradual responses
- Resource burden: Multiple proteins required for sensing and killing
- Population overshoot: Delays allow population to exceed target before control activates
- Evolutionary pressure: Long response times select for resistant mutants
✅ TADPOLE Precision Control
- Direct molecular detection: Aptamer binds quorum molecules immediately
- Sharp threshold response: Digital ON/OFF switching at precise concentrations
- Minimal resource use: Single RNA molecule performs sensing and killing
- Precise population control: Immediate response prevents overshoot
- Evolution-resistant: Fast killing prevents selection pressure
TADPOLE Proposal: Quorum Sensing Kill Switch
🎯 Precise Detection
Aptamer designed to bind specific quorum sensing molecules (AHL, AI-2, etc.) with high affinity and specificity, providing sharp concentration thresholds.
⚡ Instant Response
When quorum molecule concentration exceeds threshold, aptamer binding immediately activates ribozyme, bypassing all protein synthesis delays.
🎛️ Tunable Control
Aptamer affinity can be engineered to set precise population density thresholds, enabling fine-tuned population control.
Specific TADPOLE Design for Kyoto Enhancement
Components:
- CRE: AHL-binding aptamer (or other quorum molecule-specific aptamer)
- FRE: Ribozyme targeting mRNAs encoding essential proteins
- Logic: Low density (low AHL) → ribozyme OFF → growth continues; High density (high AHL) → ribozyme ON → population reduction
Performance improvements:
TADPOLE transforms kill switches from slow, unreliable safety mechanisms into precise, immediate control systems:
- Speed Revolution: Hours → Minutes response times eliminate escape windows
- Precision Enhancement: Digital switching replaces gradual, noisy responses
- Efficiency Gains: RNA-only systems reduce metabolic burden
- Reliability Improvement: Direct molecular control eliminates cascade failures
- Safety Advancement: Faster, more reliable containment for engineered organisms
This represents a paradigm shift: from kill switches as backup safety measures to kill switches as precise, real-time control systems that can be trusted for critical applications.
🔬 Improving Traditional Riboswitches
Riboswitches are naturally occurring RNA structures that bind small molecules and regulate gene expression. While powerful, traditional riboswitches are limited to simple ON/OFF control of translation. TADPOLE transforms these basic switches into sophisticated, amplified detection systems.
The Original Project: PennState (iGEM 2024)
🔬 Original Project Overview
The PennState team developed a biosensor system to detect CA125, a glycoprotein biomarker associated with ovarian cancer. Their approach used a traditional riboswitch design where CA125 binding to an aptamer domain causes a conformational change that exposes a ribosome binding site (RBS), allowing translation of a fluorescent reporter protein. The system aimed to provide early detection of ovarian cancer through a simple, cost-effective biosensor.
❌ Fundamental Limitations
- Linear signal relationship: Output directly proportional to biomarker concentration - no amplification
- Low sensitivity threshold: Requires high biomarker concentrations for detectable signal
- Single function constraint: Can only control translation ON/OFF, limiting versatility
- Slow protein-dependent readout: Relies on protein synthesis and folding for signal generation
- Background noise issues: Leaky translation creates false positives
- Limited dynamic range: Narrow window between background and saturation
✅ TADPOLE Revolutionary Enhancements
- Exponential signal amplification: One binding event triggers multiple catalytic cycles
- Ultra-high sensitivity: Detection at picomolar concentrations
- Multi-functional capability: By fusing them with FRE, we can enable Control splicing, cleavage, activation cascades
- Rapid RNA-based readout: Immediate signal without protein synthesis delays
- Digital switching: Sharp ON/OFF transitions minimize noise
- Tunable dynamic range: Adjustable sensitivity and saturation points
TADPOLE Proposal: Amplified Riboswitch Design
🎯 Signal Amplification Mechanism
CA125 binding to aptamer triggers conformational change that activates a ribozyme. Each active ribozyme can cleave hundreds of substrate RNAs, creating exponential signal amplification from a single binding event.
📈 Sensitivity Enhancement
Amplification has the potential to enable detection at concentrations 100-1000x lower than traditional riboswitches. Early-stage cancer detection becomes feasible with minimal sample volumes.
🔧 Functional Versatility
Beyond simple reporter activation, TADPOLE riboswitches can control RNA splicing, activate therapeutic RNAs, or trigger complex regulatory cascades for sophisticated biosensor networks.
