Licensable Technologies

ART uses probabilistic modeling techniques to guide metabolic engineering systematically without requiring a full mechanistic understanding of the biological system. Using sampling-based optimization, ART provides a set of recommended strains to be built in the next engineering cycle, alongside probabilistic predictions of their production levels. ART is built around a unique uncertainty quantification approach and has been demonstrated to have high predictive accuracy. Using ART improved tryptophan titer and productivity by up to 74 and 43%, respectively, compared to the best designs used for algorithm training. Learn more about this technology.

Researchers have determined that the combination of machine learning and abundant multiomics data (proteomics and metabolomics) can be used to effectively predict pathway dynamics in an automated fashion. The new method outperforms a classical kinetic model, and produces qualitative and quantitative predictions that can be used to productively guide bioengineering efforts. This method systematically leverages arbitrary amounts of new data to improve predictions, and does not assume any particular interactions, but rather implicitly chooses the most predictive ones. Learn more about this technology.

The Omics Mock Generator (OMG) library is used to provide synthetic multiomics data for testing computational tools for bioengineering metabolic models. Since experimental multiomics data is expensive to produce, OMG provides a simple and efficient way to produce large amounts of multiomics data. This data is both accessible and also biologically accurate such that it can be used to test algorithms and tools systematically. Omics Mock Generator works by creating fluxes based on Flux Balance Analysis (FBA) and growth rate maximization, leveraging COBRApy. OMG is compatible with any genome-scale model. In order to obtain proteomics data, it can be assumed that the corresponding protein expression and gene transcription are linearly related to the fluxes, while the amount of metabolite present is assumed to be proportional to the sum of absolute fluxes coming in and out of the metabolite. Learn more about this technology.

DIVA improves the operational efficiency of companies and research institutions working on DNA design and construction, and enables users to collaboratively and visually design DNA constructs and submit the designs to internal resources or outside vendors for construction at scale. Users design DNA with a web-based graphical user interface, submit their designs to a central queue, and after internal or external review, receive their sequence-verified clonal constructs.  Users can track the construction and subsequent sequence validation processes using DIVA.  Completely independent DNA construction tasks can be aggregated into the same multi-well plates and pursued in parallel because the DIVA DNA construction methods are sequence-agnostic and standardized, i.e. they use the same enzymatic master mixes and reaction conditions. Since DIVA captures in a central database all DNA construction success and failure rates, including at which stage a given clonal construction procedure failed, the data can be leveraged to refine and track the efficiency of the DNA assembly design process. Learn more about this technology.

Pseudomonas putida KT2440 engineered to convert glucose and xylose, the primary carbohydrates in lignocellulosic hydrolysates, to muconic acid using a model-guided strategy to maximize the theoretical yield. Learn more about this technology.

The present disclosure relates to a non-naturally occurring microorganism that includes an endogenous genetic deletion that eliminates the expression of at least a pyruvate kinase, where the genetically modified prokaryotic microorganism is capable of producing 3-deoxy-D-arabino-heptulosonate-7-phosphate. Learn more about this technology.

This application provides recombinant Aspergillus fungi having an endogenous cis-aconitic acid decarboxylase (cadA) gene genetically inactivated, which allows aconitic acid production by the recombinant fungi. Such recombinant fungi can further include an exogenous nucleic acid molecule encoding aspartate decarboxylase (panD), an exogenous nucleic acid molecule encoding β-alanine-pyruvate aminotransferase (BAPAT), and an exogenous nucleic acid molecule encoding 3-hydroxypropionate dehydrogenase (HPDH). Kits including these fungi, and methods of using these fungi to produce aconitic acid and 3-hydroxypropionic acid (3-HP) are also provided. Learn more about this technology.

Multi-omics analysis on membrane proteins in both wild type A. pseudoterreus and cad deletion stains led to identification of a specific exporter responsible for aconitic acid high production in the cad deletion platform. Overexpression of this single exporter increased aconitc acid titer by 5 fold, from 10g/L to 50g/L. Learn more about this technology.

Researchers have developed a high throughput enzyme and whole cell biocatalyst screening platform to efficiently navigate through large sequence space, and allows rapid screening of the large library to  increase the chances of capturing rare gain-of-function events that result in improved enzymes and biocatalysts. Learn more about this technology.

The present disclosure relates to a non-naturally occurring microorganism that includes a gene encoding a MucK transporter protein, where the microorganism is capable of catabolizing terephthalic acid (TPA). In some embodiments of the present disclosure, the gene encoding the MucK transporter protein may contain at least one mutation, relative to a reference gene encoding a reference MucK transporter protein. Learn more about this technology.

The present disclosure relates to genetically engineered host cells and methods of producing a lipid-derived compound by employing such host cells. In particular embodiments, the host cell includes a first mutant gene encoding a cytoplasmic tRNA thiolation protein. Optionally, the host cell can include other mutant genes for decreasing fatty alcohol catabolism, decreasing re-importation of secreted fatty alcohol, or displaying other useful characteristics. Learn more about this technology.

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PeakDecoder is a machine learning-based metabolite identification algorithm for multidimensional mass spectrometry measurements incorporating liquid chromatography (LC) and ion mobility spectrometry (IM) separations, and collecting extensive fragmentation spectra with data-independent acquisition (DIA) methods. The algorithm learns to distinguish true co-elution and co-mobility from raw data and calculates metabolite identification error rates. Learn more about this software.

Flux RETAP is a simple and computationally inexpensive method that leverages the prior mechanistic knowledge embedded in genome-scale metabolic models for suggesting targets for genetic overexpression, downregulation or deletion, with the final goal of increasing metabolite production. Learn more about this software.