# US 2026/0100240 A1 · Platforms, systems, and methods for genetic generalization expression languages

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- Number: US 2026/0100240 A1
- Document ID: US20260100240A1
- Status: Published application
- Kind: A1
- Application number: 19/415,089
- Filed: 2025-12-10
- Published: 2026-04-09
- Earliest claimed: 2024-06-03
- Applicants: X Development LLC
- Inventors: John Ata Bachman; Laura Barker; Relly Brandman; Federico Vaggi; Nicholas Ruggero; Carl Hans Albach; Chiam Yu Ng; Jeffrey David Orth; Lin Wang
- Web page: https://patentgrep.com/patent/US20260100240A1

## CPC

- G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks
- G16B 40/00: ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
- C12M 29/00: {Means for introduction, extraction or recirculation of materials, e.g. pumps (pumps per se F04B)}
- C12M 41/48: {Automatic or computerized control (automatic analysis G01N35/00)}
- G01N 30/8651: {Recording, data aquisition, archiving and storage}
- G01N 30/8693: {Models, e.g. prediction of retention times, method development and validation}
- G06F 11/3608: {using formal methods, e.g. model checking, abstract interpretation (theorem proving G06N5/013)}
- G06F 11/3692: {for test results analysis}
- G06F 30/27: using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
- G06N 3/044: Recurrent networks, e.g. Hopfield networks
- G06N 3/0442: characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
- G06N 3/045: Combinations of networks
- G06N 3/0455: Auto-encoder networks; Encoder-decoder networks
- G06N 3/0475: Generative networks
- G06N 3/08: Learning methods
- G06N 3/088: Non-supervised learning, e.g. competitive learning
- G06N 3/091: Active learning
- G06N 5/01: Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
- G06N 20/00: Machine learning
- G16B 5/00: ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks
- G16B 20/00: ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
- G16B 20/20: Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
- G16B 20/50: Mutagenesis
- G16B 25/10: Gene or protein expression profiling; Expression-ratio estimation or normalisation
- G16B 30/00: ICT specially adapted for sequence analysis involving nucleotides or amino acids
- G16B 35/20: Screening of libraries
- G16B 40/20: Supervised data analysis
- G16B 40/30: Unsupervised data analysis
- G16C 20/10: Analysis or design of chemical reactions, syntheses or processes
- G16C 20/70: Machine learning, data mining or chemometrics
- G06N 20/10: using kernel methods, e.g. support vector machines [SVM]
- G16B 5/20: Probabilistic models
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G16B: BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- C12M: APPARATUS FOR ENZYMOLOGY OR MICROBIOLOGY; {APPARATUS FOR CULTURING MICROORGANISMS FOR PRODUCING BIOMASS, FOR GROWING CELLS OR FOR OBTAINING FERMENTATION OR METABOLIC PRODUCTS, i.e. BIOREACTORS OR FERMENTERS}
- G01N: INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES (measuring or testing apparatus or processes other than immunoassay, involving enzymes or microorganisms C12M, C12Q)
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G16C: COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE

## Abstract

A system may receive information about a biologic product, wherein the information includes a description of at least a portion of the biologic product in an expression language. The system may generate a set of edits of the biologic product based on the description the at least a portion of the biologic product in the expression language. The system may generate a performance prediction for each edit of the set of edits of the biologic product based on a pre-trained genetic generalization model applied to each edit of the set of edits.

## Claims

### Claim 1

A method for predicting performance associated with genetic edits, the method comprising:
receiving, by a platform, information about a biologic product, wherein the information includes a description of at least a portion of the biologic product in an expression language;
generating, by the platform, a set of edits of the biologic product based on the description the at least a portion of the biologic product in the expression language; and
generating, by the platform, a performance prediction for each edit of the set of edits of the biologic product based on a pre-trained genetic generalization model applied to each edit of the set of edits, wherein the pre-trained genetic generalization model is trained using a two-step process including, pre-training the genetic generalization model using a set of training data, and fine-tuning the genetic generalization model using additional strain-specific data, wherein the set of training data is a larger data set compared to the strain-specific data.

### Claim 2 (depends on 1)

The method of claim 1, wherein the biologic product includes a protein, the expression language includes a protein expression language, and the information includes a description of at least a portion of the protein in the protein expression language.