Specific TADPOLE Design for CA125 Detection
System Architecture:
- CRE Component: CA125-binding aptamer (maintains original specificity)
- FRE Component: Hammerhead ribozyme targeting reporter mRNA
- Linker Design: 8-nucleotide hinge optimized for conformational switching
- Reporter System: Multiple copies of cleavable reporter RNA for amplification
Operational Logic:
- OFF State (No CA125): Ribozyme sequestered by aptamer → No cleavage → High reporter signal
- ON State (CA125 present): Aptamer binding → Ribozyme release → Reporter cleavage → Signal reduction
Performance Improvements:
- Sensitivity: Low → High
- Response Time: Hours → Minutes
🔄 Building on Toehold Switches: UParis-BME (iGEM 2021)
Toehold switches represent one of the most successful RNA-based regulatory tools in synthetic biology. However, they are typically limited to simple translation control. TADPOLE transforms these foundational tools into sophisticated, multi-functional RNA circuits capable of complex logic and amplified responses.
Original Project Foundation: SwitchMi Designer
🔬 Original Project Overview
The UParis-BME team developed SwitchMi Designer, a computational tool for designing toehold switches that detect specific microRNAs. Their system uses RNA hairpin structures that sequester ribosome binding sites (RBS). When the target microRNA binds to the toehold region, it unfolds the hairpin, exposing the RBS and enabling translation of a reporter gene. This work established a reliable framework for microRNA detection with high specificity and low cross-reactivity.
❌ Original System Constraints
- Single function limitation: Only controls translation ON/OFF
- No signal amplification: One microRNA binding = one protein produced
- Protein-dependent readout: Requires time for protein synthesis and maturation
- Limited logic capability: Simple binary response without complex processing
- No cascading potential: Cannot trigger downstream RNA events
- Static design: Fixed response without tunable parameters
✅ TADPOLE Transformative Enhancements
- Multi-functional capability: By fusing them with FRE, we can control ribozymes, splicing, RNA processing
- Exponential amplification: One binding triggers hundreds of events
- Immediate RNA readout: Direct RNA-level responses without protein delays
- Complex logic processing: AND/OR gates
- Cascading architecture: Multi-step RNA circuit activation
- Tunable parameters: Adjustable sensitivity and response kinetics
TADPOLE Prposal: Enhancement Strategy
Building on SwitchMi Designer's foundation to create next-generation RNA circuits
🔧 Foundation Integration
Use the iGem team's tool SwitchMi Designer to search for validated toehold sequences as starting points, ensuring reliable microRNA recognition while adding functional complexity.
⚡ Functional Expansion
Replace simple translation control with sophisticated RNA functions: ribozymes for cleavage, self-splicing introns for processing, aptamers for additional inputs.
🔗 Circuit Architecture
Create modular RNA circuits where toehold activation triggers cascades of RNA events, enabling complex information processing and amplified responses.
Step-by-Step TADPOLE Enhancement Process:
Step 1: Foundation - SwitchMi Designer Integration
Objective: Leverage existing validated toehold designs
- Input target microRNA sequence into SwitchMi Designer
- Obtain thermodynamically validated toehold sequence with optimal binding kinetics
- Verify specificity against off-target microRNAs
- Use this sequence as the CRE component in TADPOLE design
Advantage: Builds on proven technology while maintaining reliability
Step 2: Functional Enhancement - FRE Integration
Objective: Replace simple translation control with sophisticated RNA functions
- Select appropriate FRE based on desired function:
- • Ribozyme: For signal amplification through RNA cleavage
- • Self-splicing intron: For conditional RNA processing
- • SECIS element: For Stop Codon Readthrough[3] control
- Design optimal linker using TADPOLE algorithms
- Ensure proper folding in both ON and OFF states
Advantage: Transforms binary switch into multi-functional processor
Step 3: Circuit Architecture - Multi-Level Control
Objective: Create sophisticated RNA circuits with complex logic
OFF State Architecture:
- Toehold hairpin sequesters FRE
- FRE remains inactive
- Downstream targets unaffected
- Circuit in standby mode
ON State Architecture:
- microRNA binding unfolds hairpin
- FRE becomes active and functional
- Triggers cascade of RNA events
- Amplified, multi-functional response
Advanced Functions Enabled:
- Signal Amplification: Ribozyme cleaves multiple substrate RNAs
- Conditional Processing: Self-splicing introns enable/disable gene expression
- Logic Gates: Multiple toehold inputs create AND/OR logic
- Cascading Circuits: Output of one switch becomes input for another
Step 4: Advanced Applications - Next-Generation RNA Circuits
Objective: Implement sophisticated biological computing
Diagnostic Networks
- Multiple microRNA detection
- Disease signature recognition
- Quantitative readouts
Therapeutic Circuits
- Conditional drug activation
- Cell-type specific responses
- Feedback-controlled therapy
Biosafety Systems
- Multi-input kill switches
- Environmental monitoring
- Containment circuits
Concrete Example: Enhanced microRNA Cancer Detection
Original UParis-BME Approach:
- miR-21 (cancer-associated microRNA) → Toehold unfolds → GFP translation → Fluorescent signal
- Simple binary readout: Cancer present/absent
TADPOLE Enhancement:
- Multi-input Detection: Toeholds for miR-21, miR-155, miR-210 (cancer signature)
- Logic Processing: AND gate requires 2+ microRNAs for activation
- Amplified Response: Activation triggers ribozyme that cleaves 100+ reporter RNAs
- Quantitative Output: Cleavage rate proportional to cancer progression
- Therapeutic Activation: Same circuit can activate anti-cancer siRNA
Revolutionary Impact: From Simple Switches to RNA Computers
Paradigm Transformation:
- Computational Complexity: Simple binary switches → Multi-input logic processors
- Response Amplification: Linear responses → Exponential amplification
- Functional Scope: Detection only → Detection + processing + action
- Circuit Integration: Isolated switches → Networked RNA computers
- Therapeutic Potential: Diagnostic tools → Theranostic systems
Community Impact: TADPOLE doesn't replace SwitchMi Designer—it elevates it. Teams can use their proven toehold designs as building blocks for sophisticated RNA circuits, accelerating innovation while maintaining reliability. This represents the evolution from RNA switches to RNA computers, opening entirely new possibilities for synthetic biology applications.
