Inscripta, Inc.

Krishna Yerramsetty

Richard Fox

Eric Abbate

Daniel Held

Michael Clay

Katherine Krouse

Nandini Krishnamurthy

Miles Gander

Matthew David Estes

Paul Hardenbol

Thomas Hraha

Isaac David Wagner

Andrea Halweg-Edwards

Shea Lambert

Chad Douglas Sanada

Disclosed systems and methods relate to predicting the relative representation of genomic variants in an edited cell population, based on the editing cassette design representation in an editing cassette design library used to generate the edited cell population. A library of editing cassette designs is generated, and a feature vector, or sequence embedding, is developed for each design using natural language processing techniques. The feature vector may be based upon sequence attributes and editing kinetics of each cassette design as well as attributes that describe the library context. Features may include sequence embeddings generated from a neural network, linguistic-type distances, and statistical distance summaries thereof. The feature vectors are classified using one or more machine learning models, and the classified feature vectors are used to predict the representation of each design an edited cell population.

Methods and systems for modeling of design representation in a library of editing cassettes

The present disclosure relates to various different types of variants incoding and noncoding regions leading to enhanced lysine production for, e.g., supplements and nutraceuticals.

Genome-wide rationally-designed mutations leading to enhanced lysine production in

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Boulder, CO, United States of America

Krishna Yerramsetty