Hybrid Physics-Informed Metabolic Cybergenetics: Modeling Framework for Optogenetically-Controlled Itaconate Production by E. coli

Core Concepts

Optimizing itaconate production in E. coli through a hybrid physics-informed modeling framework with optogenetic control.

Abstract

The content introduces a novel approach to metabolic cybergenetics, focusing on dynamic metabolic engineering strategies and the use of machine-learning surrogates. The study presents an alternative modeling framework for metabolic cybergenetics, simplifying optimization, control, and estimation tasks. By integrating machine-learning surrogates informed by flux balance analysis, the model effectively embeds the physics of metabolic networks into process rates of macro-kinetic models coupled with gene expression. The hybrid modeling approach aims to maintain system states at a minimum level for easier process monitoring and estimation. A computational case study on itaconate production by Escherichia coli is used to demonstrate the effectiveness of the proposed framework. Structure: Introduction to Metabolic Cybergenetics Dynamic Metabolic Engineering Strategies Transcriptional Gene Modulation vs Post-Translational Level Modulation Challenges in Model-Based Optimization for Metabolic Cybergenetics Hybrid Physics-Informed Dynamic Modeling Approach Computational Case Study: Optogenetically-Assisted Itaconate Production by E. coli

Stats

Recent strategies employ constraint-based dynamic models for process optimization. The hybrid modeling approach integrates machine-learning surrogates informed by flux balance analysis. Enzyme-capacity relationships map manipulatable enzyme concentrations to corresponding manipulatable fluxes. Neural-network surrogates are used within macro-kinetic dynamic models for single-level optimizations.

Quotes

"Metabolic cybergenetics interfaces gene expression and cellular metabolism with computers." "Our hybrid physics-informed model simplifies optimization, control, and estimation tasks."

Key Insights Distilled From

Hybrid physics-informed metabolic cybergenetics

by Seba... at arxiv.org 03-26-2024

https://arxiv.org/pdf/2401.00670.pdf

Deeper Inquiries

How can the proposed hybrid modeling approach be applied to other microbial biotechnologies

The proposed hybrid modeling approach can be applied to other microbial biotechnologies by adapting the model structure and parameters to fit the specific metabolic pathways and genetic regulation mechanisms of different organisms. For example, in yeast-based bioprocesses for bioethanol production, manipulatable enzymes involved in ethanol synthesis could be integrated into the dynamic model framework. By identifying key enzymes that influence metabolic fluxes and training neural-network surrogates informed by FBA simulations, similar optimization and control strategies can be implemented to enhance product yields or process efficiencies in yeast systems.

What are potential limitations or drawbacks of using machine-learning surrogates in this context

Potential limitations or drawbacks of using machine-learning surrogates in this context include: Data Quality: The accuracy of the surrogate models heavily relies on the quality and representativeness of the data used for training. Inaccurate or biased data can lead to suboptimal predictions. Interpretability: Machine learning models are often considered as "black boxes," making it challenging to interpret how decisions are made based on complex interactions within the model. Generalization: Surrogate models may struggle with generalizing beyond the range of data they were trained on, leading to inaccuracies when extrapolating to new scenarios. Computational Resources: Training complex neural networks for large-scale metabolic networks can require significant computational resources and time.

How might advancements in optogenetic technology impact future developments in metabolic cybergenetics

Advancements in optogenetic technology have a profound impact on future developments in metabolic cybergenetics: Precision Control: Optogenetics offers precise spatiotemporal control over gene expression, enabling fine-tuning of metabolic pathways at specific times or locations within cells. Orthogonality: Light-inducible gene expression systems provide orthogonal control inputs that do not interfere with endogenous cellular processes, enhancing specificity and minimizing off-target effects. Dynamic Regulation: Optogenetic tools allow real-time modulation of gene expression levels, facilitating dynamic adjustments in response to changing environmental conditions or process requirements. Multi-Gene Regulation: With advancements allowing multiplexed optogenetic systems controlling multiple genes simultaneously, intricate regulatory networks within cells can be engineered for more sophisticated metabolic engineering applications. These advancements open up exciting possibilities for designing advanced metabolic cybergenetic schemes with enhanced controllability and efficiency across various biotechnological applications.