Building kinetic models for metabolic engineering

Foster, Charles; Wang, Lin; Dinh, Hoang V.; Suthers, Patrick F.; Maranas, Costas D.

doi:10.1016/j.copbio.2020.11.010

Cited by 44 publications

(31 citation statements)

References 58 publications

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“…Noteworthy is that the current GEM‐based analyses did not include any regulatory information. To address this limitation, kinetic models that quantitatively describe the metabolic and regulatory processes through kinetic formalisms (e.g., mass action, Michaelis–Menten; Foster et al, 2021). Few examples of such models exist among nonconventional yeast.…”

Section: Systems Metabolic Engineering Of Y Lipolyticamentioning

confidence: 99%

Systems‐level approaches for understanding and engineering of the oleaginous cell factory Yarrowia lipolytica

Poorinmohammad

Kerkhoven

2021

Biotech & Bioengineering

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Concerns about climate change and the search for renewable energy sources together with the goal of attaining sustainable product manufacturing have boosted the use of microbial platforms to produce fuels and high‐value chemicals. In this regard, Yarrowia lipolytica has been known as a promising yeast with potentials in diverse array of biotechnological applications such as being a host for different oleochemicals, organic acid, and recombinant protein production. Having a rapidly increasing number of molecular and genetic tools available, Y. lipolytica has been well studied amongst oleaginous yeasts and metabolic engineering has been used to explore its potentials. More recently, with the advancement in systems biotechnology and the implementation of mathematical modeling and high throughput omics data‐driven approaches, in‐depth understanding of cellular mechanisms of cell factories have been made possible resulting in enhanced rational strain design. In case of Y. lipolytica, these systems‐level studies and the related cutting‐edge technologies have recently been initiated which is expected to result in enabling the biotechnology sector to rationally engineer Y. lipolytica‐based cell factories with favorable production metrics. In this regard, here, we highlight the current status of systems metabolic engineering research and assess the potential of this yeast for future cell factory design development.

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Section: Systems Metabolic Engineering Of Y Lipolyticamentioning

confidence: 99%

Systems‐level approaches for understanding and engineering of the oleaginous cell factory Yarrowia lipolytica

Poorinmohammad

Kerkhoven

2021

Biotech & Bioengineering

View full text Add to dashboard Cite

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“…Noteworthy is that the current GEM-based analyses did not include any regulatory information. To address this limitation, kinetic models that quantitatively describe the metabolic and regulatory processes through kinetic formalisms (e.g., mass action, Michaelis-Menten; Foster et al, 2021). Few examples of such models exist among nonconventional yeast.…”

Section: Lipids and Fatty Acid-derived Productsmentioning

confidence: 99%

Systems-level approaches for understanding and engineering of the oleaginous cell factory Yarrowia lipolytica

Poorinmohammad

Kerkhoven

2020

Preprint

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Concerns about climate change and the search for renewable energy sources together with the goal of attaining sustainable product manufacturing have boosted the use of microbial platforms to produce fuels and high-value chemicals. In this regard, Yarrowia lipolytica has been known as a promising yeast with potentials in diverse array of biotechnological applications such as being a host for different oleochemicals, organic acid, and recombinant protein production. Having a rapidly increasing number of molecular and genetic tools available, Y. lipolytica has been well studied amongst oleaginous yeasts and metabolic engineering has been used to explore its potentials. More recently, with the advancement in systems biotechnology and the implementation of mathematical modeling and high throughput omics data-driven approaches, in-depth understanding of cellular mechanisms of cell factories have been made possible resulting in enhanced rational strain design. In case of Y. lipolytica, these systems-level studies and the related cutting-edge technologies have recently been initiated which is expected to result in enabling the biotechnology sector to rationally engineer Y. lipolytica-based cell factories with favorable production metrics. In this regard, here, we highlight the current status of systems metabolic engineering research and assess the potential of this yeast for future cell factory design development.

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“…However, the difficulty of acquiring the knowledge of (i) the exact mechanism of each reaction and (ii) the parameters of the said mechanisms hampers the building of kinetic models. In most of the current kinetic modeling methods [9][10][11][12] , the unknown reaction mechanisms are hypothesized or modeled by approximate reaction mechanisms 13,14 . The main challenge in obtaining the unknown parameters is qualitative and quantitative uncertainties intrinsic to biological systems.…”

Section: Introductionmentioning

confidence: 99%

Reconstructing Kinetic Models for Dynamical Studies of Metabolism using Generative Adversarial Networks

Choudhury

Moret

Salvy

et al. 2022

Preprint

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Kinetic models of metabolic networks relate metabolic fluxes, metabolite concentrations, and enzyme levels through well-defined mechanistic relations rendering them an essential tool for systems biology studies aiming to capture and understand the behavior of living organisms. However, due to the lack of information about the kinetic properties of enzymes and the uncertainties associated with available experimental data, traditional kinetic modeling approaches often yield only a few or no kinetic models with desirable dynamical properties making the computational analysis unreliable and computationally inefficient. We present REKINDLE (REconstruction of KINetic models using Deep LEarning), a deep-learning-based framework for efficiently generating large-scale kinetic models with dynamic properties matching the ones observed in living organisms. We showcase REKINDLE's efficiency and capabilities through three studies where we: (i) generate large populations of kinetic models that allow reliable in silico testing of hypotheses and systems biology designs, (ii) navigate the phenotypic space by leveraging the transfer learning capability of generative adversarial networks, demonstrating that the generators trained for one physiology can be fine-tuned for another physiology using a low amount of data, and (iii) expand upon existing datasets, making them amenable to thorough computational biology and data-science analyses. The results show that data-driven neural networks assimilate implicit kinetic knowledge and structure of metabolic networks and generate novel kinetic models with tailored properties and statistical diversity. We anticipate that our framework will advance our understanding of metabolism and accelerate future research in health, biotechnology, and systems and synthetic biology. REKINDLE is available as an open-access tool.

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Building kinetic models for metabolic engineering

Cited by 44 publications

References 58 publications

Systems‐level approaches for understanding and engineering of the oleaginous cell factory Yarrowia lipolytica

Systems‐level approaches for understanding and engineering of the oleaginous cell factory Yarrowia lipolytica

Systems-level approaches for understanding and engineering of the oleaginous cell factory Yarrowia lipolytica

Reconstructing Kinetic Models for Dynamical Studies of Metabolism using Generative Adversarial Networks

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