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Title: Simulating the metabolic pathway dynamics of an organism

Abstract

Disclosed herein are systems and methods for determining metabolic pathway dynamics using time series multiomics data. In one example, after receiving time series multiomics data comprising time-series metabolomics data associated a metabolic pathway and time-series proteomics data associated with the metabolic pathway, derivatives of the time series multiomics data can be determined. A machine learning model, representing a metabolic pathway dynamics model, can be trained using the time series multiomics data and the derivatives of the time series multiomics data, wherein the metabolic pathway dynamics model relates the time-series metabolomics data and time-series proteomics data to the derivatives of the time series multiomics data. The method can include simulating a virtual strain of the organism using the metabolic pathway dynamics model.

Inventors:
;
Issue Date:
Research Org.:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1998332
Patent Number(s):
11636917
Application Number:
16/022,113
Assignee:
The Regents of the University of California (Oakland, CA)
DOE Contract Number:  
AC02-05CH11231
Resource Type:
Patent
Resource Relation:
Patent File Date: 06/28/2018
Country of Publication:
United States
Language:
English

Citation Formats

Costello, Zachary, and Martin, Hector Garcia. Simulating the metabolic pathway dynamics of an organism. United States: N. p., 2023. Web.
Costello, Zachary, & Martin, Hector Garcia. Simulating the metabolic pathway dynamics of an organism. United States.
Costello, Zachary, and Martin, Hector Garcia. Tue . "Simulating the metabolic pathway dynamics of an organism". United States. https://www.osti.gov/servlets/purl/1998332.
@article{osti_1998332,
title = {Simulating the metabolic pathway dynamics of an organism},
author = {Costello, Zachary and Martin, Hector Garcia},
abstractNote = {Disclosed herein are systems and methods for determining metabolic pathway dynamics using time series multiomics data. In one example, after receiving time series multiomics data comprising time-series metabolomics data associated a metabolic pathway and time-series proteomics data associated with the metabolic pathway, derivatives of the time series multiomics data can be determined. A machine learning model, representing a metabolic pathway dynamics model, can be trained using the time series multiomics data and the derivatives of the time series multiomics data, wherein the metabolic pathway dynamics model relates the time-series metabolomics data and time-series proteomics data to the derivatives of the time series multiomics data. The method can include simulating a virtual strain of the organism using the metabolic pathway dynamics model.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2023},
month = {4}
}

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