Reduce DNA synthesis failures by 30–50% with physics-informed pre-screening, without changing your existing tools.
Φ-mapping provides three core validation mechanisms that identify unstable sequences before fabrication, reducing waste and improving reliability.
Pre-screen sequences before fabrication with a single compliance metric. Sequences are classified as stable, marginal, or unstable based on thermodynamic analysis. Unstable sequences are flagged for redesign before costly synthesis attempts.
Visualize regions within sequences most likely to fail or generate errors during synthesis and operation. Hotspot maps identify structural stress points that correlate with synthesis failures, allowing targeted sequence optimization.
A single metric summarizing overall sequence stability and error risk. Ssym integrates thermodynamic properties across the entire sequence, providing a quantitative measure for comparing designs and predicting synthesis success rates.
Reduce failed synthesis runs and material waste with pre-screening validation. Φ-mapping integrates into existing CAD workflows to flag problematic sequences before fabrication, lowering costs and improving throughput.
Achieve more predictable gate behavior and lower error rates in biological computation. Thermodynamic validation ensures logic gates operate within stable regimes, reducing spurious outputs and improving circuit reliability.
Establish measurable thermodynamic bounds for bio-computation and biological AI systems. Φ-mapping provides quantitative safety metrics that can be incorporated into regulatory frameworks and standards bodies.
Φ-mapping has been validated on large-scale synthesis datasets and pilot programs with DNA foundries. Results demonstrate consistent correlation between thermodynamic scores and synthesis outcomes across diverse sequence types and experimental conditions.
Request detailed validation methodology →Φ-mapping is designed as a modular validation layer that works with your existing DNA design pipeline. No architectural changes required.
RESTful API for on-demand sequence validation. Submit sequences via HTTPS, receive compliance scores and hotspot maps in JSON format. Typical response time: <1 second per sequence. Integrates with Python, R, JavaScript, and command-line workflows.
POST /api/v1/validate{"sequence": "ATCG...", "format": "fasta"}→ {"compliance": 0.87, "class": "stable", "hotspots": [...]}
Self-hosted validation for organizations requiring data sovereignty or air-gapped environments. Docker image includes complete Φ-mapping engine with GPU acceleration support. Runs on Linux, integrates with existing HPC infrastructure.
docker pull phi-mapping/validator:latestdocker run -p 8080:8080 phi-mapping/validator
Native plugins for common DNA design software (Benchling, Geneious, SnapGene). Python SDK for custom workflows and automation. Pre-built connectors for LIMS systems including Benchling, TeselaGen, and Antha.
from phi_mapping import Validatorval = Validator()result = val.validate_sequence(seq)
All integration modes use the same validation engine, ensuring consistent results across deployment scenarios. Organizations can start with cloud API for evaluation and migrate to on-premises deployment for production use.
We offer a structured pilot program for DNA foundries, biotech companies, and research institutions to validate Φ-mapping's impact using your own sequences and synthesis data.
Ideal pilot partners: DNA synthesis companies (Twist, Integrated DNA Technologies, Evonetix, Catalog), biotech R&D labs using DNA computation, government research programs (DARPA, ARPA-H), and standards organizations.
Request Pilot Program DetailsWhether you're evaluating validation solutions, researching DNA computation, or establishing governance frameworks, we're here to help.
General inquiries: contact@phi-mapping.org