Welcome to Prometheus, the AI model that allows you to generate DNA sequences for any creature you can imagine. Whether it’s a pink panda, an elephant-sized turtle, or a completely new lifeform from your wildest dreams, Prometheus decodes the mystery of biology and synthesizes genetic blueprints with precision.
Prometheus is a state-of-the-art AI-powered tool that bridges the gap between imagination and reality by generating complete or partial DNA sequences for custom-designed lifeforms. By harnessing cutting-edge neural networks, evolutionary biology, and genetic data, Prometheus allows you to:
- Generate entire genomes for real or imaginary animals.
- Modify specific traits like color, size, and behavior.
- Simulate viability to ensure your creations can exist and thrive.
- Customize features by simply describing what you want in natural language.
-
🌐 Natural Language Input: Describe your creature using plain text, and Prometheus will transform it into a genetic blueprint. Example: "Generate the DNA for a pink panda with blue eyes."
-
🧬 Complete DNA Synthesis: Create fully detailed genomes or modify specific genes for existing species to alter features like color, size, or behavior.
-
🔬 Real-Time Gene Editing Simulation: Understand the viability of your creature with simulated biological systems that check for survival and adaptation in real-world conditions.
-
💡 Phenotype-Genotype Mapping: Automatically translate phenotypic traits into the corresponding gene sequences.
-
⚙️ Extensible API: Connect Prometheus to advanced synthetic biology tools, CRISPR simulators, and more.
Clone the repository and install the required dependencies to get started with Prometheus.
git clone https://github.com/kyegomez/Prometheus
cd prometheus
pip install -r requirements.txtYou can use Prometheus either through the command line, in Python scripts, or as an API for your bioengineering needs.
from prometheus import GenomeGenerator
# Initialize Prometheus
generator = GenomeGenerator()
# Input your desired creature
creature_description = "I want a pink panda with glowing fur and blue eyes."
# Generate the DNA sequence
dna_sequence = generator.generate(creature_description)
# Output the result
print("DNA Sequence for Pink Panda:")
print(dna_sequence)This will output the complete DNA sequence for your custom pink panda!
Prometheus leverages a multi-step process to translate your inputs into actionable genetic blueprints:
-
Natural Language Processing (NLP): Your description is processed to extract phenotypic traits such as species, color, size, and more.
-
Phenotype-Genotype Translation: The model maps phenotypic traits to corresponding genetic markers, identifying key genes for features like pigmentation, muscle growth, and behavior.
-
DNA Synthesis: Prometheus then synthesizes or modifies a genetic sequence using known biological data and gene-editing techniques.
-
Viability Simulation: The model runs a simulation to check if the generated genome is biologically viable and provides a report on possible outcomes.
Here are a few examples of what you can create with Prometheus:
- A neon-green lion with enhanced night vision and bioluminescent fur.
- A miniature elephant that is the size of a house cat.
- A blue-scaled dragon with the ability to produce fire through chemical reactions in its glands.
- An elephant-sized turtle that moves with surprising speed.
There are a few existing datasets and resources that are relevant for Phenotype-Genotype Translation—mapping phenotypic traits to corresponding genetic markers. These datasets often come from fields like genomics, agriculture, and medical research, where researchers investigate the relationships between genes and observable traits. Here are some notable examples:
- Description: A catalog of published genome-wide association studies that link specific genetic variants with traits and diseases.
- Data: Provides associations between phenotypic traits (e.g., height, pigmentation) and genetic markers (SNPs).
- Link: GWAS Catalog
- Description: Hosted by the National Institutes of Health (NIH), dbGaP provides access to datasets that integrate genotype and phenotype data, particularly in the context of medical research.
- Data: Covers a range of diseases and traits, providing both genetic data (e.g., SNPs, mutations) and associated phenotypic data (e.g., physical traits, behavior).
- Link: dbGaP
- Description: A large-scale biomedical database containing in-depth genetic and health information from half a million UK participants.
- Data: Includes extensive phenotypic traits (e.g., pigmentation, body structure) and genotype data. Particularly useful for studies on genetic markers linked to phenotypic traits.
- Link: UK Biobank
- Description: This project aimed to create the most detailed catalog of human genetic variation, including SNPs, structural variants, and their linkage to phenotypic traits.
- Data: Provides genomic data from various human populations, with some links to phenotypic information such as pigmentation, ancestry, etc.
- Link: 1000 Genomes Project
- Description: A resource for phenotypic and genotypic data on mouse strains, often used as models for human disease and genetic research.
- Data: Provides a variety of phenotypic traits in mice (e.g., behavior, pigmentation, muscle growth) linked to genetic markers.
- Link: Mouse Phenome Database
- Description: HPO provides a standardized vocabulary of phenotypic abnormalities encountered in human disease and links them to genes and mutations.
- Data: Phenotypic traits connected to genetic markers, often in the context of rare genetic disorders.
- Link: HPO
- Description: This database compiles quantitative trait loci (QTL) data for livestock species, which link genotype to phenotypic traits such as muscle growth, behavior, and more.
- Data: Includes genotypic data and phenotypic traits, focusing on agricultural animals like pigs, cattle, and chickens.
