Systems Metabolic Engineering Strategies: Integrating Systems and Synthetic Biology with Metabolic Engineering

Choi, Kyeong Rok; Jang, Woo Dae; Yang, Dongsoo; Cho, Jae Sung; Park, Dahyeon; Lee, Sang Yup

doi:10.1016/j.tibtech.2019.01.003

Cited by 399 publications

(238 citation statements)

References 194 publications

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“…Our analysis predicts regulatory mechanisms that will likely orchestrate flux adjustments based on reaction attributes. This can guide drug discovery and metabolic engineering efforts by identifying regulators that are dominant in different parts of the network [44]. CAROM can be applied to uncover target specificities of other regulators such as non-coding RNAs and PTMs, and help understand the architecture of metabolic regulation in a wide range of organisms.…”

Section: Resultsmentioning

confidence: 99%

Metabolic signatures of regulation by phosphorylation and acetylation

Smith

Shen

Lee

et al. 2019

Preprint

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8The metabolism of most organisms is controlled by a diverse cast of regulatory processes, including 9transcriptional regulation and post-translational modifications (PTMs). Yet how metabolic control is 10 distributed between these regulatory processes is unknown. Here we present Comparative Analysis of 11Regulators of Metabolism (CAROM), an approach that compares regulators based on network 12 connectivity, flux, and essentiality of their reaction targets. Using CAROM, we analyze transcriptome, 13proteome, acetylome and phospho-proteome dynamics during transition to stationary phase in E. coli 14 and S. cerevisiae. CAROM uncovered that the targets of each regulatory process shared unique 15 metabolic properties: growth-limiting reactions were regulated by acetylation, while isozymes and futile-16 cycles were preferentially regulated by phosphorylation. Reversibility, essentiality, and molecular-17weight further distinguished reactions controlled through diverse mechanisms. While every enzyme can 18 be potentially regulated by multiple mechanisms, analysis of context-specific datasets reveals a 19 conserved partitioning of metabolic regulation based on reaction attributes. 20Author summary 21There are several ways to regulate an enzyme's activity in a cell. Yet, the design principles that 22determine when an enzyme is regulated by transcription, translation or post-translational modifications 23 are unknown. Each control mechanism, such as transcription, comprises several regulators that control 24 a distinct set of targets. So far, it is unclear if similar partitioning of targets occurs at a higher level, 25between different control mechanisms. Here we systematically analyze patterns of metabolic regulation 26 in model microbes. We find that five key parameters can distinguish the targets of each mechanism. 27These key parameters provide insights on specific roles played by each mechanism in determining 28 overall metabolic activity. This approach may help define the basic regulatory architecture of metabolic 29 networks. 30

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Section: Resultsmentioning

confidence: 99%

Metabolic signatures of regulation by phosphorylation and acetylation

Smith

Shen

Lee

et al. 2019

Preprint

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“…Recently, systems metabolic engineering was emphasized as an integral part in the development and optimization of microbial cell factories (Wittmann and Lee, 2012;Maia et al, 2016;Saa et al, 2019;Choi et al, 2019;Sarkar and Maranas, 2019). Thus, we want to put the PAM forth to highlight the advantages of considering the allocation of the total condition-dependent proteome for constraint-based modeling techniques in favor of more accurate mutant strain predictions.…”

Section: Discussionmentioning

confidence: 99%

Protein allocation and enzymatic constraints explainEscherichia coliwildtype and mutant phenotypes

Alter

Blank

Ebert

2020

Preprint

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Proteins have generally been recognized to constitute the key cellular component in shaping microbial phenotypes. Due to limited cellular resources and space, optimal allocation of proteins is crucial for microbes to facilitate maximum proliferation rates while allowing a flexible response to environmental changes. Regulatory patterns of protein allocation were utilized to account for the condition-dependent proteome in a genome-scale metabolic reconstruction of Escherichia coli by linearly linking mass concentrations of protein sectors and single metabolic enzymes to flux variables. The resulting protein allocation model (PAM) correctly approximates wildtype phenotypes and flux distributions for various substrates, even under data scarcity. Moreover, we showed the ability of the PAM to predict metabolic responses of single gene deletion mutants by additionally assuming growth-limiting, transcriptional restrictions. Thus, we promote the integration of protein allocation constraints into classical constraint-based models to foster their predictive capabilities and application for strain analysis and metabolic engineering purposes. K E Y W O R D S constraint-based modeling, enzyme kinetics, protein allocation, transcriptional control, Escherichia coli 1

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“…Pharmaceutical companies are already investing substantially in computational approaches to guide drug discovery and cell design. While these important applications are out of the scope of this paper, we refer the interested readers to detailed reviews on the usage of metabolic engineering and synthetic biology in industrial context [5][6][7][8] . However, model-based methods can be used for much more than this.…”

Section: Introductionmentioning

confidence: 99%

Towards a widespread adoption of metabolic modeling tools in biopharmaceutical industry: a process systems biology engineering perspective

et al. 2020

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In biotechnology, the emergence of high-throughput technologies challenges the interpretation of large datasets. One way to identify meaningful outcomes impacting process and product attributes from large datasets is using systems biology tools such as metabolic models. However, these tools are still not fully exploited for this purpose in industrial context due to gaps in our knowledge and technical limitations. In this paper, key aspects restraining the routine implementation of these tools are highlighted in three research fields: monitoring, network science and hybrid modeling. Advances in these fields could expand the current state of systems biology applications in biopharmaceutical industry to address existing challenges in bioprocess development and improvement.npj Systems Biology and Applications (2020) 6:6 ; https://doi.

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Systems Metabolic Engineering Strategies: Integrating Systems and Synthetic Biology with Metabolic Engineering

Cited by 399 publications

References 194 publications

Metabolic signatures of regulation by phosphorylation and acetylation

Metabolic signatures of regulation by phosphorylation and acetylation

Protein allocation and enzymatic constraints explainEscherichia coliwildtype and mutant phenotypes

Towards a widespread adoption of metabolic modeling tools in biopharmaceutical industry: a process systems biology engineering perspective

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