CD Biosynsis pioneers the application of Artificial Intelligence (AI) and Machine Learning (ML) to revolutionize the field of synthetic biology. Our services integrate large-scale experimental data with powerful computational algorithms to move beyond traditional empirical methods. We provide solutions for predictive modeling , design space exploration , and pathway optimization , drastically accelerating the Design-Build-Test-Learn (DBTL) cycle. By leveraging deep learning and specialized ML techniques, we enable our clients to discover optimal genetic parts, predict strain performance, and rationally engineer biological systems with unprecedented speed and precision.
Get a QuoteThe core challenge in synthetic biology is the vastness of the biological design space. Manually testing every potential genetic combination is impossible. AI and Machine Learning overcome this by finding complex, non-linear relationships within large, high-throughput data sets. Our platform learns from past experimental results (the Learn phase) to generate refined, high-probability designs for the next Build phase, making the engineering process iterative, predictive, and much faster. This computational approach allows for the discovery of non-intuitive solutions that are inaccessible through human hypothesis alone.
Strain Performance Modeling
Training models on thousands of genotypic/phenotypic data points to predict product yield, growth rate, or stress tolerance for new, untested genetic variants.
Genetic Part Function Prediction
Using Deep Learning (e.g., CNNs) to predict the strength of synthetic elements like promoters, ribosomal binding sites (RBS), or terminators based on their sequence.
Protein Fitness Prediction
Machine Learning models to predict the activity, stability, or expression level of novel enzyme sequences generated through directed evolution or design.
Generative Design
Utilizing generative adversarial networks (GANs) and variational autoencoders (VAEs) to propose entirely new, functionally superior genetic circuits or protein sequences.
Bayesian Optimization (BO)
Applying BO to intelligently select the next set of experimental conditions (e.g., media composition, induction timing) to maximally improve the desired output.
Pathway Flux Optimization
ML models integrated with Flux Balance Analysis (FBA) to pinpoint rate-limiting enzymes and suggest optimal enzyme loading or expression levels.
Multi-Omics Fusion
ML models to integrate and interpret disparate data types (Genomics, Transcriptomics, Metabolomics) to build a holistic understanding of cellular behavior.
Regulatory Network Inference
Applying network analysis algorithms to transcriptomic data to map out and understand complex gene regulatory interactions.
Data Curation and Standardization
Cleaning, standardizing, and structuring raw experimental data (often from HTS) to ensure its quality for reliable model training.
Deep Learning
Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for sequence-to-function predictions (DNA, RNA, Protein).
Gaussian Processes & BO
Probabilistic models (Gaussian Processes) coupled with Bayesian Optimization for efficient and reliable experimental design under uncertainty.
Reinforcement Learning (RL)
RL algorithms used to train 'agents' that can autonomously navigate the optimization landscape of a metabolic pathway or bioreactor condition.
De Novo Biosynthesis Pathway Design
AI models propose novel, synthetic metabolic routes for the production of high-value compounds that do not exist naturally.
Enzyme Directed Evolution Guidance
ML predicts the optimal amino acid mutations that will maximize enzyme activity or selectivity, dramatically reducing library size.
Bioreactor Process Optimization
AI models analyze fermentation data in real-time to adjust parameters (e.g., feed rate, DO, pH) for maximized productivity during scale-up.
A closed-loop system for continuous optimization.
Data Collection & Curation
Model Training & Validation
Prediction & Rational Design
Experimental Build & Test
Gather Data: Collect high-quality, quantitative data from HTS and omics characterization.
Standardize: Clean and format data for model ingestion (e.g., one-hot encoding of sequences).
Select Model: Choose the appropriate ML architecture (e.g., Deep Neural Network, Gaussian Process).
Train & Test: Train the model on the historical data and validate its predictive accuracy.
Generate Hypotheses: Use the validated model to predict the outcome of millions of new designs.
Select Next Designs: Utilize optimization algorithms (BO) to select the most informative and promising designs to test next.
Build & Test: Synthesize the AI-recommended designs in the lab and measure performance.
Learn: Feed new experimental data back into the model for continuous improvement.
Non-Intuitive Discovery
ML can identify complex interactions and optimal parameters that human intuition and traditional methods often miss.
Massive Design Space Coverage
Enables the virtual testing of millions of designs, reducing the physical burden of laboratory experiments.
Rapid Convergence to Optimum
Bayesian Optimization reduces the number of experimental cycles needed, rapidly lowering project costs and timelines.
Enhanced Data Utilization
Converts raw HTS and omics data into structured, predictive knowledge that fuels future design efforts.
"The predictive model trained by CD Biosynsis cut our optimization cycles in half. The AI-recommended promoter set was counter-intuitive, but it delivered the highest yield we’ve seen to date."
Dr. Alex Rivera, Lead Computational Biologist, Industrial Biofuel Co.
