University of Illinois Urbana-Champaign

Improving constraint-based metabolic models with deep learning

Brasch, Brendan

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https://hdl.handle.net/2142/120576

Description

Title
Improving constraint-based metabolic models with deep learning
Author(s)
Brasch, Brendan
Issue Date
2023-05-02
Director of Research (if dissertation) or Advisor (if thesis)
Jensen, Paul A
Department of Study
Bioengineering
Discipline
Bioengineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
  • deep learning
  • metabolic modeling
  • COBRA modeling
  • constraint-based metabolic modeling
  • genome-scale models
  • neural networks
  • Streptococcus mutans
  • CobraNet
Abstract
Constraint-based metabolic modeling is a powerful tool that allows researchers to map out biological systems and run simulations in order to generate hypotheses related to metabolism. However, these systems are limited by their computational complexity and assumptions made during model creation that hinder predictive accuracy. Neural networks represent a promising approach that can be leveraged in combination with constraint-based models to elucidate further predictions from experimental data. However, researchers often have difficulty extracting features from neural network predictions, leading to difficulties generating and supporting research hypotheses. Furthermore, neural networks are limited by the availability and quality of relevant experimental data. Here, we describe a novel framework, CobraNet, which exploits the strengths of constraint-based models and neural networks in order to generate predictions. We demonstrate how this framework can be leveraged to generate predictions of enzyme activity and biological fitness on an understudied bacterium, Streptococcus mutans. This CobraNet model was built using a constraint-based model for S. mutans and simple metabolic experimental data, demonstrating the broad applicability of this framework. The CobraNet framework can be used throughout the field of biological modeling and would greatly enhance the accessibility of genome-scale models within the bioinformatics community.
Graduation Semester
2023-05
Type of Resource
Thesis
Copyright and License Information
Copyright 2023 Brendan Brasch

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