Experimental Validation
In
The Need for TADPOLE
section, 'Our Approach' subsection, we explained the expectations we had when building the software,
and how exactly we expected it to help us design our systems: with
semi-rational design.
And the results reflect this approach perfectly:
-
We finally did not need to rely on experimental testing as
all 4 different systems we tested in the lab correctly worked as switches.
This shows that our considerations about energy and nucleotide complementarity were good enough to design functional systems.
It would be virtually impossible to obtain 4 working switches by chance from just 4 random sequences.
-
At the same time, the structural predictions were not completely accurate.
While the software suggested an ON–ON system, the lab results showed an OFF–ON system
(see details in the Model or Engineering page).
This outcome validates the value of a semi-rational approach: even with imperfect structural
predictions, our tool could reduce the design space enough to reliably generate functional switches.
What happened exactly?
The results in the lab show that:
- Without theophylline: we have Readthrough
- With theophylline: less Readthrough
Important consideration
Despite the fact we have been dealing in absolutes regarding FRE activity ("Functional / Non-functional"), it is important to remember that in the lab these systems work in relatives.
Elements like STOP codon readthrough sequences, or frameshift structures, are able to trigger their function to a certain level, but they never reach 100%.
When isolated, SECIS performed in a cell-free assay with a certain percentatge of readthrough induction (see more on Results).
What we predicted as the OFF state shows less Readthrough than with the isolated SECIS (see more on Results),
therefore our prediction of the OFF state was correct: there is indeed a reduction on the level of Readthrough.
However, what should have been the ON state, shows less Readthrough: about half (see more on Results).
This means that, instead of recovering its functional structure, when theophylline is added,
the structural change disrupts the FRE even more. This deviation with predictions is most likely
due to the limitations we already explained about RNAFold, more precisely, the fact that it is not
able to predict the non-canonical base pairings located at the heart of the functional core of SECIS, the most important part of the element.
So what is working?
We wanted the model to compute two states in which the addition of theophylline would provoke a structural change.
→
There is a structural change in our system when theophylline is added that modulates functionality
→
✔️ Checked
We also needed these two states with energies close enough so that it would allow us to pivot between the two conformations.
→
As the functionality is modulated when theophylline is added, there has to be a
conformational change
(we eliminated other possibilities) and therefore we are able to pivot.
→
✔️ Checked
What is not working?
As expected, the structural prediction of the state with theophylline is not accurate, which is explained by RNAFold limitations.
We were able to build two conformations that would result in a switch (we were able to design a switch)
but we were not able to predict the exact behaviour of our system
due to the current limitations on structural prediction.
This limitations comply with the definition af a semi-rational designing tool.
Therefore, what can other people expect from TADPOLE?
We were able to make a semi-rational design that worked properly
for the system we tested in the lab,
one of the most difficult systems
(due to the non-canonical pairings) we could have presented.
We proved how it can work even outside of ordinary cases.
Therefore, with FRE elements without these uncommon structures,
our tool should predict even more accurate results and.
Others can use this tool as a functional semi-rational design tool.
References
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- Berry, M. J., Banu, L., Harney, J. W., & Larsen, P. R. (1993). Functional characterization of the eukaryotic SECIS elements that direct selenocysteine insertion at UGA codons. EMBO Journal, 12(8), 3315–3322. https://pubmed.ncbi.nlm.nih.gov/8344267/
- Mathews, D. H., Sabina, J., Zuker, M., & Turner, D. H. (1999). Expanded sequence dependence of thermodynamic parameters improves prediction of RNA secondary structure. Journal of Molecular Biology, 288(5), 911–940. https://doi.org/10.1006/jmbi.1999.2700
- Holland, J. H. (1992). Adaptation in Natural and Artificial Systems. MIT Press.