"Using their Bayesian Optimization service, we quickly navigated a complex media formulation space, finding the perfect nutrient balance that significantly boosted our fermentation titer."
Ms. Naomi Patel, Head of Process Development, Food Ingredients
"The Deep Learning model for sequence-to-function prediction allowed us to filter our enzyme library down to the top 1% virtually, saving months of lab time and material costs."
Dr. Ben Carter, PI, Enzyme Engineering Lab
"We commissioned CD Biosynsis to support an intricate gene editing project with multiple targets. Their talent in producing high-quality work in a short period of time was impressive. Their solutions were custom made to suit our needs, and they went above and beyond to ensure our experiments worked. Their support has been a great asset to our research department and we look forward to further working with them."
Dr. Raj Patel, Principal Investigator, Department of Molecular Biology
"As a pharmaceutical company working to discover new cancer therapies, we require accurate, trustworthy gene editing solutions. CD Biosynsis did more than what we expected when it came to providing strong, accurate CRISPR/Cas9 solutions for our preclinical research. Their technical support team was excellent and responsive, and they quickly replied to our questions. This alliance has been pivotal in helping us move our drug pipeline forward. Thank you, CD Biosynsis, for your amazing service!"
Dr. Clara Rodriguez, Chief Scientist, AstraZeneca Pharmaceuticals, Spain
What kind of data is needed to train an AI model?
High-quality, quantitative data is essential. This typically includes genotype data (DNA/protein sequences), process data (fermentation conditions), and phenotype data (titer, yield, growth rate) collected from High-throughput Screening (HTS).
Which organisms can your AI models be applied to?
Our models are host-agnostic and can be applied to any organism, including E. coli, yeast, fungi, and mammalian cells, provided there is sufficient quantitative data for model training.
How does AI differ from traditional metabolic modeling (e.g., FBA)?
Traditional metabolic modeling (FBA) uses known biochemical rules (stoichiometry). AI/ML learns complex, often unknown, relationships directly from experimental data, making it highly effective for optimizing synthetic pathways and non-linear interactions.
How quickly can you provide a design recommendation using AI?
Once the initial data is curated and the model is trained (which can take days to a few weeks depending on complexity), the model can generate millions of new design predictions instantly, making the Design phase extremely fast.
How much does Metabolic Engineering services cost?
The cost of Metabolic Engineering services depends on the project scope, complexity of the target compound, the host organism chosen, and the required yield optimization. We provide customized quotes after a detailed discussion of your specific research objectives.
Do your engineered strains meet regulatory standards?
We adhere to high quality control standards in all strain construction and optimization processes. While we do not handle final regulatory approval, our detailed documentation and compliance with best laboratory practices ensure your engineered strains are prepared for necessary regulatory filings (e.g., GRAS, FDA).
What to look for when selecting the best gene editing service?
We provide various gene editing services such as CRISPR-sgRNA library generation, stable transformation cell line generation, gene knockout cell line generation, and gene point mutation cell line generation. Users are free to select the type of service that suits their research.
Does gene editing allow customisability?
Yes, we offer very customised gene editing solutions such as AAV vector capsid directed evolution, mRNA vector gene delivery, library creation, promoter evolution and screening, etc.
What is the process for keeping data private and confidential?
We adhere to the data privacy policy completely, and all customer data and experimental data are kept confidential.
CRISPR-Cas9 technology represents a transformative advancement in gene editing techniques. The main function of the system is to precisely cut DNA sequences by combining guide RNA (gRNA) with the Cas9 protein. This technology became a mainstream genome editing tool quickly after its 2012 introduction because of its efficient, simple and low-cost nature.
The CRISPR gene editing system with its Cas9 version stands as a vital instrument for current biological research. CRISPR technology enables gene knockout (KO) through permanent gene expression blockage achieved by sequence disruption. Various scientific domains including disease modeling and drug screening employ this technology to study gene functions. CRISPR KO technology demonstrates high efficiency and precision but requires confirmation and verification post-implementation because unsatisfactory editing may produce off-target effects or incomplete gene knockouts which impact experimental result reliability. For precise and efficient Gene Editing Services - CD Biosynsis, Biosynsis offers comprehensive solutions tailored to your research needs.
The CRISPR-Cas9 knockout cell line was developed using CRISPR/Cas9 gene editing to allow scientists to remove genes accurately for research on gene function and disease models and pharmaceutical discovery. Genetic research considers this technology essential due to its high efficiency together with simple operation and broad usability.
If your question is not addressed through these resources, you can fill out the online form below and we will answer your question as soon as possible.
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CD Biosynsis is a leading customer-focused biotechnology company dedicated to providing high-quality products, comprehensive service packages, and tailored solutions to support and facilitate the applications of synthetic biology in a wide range of areas.