De Novo DNA

De Novo DNA

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Private Company

Funding information not available

Overview

De Novo DNA is a private, early-revenue company founded in 2018, offering a sophisticated computational platform for the rational design of genetic systems. Its core technology consists of predictive calculators (e.g., Promoter Calculator, RBS Calculator) that allow users to model and engineer biological parts for precise control of gene expression, serving a large academic and industrial research community. The company leverages its platform to provide both software-as-a-service and custom protein library construction, positioning itself at the intersection of bioinformatics, genetic engineering, and automation in the rapidly growing synthetic biology market.

Genetics & GenomicsSynthetic Biology

Technology Platform

A web-based suite of predictive biophysical models (Promoter Calculator, RBS Calculator, RBS Library Calculator, Operon Calculator) for designing and optimizing genetic systems to control transcription, translation, and protein expression levels. Complemented by a service for constructing massively parallel protein libraries.

Funding History

1
SeedUndisclosed

Opportunities

The rapid growth of the synthetic biology market and the increasing adoption of automated biofoundries create a strong demand for reliable, predictive design software.
Expanding the platform to support more complex organisms (e.g., yeast, mammalian cells) and integrating with DNA synthesis and sequencing providers could open significant new markets.

Risk Factors

The company faces competition from both large life science tool companies and specialized startups.
Its reliance on the accuracy of its proprietary models poses a technology risk, and scaling the services component requires different operational expertise than software development.

Competitive Landscape

De Novo DNA competes in the biological design software space with companies like Benchling (which offers broader lab data management with some design tools), TeselaGen, and GenoCAD. Its deep focus on predictive biophysical modeling for prokaryotes is a key differentiator, but it may face pressure from platforms incorporating more machine learning or offering broader organism support.