Challenges of cultivated meat production and applications of genome‐scale metabolic modeling

Suthers, Patrick F.; Maranas, Costas D.

doi:10.1002/aic.16235

Cited by 10 publications

(9 citation statements)

References 82 publications

(94 reference statements)

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“…Over the past three decades, GEMs have become integral for modeling metabolism at a systems-level perspective. GEMs have enabled the study of metabolic rewiring in response to environmental factors and paved the way for metabolic engineering in agricultural, industrial, and medical applications [ 15 , 116 , 117 ]. At the same time, advances in biochemical research have led to increased knowledge of the regulatory intricacies involved in a multitude of cellular processes.…”

Section: Discussionmentioning

confidence: 99%

Next-Generation Genome-Scale Metabolic Modeling through Integration of Regulatory Mechanisms

et al. 2021

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Genome-scale metabolic models (GEMs) are powerful tools for understanding metabolism from a systems-level perspective. However, GEMs in their most basic form fail to account for cellular regulation. A diverse set of mechanisms regulate cellular metabolism, enabling organisms to respond to a wide range of conditions. This limitation of GEMs has prompted the development of new methods to integrate regulatory mechanisms, thereby enhancing the predictive capabilities and broadening the scope of GEMs. Here, we cover integrative models encompassing six types of regulatory mechanisms: transcriptional regulatory networks (TRNs), post-translational modifications (PTMs), epigenetics, protein–protein interactions and protein stability (PPIs/PS), allostery, and signaling networks. We discuss 22 integrative GEM modeling methods and how these have been used to simulate metabolic regulation during normal and pathological conditions. While these advances have been remarkable, there remains a need for comprehensive and widespread integration of regulatory constraints into GEMs. We conclude by discussing challenges in constructing GEMs with regulation and highlight areas that need to be addressed for the successful modeling of metabolic regulation. Next-generation integrative GEMs that incorporate multiple regulatory mechanisms and their crosstalk will be invaluable for discovering cell-type and disease-specific metabolic control mechanisms.

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Section: Discussionmentioning

confidence: 99%

Next-Generation Genome-Scale Metabolic Modeling through Integration of Regulatory Mechanisms

et al. 2021

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“…So, additional constraints such as flux capacity, thermodynamic feasibility, gene expression, etc., are imposed to shrink the solution space [39]. Moreover, MFA can combine with FBA to determine internal metabolic fluxes to increase the prediction power [40]. Besides, other forms of FBA and MFA, such as dynamic FBA and MFA, can be used based on the aim of the research [41,42].…”

Section: An Overview Of Cbm Main Conceptsmentioning

confidence: 99%

Constraint-based modeling and machine learning applications for analysis and optimization of fermentation parameters

Isp

Lewis

et al. 2021

Preprint

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Recent noteworthy advances in the development of high-performing microbial and mammalian strains have enabled the sustainable production of bio-economically valuable substances such as bio-compounds, biofuels, and biopharmaceuticals. However, to obtain an industrially viable mass-production scheme, much time and effort are required. The robust and rational design of fermentation processes requires analysis and optimization of different extracellular conditions and medium components, which have a massive effect on growth and productivity. In this regard, knowledge- and data-driven modeling methods have received much attention. Constraint-based modeling (CBM) is a knowledge-driven mathematical approach that has been widely used in fermentation analysis and optimization due to its capabilities of predicting the cellular phenotype from genotype through high-throughput means. On the other hand, machine learning (ML) is a data-driven statistical method that identifies the data patterns within sophisticated biological systems and processes, where there is inadequate knowledge to represent underlying mechanisms. Furthermore, ML models are becoming a viable complement to constraint-based models in a reciprocal manner when one is used as a pre-step of another. As a result, more predictable models are produced. This review highlights the applications of CBM and ML independently and the combination of these two approaches for analyzing and optimizing fermentation parameters.

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“…Computational methods and systems biology approaches can play key roles in the discovery of suitable drugs. Constraint-based modeling (CBM) has been successfully applied in fundamental research [13] , [14] , [15] , the inference of oncogenes [16] , [17] , [18] , [19] , [20] , [21] , [22] , the discovery of anticancer targets [23] , [24] , [25] , [26] in oncology, microbial engineering [27] , [28] , [29] , and other research fields. CBM uses data- and knowledge-driven constraints to identify feasible metabolic flux distributions for a given condition [ 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

Human/SARS-CoV-2 genome-scale metabolic modeling to discover potential antiviral targets for COVID-19

Wang¹,

Chen²,

Chu³

2022

Journal of the Taiwan Institute of Chemical Engineers

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Challenges of cultivated meat production and applications of genome‐scale metabolic modeling

Abstract: SAVE THE DATE • NOVEMBER 15-20, 2020 | SAN FRANCISCO, CAThis plenary session will focus on ongoing success stories in diversity and inclusion and feature a moderated panel of executives from industry and senior academic administrators.

Cited by 10 publications

References 82 publications

Next-Generation Genome-Scale Metabolic Modeling through Integration of Regulatory Mechanisms

Next-Generation Genome-Scale Metabolic Modeling through Integration of Regulatory Mechanisms

Constraint-based modeling and machine learning applications for analysis and optimization of fermentation parameters

Human/SARS-CoV-2 genome-scale metabolic modeling to discover potential antiviral targets for COVID-19

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