- Link: Animal QTLdb
These datasets can help identify genetic markers responsible for phenotypic traits and behaviors across different species, including humans and animals. For deep learning or machine learning applications, you can often combine data from these resources to create models that predict phenotypes based on genotypic data.
To translate the outputted gene sequences from a model like Prometheus into a real, living organism involves multiple advanced stages of synthetic biology, genetics, and biotechnology. Here's a breakdown of how it would be theoretically possible to take those generated gene expressions and create an actual lifeform:
- Description: Once Prometheus outputs a genetic sequence, the next step is to synthesize that DNA in a laboratory. This is currently possible through DNA synthesis technologies that can chemically build DNA strands. Companies like Twist Bioscience and Integrated DNA Technologies (IDT) specialize in creating synthetic DNA molecules.
- Process: The generated DNA sequence would be converted into synthetic DNA using these platforms. The longer the sequence, the more challenging and expensive it becomes, but advances are making it more feasible to synthesize long genomes.
- Description: Once you have synthetic DNA fragments, they need to be assembled into a full genome. Genome assembly involves stitching together smaller DNA fragments into larger contiguous sequences, mimicking the genome of the intended lifeform.
- Process: This can be done in stages using molecular techniques such as recombination or ligation, or through more advanced techniques like yeast artificial chromosomes (YACs) to store and replicate large genomic sequences.
- Description: After assembling the genome, the DNA must be integrated into a living cell. This can be done by:
- Cellular reprogramming: You might introduce the synthetic genome into a blank or "empty" cell, such as a synthetic cell or a cell with its nucleus removed (enucleated).
- Cloning techniques: Using methods similar to somatic cell nuclear transfer (SCNT), where the nucleus of a cell containing the synthetic genome is inserted into an egg cell from which the original nucleus has been removed.
- Process: Techniques like electroporation or viral vectors could be used to introduce synthetic DNA into the cell.
- Description: For the lifeform to grow, the engineered cell containing the synthetic genome needs to develop into a multicellular organism. This step involves coaxing the cell to divide and differentiate into various tissues and organs.
- Process: If the synthetic genome was introduced into a mammalian egg cell, for example, the cell could potentially be implanted into a surrogate organism (similar to cloning techniques like those used for Dolly the Sheep). The cell would then develop through normal embryonic stages.
- Description: The newly created genome must be able to properly express its genes at the right time and in the right cells for the organism to grow and function. This involves epigenetic regulation, gene editing, and protein synthesis control mechanisms.
- Process: Techniques like CRISPR can be employed to fine-tune the genome's expression, ensuring the right proteins are produced to match the phenotypic traits encoded by Prometheus’ genetic blueprint.
- Description: Before attempting to grow a full organism, the synthetic DNA and the cellular systems need to be checked for viability, i.e., whether they can produce a functioning organism. This is a crucial step, as many synthetic combinations could lead to non-viable organisms due to unforeseen genetic interactions.
- Process: Prometheus or similar AI models would simulate various biological processes in silico (computationally) before actual lab trials. This includes checking for potential issues like developmental defects, improper protein folding, or metabolic problems.
- Description: Once the synthetic genome has been successfully integrated and viability is confirmed, the organism must develop naturally or through assisted bioengineering. This involves ensuring the synthetic lifeform grows from a single cell to a fully functioning organism.
- Process: This may involve embryogenesis (if starting from a fertilized egg) or using advanced tissue engineering and organoid systems to simulate early development. The organism could also be grown in an artificial womb (technology is still in its infancy) or implanted in a surrogate animal.
- Description: Creating new lifeforms raises significant ethical and regulatory questions. Laws and frameworks governing synthetic biology, genetic modification, and bioengineering vary widely by country.
- Process: Any attempt to create novel organisms would require rigorous approval from bioethics boards and regulatory agencies (e.g., FDA, EMA) to ensure safety, both for the lifeform itself and the surrounding ecosystem.
- Advanced Embryo Simulation: Develop simulations that predict how embryos develop into fully-grown organisms.
- Enhanced Ecosystem Integration: Include predictive models for ecosystem impact when introducing new lifeforms.
- Cloud-Based Prometheus API: Allow users to generate creatures through a web interface and retrieve results via API calls.
Prometheus is licensed under the MIT License. Feel free to fork and contribute to the project!
We welcome contributions from the community! Whether it's new features, bug fixes, or improved documentation, we encourage you to make Prometheus even better.
- Fork the repository
- Create your feature branch (
git checkout -b feature/new-feature) - Commit your changes (
git commit -m 'Add new feature') - Push to the branch (
git push origin feature/new-feature) - Open a Pull Request
Connect with fellow bioengineers and enthusiasts in the Prometheus community:
- Text Tokenization
- Genomic embedding input
- Merge layer where we merge the pheno type features and the genomic type features maybe with an linear, and then mlp or something or concat or attention
- Output head that generates the genomes, we need to detokenize billions of pairs at once
- Create a single masssive dataset of all the phenotypes mapped to genomic types and put it on hugginface
- Create the script to train this behemoth on this dataset