De Novo DNA Design Platform

De Novo DNA is a web-based design software and services platform built for researchers and engineers working to engineer genetic systems in living organisms. The platform provides a suite of computational tools that enable users to predict, control, design, and optimize gene expression, metabolic pathways, and genetic circuits across a wide range of organisms and applications. With over 10,000 registered researchers, more than 850,000 designed genetic systems, and applications spanning 1,200+ use cases, De Novo DNA serves the synthetic biology, metabolic engineering, and bioprospecting communities.

The platform is developed by a team combining expertise in chemical engineering, synthetic biology, molecular biology, biochemistry, genomics, and next-generation sequencing. Its tools are grounded in quantitative, predictive models and have been validated through extensive published research from leading institutions worldwide.

Core Design Tools and Capabilities

• Promoter Calculator: Calculates transcription rates across genetic systems, identifies cryptic promoters and mis-annotated coding sequences, and enables the design of synthetic promoters with desired expression levels.
• RBS Calculator: Predicts and controls translation initiation rates and mRNA decay rates, and supports the design of ribosome binding sites (RBS) tuned to specific protein expression levels.
• RBS Library Calculator: Generates combinatorial libraries of RBS sequences to systematically vary enzyme expression levels, identify rate-limiting steps, and optimize metabolic pathway performance.
• Operon Calculator: Designs and optimizes multi-gene operons to coordinate expression of multiple enzymes, redirect metabolic fluxes, and maximize organism productivity.

Protein Expression Prediction and Control

• Calculate transcription rates, translation rates, and mRNA decay rates across entire genetic systems.
• Detect cryptic promoters and mis-annotated coding sequences that may interfere with intended expression.
• Design synthetic promoters and ribosome binding sites engineered to achieve desired protein expression levels.
• Tune expression of transcription factors, inducible circuits, and regulatory elements with quantitative precision.

Metabolic Pathway Design and Optimization

• Systematically vary and optimize enzyme expression levels across multi-enzyme pathways.
• Identify rate-limiting steps within biosynthetic pathways to guide targeted engineering efforts.
• Redirect metabolic fluxes to maximize production of desired compounds.
• Maximize organism productivities for a wide range of target molecules including biofuels, natural products, and commodity chemicals.

Massively Parallel Protein Library Services

• Design and clone libraries of 10 to 10,000 proteins at approximately $100 per cloned expression plasmid.
• Supports any amino acid sequence up to 900 amino acids in length.
• Applicable to bioprospecting, biologics development, enzyme engineering, and additional research areas.

Published Research Applications

• MIT researchers used the RBS Library Calculator to optimize enzyme expression in the nif gene cluster, maximizing nitrogen fixation rates in Rhizobium sp. IRBG74 and identifying design rules for engineered operons.
• MIT researchers applied the RBS Calculator to optimize a genetic circuit in engineered L. lactis probiotic bacteria capable of detecting and suppressing cholera infection in vivo.
• University of Manchester researchers used the RBS Library Calculator combined with machine learning to boost limonene production in E. coli by over 60% while screening less than 3% of the combinatorial library.
• University of Illinois researchers combined the RBS Library Calculator with Bayesian optimization and automated strain construction to over-produce lycopene in E. coli.
• Washington University researchers applied the RBS Calculator to maximize limonene production in Synechococcus 2973 by identifying optimal expression levels for Limonene Synthase and GPPS.
• MIT researchers used the RBS Calculator to optimize 12 inducible transcription factors, creating a Marionette E. coli strain capable of tunable control of 12 proteins via small molecule inducers.
• Columbia University researchers applied the RBS Calculator to engineer a CRISPR-based genetic circuit that records temporal biological signals as physical genomic modifications, functioning as a biological tape recorder.
• Joint BioEnergy Institute researchers used the RBS Calculator to optimize enzyme expression for over-production of three C5 alcohols while reducing toxic byproducts, achieving 70% of maximum possible production titer.
• MIT researchers applied the RBS Library Calculator to optimize expression of 16 enzymes in the nif gene cluster, identifying design rules to maximize nitrogenase activity.
• MIT researchers used the RBS Calculator to engineer genetic circuits that convert analog chemical signals into discrete Boolean logic states using recombinases.
• Duke University researchers used the RBS Calculator to engineer a lysis-inducing genetic circuit demonstrating that programmed bacterial suicide improves population-level growth rates under stress.
• Harvard University researchers employed the RBS Calculator to design RNA scaffolds that co-position multiple enzymes to accelerate reaction rates, selecting sequences with minimal translation initiation to preserve scaffold integrity.
• Researchers from Universidad Nacional Autónoma de México used the RBS Calculator to control five enzyme expression levels for resveratrol over-production using a co-culture of two engineered E. coli strains.
• University of Wisconsin-Madison researchers applied the RBS Library Calculator to vary five enzymes for isobutanol production, identifying rate-limiting steps and achieving high production titers.
• Joint Bioenergy Institute and Lawrence Berkeley National Lab researchers used the RBS Library Calculator to optimize Crp transcription factor expression for methyl ketone production from mixed glucose-xylose feedstocks.
• Penn State University researchers combined the Operon Calculator and RBS Library Calculator to design a 5-enzyme Entner–Doudoroff pathway, increasing NADPH regeneration by over 25-fold.
• MIT and NIST researchers used the RBS Calculator to tune transcription factor expression for modular genetic circuits with automated Boolean logic design.
• University of Queensland researchers applied the Operon Calculator and RBS Calculator to express the Wood-Werkman cycle in E. coli, achieving a 5-fold increase in propionic acid production.
• University of Colorado Boulder researchers combined the RBS Library Calculator with multiplexed genome recombineering and TRACE sequencing to maximize organism tolerance to hydrolysate and isobutanol.
• MIT researchers used the RBS Calculator to optimize expression of five antimicrobial peptides delivered via phagemids targeting pathogenic bacteria.
• Texas A&M University researchers applied the RBS Calculator to increase limonene production in Synechococcus elongatus by 100-fold through enhanced CO2 and light bioconversion.
• A*STAR Singapore researchers used the RBS Calculator to increase expression of enzymes responsible for viridiflorol production in E. coli.
• MIT researchers applied the RBS Library Calculator to optimize TetR-homolog transcription factors, developing a toolbox of high-performance genetic logic gates for programming cellular behavior.
• National Renewable Energy Laboratory and University of Colorado Boulder researchers used the RBS Library Calculator to optimize a multi-enzyme pathway for styrene over-production in E. coli.
• Jiangnan University researchers applied the RBS Calculator to control hyaluronidase expression in Bacillus subtilis to tune hyaluronan polymer production rate and molecular weight.
• Rice University researchers used the RBS Calculator to control optogenetic circuit components, enabling light-based temporal control of gene expression using two wavelengths.
• University of Michigan researchers used the RBS Calculator to analyze sequence mutations from adaptive laboratory evolution conferring isobutanol tolerance in E. coli