### Claim 3 (depends on 1)

The method of claim 1, wherein,
the expression language is based on one or more embedding models, the embedding models include at least one of a GenePT model, a Proteinfer model, a pFBA-PCA model, or a GO-PCA model, and
the method further comprising aggregating a set of multi-dimensional vectors generated by the two or more embedding models to create the set of edits.

### Claim 4 (depends on 1)

The method of claim 1, wherein at least one edit of the set of edits includes an expression of the edit in the expression language.

### Claim 5 (depends on 1)

The method of claim 1, wherein the description of the at least a portion of the biologic product includes a description of at least one of:
a structural feature of the at least a portion of the biologic product,
a functional feature of the at least a portion of the biologic product,
a source of the at least a portion of the biologic product,
a metabolic pathway associated with the at least a portion of the biologic product, or
a biologic condition associated with the at least a portion of the biologic product.

### Claim 6 (depends on 1)

The method of claim 1, wherein the description of at least a portion of the biologic product is generated from at least one of:
a description of the at least a portion of the biologic product in at least one natural-language information source, or
a representation of the at least a portion of the biologic product in a knowledge graph.

### Claim 7 (depends on 1)

The method of claim 1, wherein the description of at least a portion of the biologic product is generated by a language machine learning model that has been trained to generate descriptions of at least portions of biologic products in the expression language.

### Claim 8 (depends on 1)

The method of claim 1, wherein generating the set of edits includes generating a description of at least one edit of the set of edits, and the description of the at least one edit includes a description of at least one of:
a structural feature of the at least one edit of the biologic product,
a functional feature of the at least one edit of the biologic product,
a source of the at least one edit of the biologic product,
a metabolic pathway associated with the at least one edit of the biologic product, or
a biologic condition associated with the at least one edit of the biologic product.

### Claim 9 (depends on 8)

The method of claim 8, wherein the description of the at least one edit of the set of edits is generated from at least one of:
a description of the at least a portion of the biologic product in at least one natural-language information source, or
a representation of the at least a portion of the biologic product in a knowledge graph.

### Claim 10 (depends on 8)

The method of claim 8, wherein the description of the at least one edit of the set of edits is generated by a language machine learning model that has been trained to generate descriptions of edits of biologic products.

### Claim 11 (depends on 1)

The method of claim 1, further comprising: generating, by the platform, a representation of the biologic product edited by the set of edits, wherein the representation includes a description in the expression language of at least a portion of the biologic product edited by the set of edits.

### Claim 12 (depends on 1)

The method of claim 1, wherein the pre-trained genetic generalization model comprises a first stage that generates a strain embedding characterizing the strain of a microorganism and a second stage that generates the performance prediction based on the strain embedding.

### Claim 13 (depends on 12)

The method of claim 12, wherein the first stage is one or more of a long-short term memory (LSTM) model, a transformer model, or a convolutional neural network (CNN) model.

### Claim 14 (depends on 1)

The method of claim 1, wherein the performance prediction comprises at least one of a predicted growth rate, a predicted metabolite production rate, a predicted byproduct formation rate, or a predicted protein expression level.

### Claim 15 (depends on 1)

The method of claim 1, further comprising receiving process condition information, wherein the pre-trained genetic generalization model is trained to predict performance with respect to a set of process conditions as indicated by process inputs, and wherein generating the performance prediction for each edit of the set of edits is further based on process inputs corresponding to the process condition information.

### Claim 16 (depends on 1)

The method of claim 1, further comprising updating the pre-trained genetic generalization model using an active learning process including,
generating a set of candidate genetic modifications,
generating a corresponding performance prediction for each of the set of candidate genetic modifications using the pre-trained genetic generalization model,
receiving experimental data associated with at least a portion of the set of candidate genetic modifications,
updating the set of training data using the experimental data, and
re-training the pre-trained genetic generalization model using the updated training data.

### Claim 17 (depends on 16)

The method of claim 16, further comprising determining which portion of the set of genetic modifications to test via experiment based at least in part on an uncertainty quantification generated by the pre-trained genetic generalization model.

### Claim 18 (depends on 1)

The method of claim 1, wherein the pre-trained genetic generalization model is an ensemble of multiple pre-trained genetic generalization models.

### Claim 19

(canceled)

### Claim 20

(canceled)